Category: Martech

In depth information on martech (marketing technology) including blogs, infographics, and a mini-cast series of instructional videos

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To CDP or NOT – 3 tips – then you decide

A Customer Data Platform – or a CDP – is a software category first defined in 2013 by Raab Associates (headed by longtime marketing technology analyst, David Rabb).  Shortly thereafter, Rabb Associates created the CDP Institute as a CDP promotional and education vehicle (and today actively manages it).

CDP

If you’re confused by CDPs and whether you need one, you’re not alone.  The category includes a mixed bag of companies in a wide variety of shapes, sizes, and abilities.  As of mid-November 2019, the CDP Institute listed 94 in its directory[i].  In the rest of this article, you’ll get the condensed history shedding light on the origins of CDPs, useful forensics on the category, and 3 tips to help you decide which (if any) to consider in your Martech or CX stack.

Wait, what’s a CDP?

Let’s start with the definition of a CDP from the CDP Institute:

“A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.” [ii]

And the CDP Institute follows this with a first-level unpacking of the definition:

“Packaged software”: the CDP is a prebuilt system that is configured to meet the needs of each client. 

“Creates a persistent, unified customer database”: the CDP creates a comprehensive view of each customer by capturing data from multiple systems, linking information related to the same customer, and storing the information to track behavior over time.

“Accessible to other systems”: data stored in the CDP can be used by other systems for analysis and to manage customer interactions.

On the Origin of CDPs

In my first Martech job with UPS in 1993 we signed a contract with Harte-Hanks for their “Marketing Customer Information File,” or MCIF.  Interestingly it was:

  • Packaged software: Harte-Hanks sold it first to banks, and then to companies like UPS.
  • A persistent and unified customer database: It included household and customer ids.
  • Accessible to other systems: It had export and import capabilities.

Apparently, we bought a customer data platform at the time and didn’t even know it!  And here’s the thing: Harte-Hanks’ customer database wasn’t even relational (for the techno nostalgists out there, it used a hierarchical file system).  And they shipped us updates once per month, on tapes!  The point is this 1993 software (according to the CDP Institute’s definition) technically qualified as a CDP, which isn’t very reassuring if you are looking for criteria to judge a vendor’s worthiness to serve your current-day customer data needs.  

By the late ’90s, relational databases (with SQL interfaces) had taken over and using this technology, campaign management vendors and marketing consultancies spawned the 2nd generation of mostly hand-crafted customer data platforms known as marketing data marts (many of the original on-premise campaign management systems tapped into these).  Practitioners unsatisfied with what they got from their IT partners, built these using database solutions like Oracle, IBM DB2, Microsoft SQLServer, and Teradata.  Meanwhile, IT continued building out data warehouses. 

Then, IT moved into the era of big data (or NO SQL) solutions and building data lakes.  From warehouses to lakes, to oceans, the mentality was “store it and they will come.”  But (for the most part) come they did not because IT hadn’t designed for specific business outcomes. 

Nonetheless, IT had accumulated vast data reserves, and as a result, reporting firms such as Cognos, MicroStrategy, and Business Objects tapped in with general-purpose reporting and decision-support software.  These evolved into the wide-class of business intelligence tools available today, including tools like Tableau. 

With the advent of websites and web banner ads during the .com boom, programmatic bidding platforms needed a database to store audiences (from all those cookies and device ids) and overlayed 3rd party data.  These became known as cookie pools or DMPs (Data Management Platforms).  Further, on owned websites, brands started tagging and tracking visitor behavior to understand traffic patterns and in the hope of eventually providing site personalization.  This spawned tag management and website personalization firms.  And there you have it: the genealogy of CDPs (Figure 1).

CDP Family Tree

Figure 1: CDP Family Tree

The CDP Convergence Era

By 2013, a host of factors were affecting these technologies and the data landscape:

  • The cloud movement caused Campaign Management and Email Service Providers to form Marketing Clouds.
  • Website personalization expanded to all digital channels.
  • Tag management/web analytics became commoditized (thanks in part to Google Analytics being free).
  • The DMP market slowed because cookie pools were never people-based (making it impossible to do 1:1 personalization).
  • The CX revolution forced marketing, sales, and service practitioners to think more broadly about customer data and customer journeys.
  • Big data management suppliers were looking for valuable use cases.
  • Marketing data marts, for the most part, had been absorbed by IT’s 360 data initiatives.
  • Marketers faced growing issues with the variety, volume, and increasing complexity of data.

With these dynamics at play, and to their credit, the CDP Institute attracted packaged-software businesses from various origins that qualified for inclusion in its directory.  Many found this new label attractive as their existing markets softened, got saturated, or commoditized.  In the ensuing five years, a handful of CDPs grew to nearly a hundred.

Late last year, Forrester’s Joe Stanhope and Stephanie Liu wrote an article entitled, “For B2C Marketers, Customer Data Platforms Overpromise and Underdeliver.”  The blog promoting it summed up their view: “Marketers, these aren’t the droids data platforms you’re looking for.” [iii]  This is especially true for enterprise B2C marketers. 

To further understand the complexion of the organizations that collected under the CDP umbrella, I separated them into sub-categories in terms of lineage.  I analyzed the 94 companies in the CDP Institute’s directory as of November 2019 and found this distribution:

Origins Companies Avg # Employees # Bought
Native CDP 36 71 7
Marketing Automation (e.g., Email Provider, Campaign Management) 15 112 1
Data Management (e.g., Data Quality, ETL, Data Management) 12 249 * 4
Customer Analytics (e.g, Business Intelligence, Customer 360) 9 47 3
Recommendation Engine (Product, Content, Real-time Recommendations) 8 145 0
Tag Management / Web Analytics 5 152 1
Madtech / Attribution / Journey Orchestration 5 96 0
Lead Management 4 68 1

*Removed Informatica (4700 employees) from calc to avoid over skewing

First, the 36 native CDPs:

  • Most were born recently and didn’t spring from an earlier category. 
  • They average 71 employees; many are startups; consistent with a nascent category.
  • Although some claim to have cross-industry experience most have a weight of experience in one sector.
  • Three notable examples are Lytics (CPG), mParticle (Media & Entertainment), and SessionM (Restaurants)
  • For many, their customer base is mid-market and/or B2B slanted and not enterprise.
  • In the last 5 years, the native CDPs have become acquisition targets (bigger companies bought 7 of the 36 between 2015 and 2019).

As we saw, to qualify for inclusion in the CDP Institute’s directory, the solution must prove it persists data, yet that doesn’t mean the vendor has useful business experience with that data.  And interestingly, 25 of the 94 didn’t originate as Martech vendors, but instead as general-purpose (or even sales force management) data firms.  In other words: buyer beware in terms of experience with enterprise-scale, marketing use cases, and B2C data.

Some of the critical capabilities beyond the basic CDP Institute criteria to keep an eye on are:

  • Data scrubbing – hygiene on bad or missing data
  • Data appending – attaching net-new data attributes to an existing profile
  • Data aggregation – summarizations, calculations, and pattern detection to create predictive fields for high-value use cases such as propensity to buy or churn
  • Data streaming – continuous feeding of data as it’s created
  • Identity resolution – device matching, stitching, and rationalization to pinpoint the person
  • Data visibility and privacy – compliance, security, and preference management features
  • Ecosystem connectors – Pre-built interfaces to streamline interchange with other platforms

As you wade through all the Institute’s vendors, as well as the brand-new (and untested) CDP offerings by the mega Martech vendors (Adobe’s CDP, Salesforce’s Customer 360 Truth, SAP CX Suite, Oracle’s CX Unify, and Teradata’s Vantage CX), and any others happily slapping on the CDP label, carefully inspect the above critical capabilities.  And when doing so, consider these tips as you decide whether to license a CDP.

Tip #1: Feeds and Speeds Matter

As you ponder data accessibility, think about the speed of access required for real-time customer engagement.  In my June article “The Final 4: Martech Platforms and Ecosystems,” I opined that one of the four linchpin platforms for effective real-time engagement should be a Customer Insights Platform (CIP), going on to compare and contrast it with a CDP.  The spoiler alert is that a CIP is NOT the same, and very few CDPs qualify as a CIP.

A CIP’s primary job is to feed the right individual-level data at the right time (often in real-time) to the Acquisition and Relationship Execution platforms (details in the above article).  Some of the best data to predict current intent comes from recent digital interactions.  A CIP, which is a transactional platform, can’t also be a business intelligence platform.  CIPs, designed to transact in real-time, access a customer profile (and the sub-strata of that data for an individual) in milliseconds not batching across them in minutes or hours.  Consequently, ask yourself, “Do I need a tool for business intelligence or real-time 1:1 execution action?”  If you care about real-time feeds and speeds, and the outcomes you’ll get with a well-architected execution platform, you want a CIP to feed it, and many of the CDPs won’t work.

Another crucial consideration is the latency and scalability when streaming digital channel behavior data in real-time.  Notice in Figure 2 that data must flow in real-time (not batch) into the customer profile managed by the Relationship Execution Platform.  Other slowly changing data, such as core customer records and product holdings, can enter periodically, and you might use a CDP as that data source.

Customer Insights Platform

Figure 2: Customer Insights Platform – Example data processing

Here are the CDPs from the directory with origins in Tag Management (and examples of their enterprise-grade experience)

  • Celebrus – Achmea, BOA, HSBC
  • Commanders Act – Credit Mutuel, Engie, Nestle
  • Ensighten – OI, TUI, United
  • Tealium – Cox, HSBC, Vodafone

Of the CDPs, the Tag Management vendors are best suited to capture and stream real-time digital data (handling volumes such as 5,000 transactions per second), but keep in mind some require more involved multi-page tagging to get the right behavior indicators. 

Tip #2: Inventory moments of truth – focus on the data needed to detect them

Data, like oil, is useless when trapped in the ground or in crude form.  Value comes from tapping into it, refining it, distilling it into a refined energy product, and dealing responsibly with its combustion and aftermath.  Your job is to find detailed insights that fuel a productive understanding of customers’ behaviors, demands, and intent.  Relevance happens when you react swiftly and with grace, delivering personalized offers, services, and recommendations.  So, the question is, can CDPs help you with this challenge?

That depends.  In “Deconstructing Customer Data Platforms – Myth vs. Reality,” [iv] the Winterberry Group concurs and cautions that “Different CDPs have different levels of expertise at managing different levels of data capture.”

When customers use websites, mobile apps, and other digital devices, they emit signals showing interest in products, completing tasks, subscribing to things, getting alerted, and interacting with their environment.  If brands effectively tap into these signals and react with extraordinary timing and class, they can achieve a competitive advantage.  But these moments are fleeting.

For instance, a customer searching on a site with the term “early termination fee” could be a clear sign churn is coming in minutes.  A customer dwelling on a mortgage page for the second time in a day might be making a final decision right then on who gets their home loan.  Subscribing to a 401k newsletter may be the first in many retirement interactions.  Customers’ proximity to your store (or a competitor’s) might hint shopping is imminent.

So, make a list of these events, tap into them, store patterns of data and flags about them, and devise a way to act on them.

Tip #3: Don’t confuse CDPs with more conversation and more action

Better customer engagement and conversations don’t necessarily require more master data management.  But don’t get me wrong.  If you don’t have well-organized customer data, then a CDP’s data collection, identity resolution, and unification capabilities could prove useful to drive the right engagement.  Yet if you are a large enterprise, chances are you have scores of ongoing data unification efforts, and what you probably need is rationalization and coordination, not another data repository.

In terms of orchestrating personalized customer conversations, several CDPs originated in real-time interaction management, or in the website, product, or content recommendation space:

Vendor Origins Major experience
Blueshift Content Recommendations eLearning & Media
Boxever Real-time Interaction Management Travel & Leisure
Evergage Website Personalization Retail & Tech
Jahia Content Recommendations  
Manthan Product Recommendations (BI vendor that bought Rich Relevance) Retail & CPG
NectarOm Content Recommendations  
SmarterHQ Real-time Interaction Management Retail
SymphonyRM Real-time Interaction Management Healthcare

Some are good recommendation engines, and have specific areas of experience, but remember it’s not a recommendation engine you are after in the CDP area.  It’s outcome-oriented customer data management.  So, don’t get distracted by recommendation capabilities when what you seek is the ability to handle data feeds and speeds, find insights, and activate an execution platform to deliver at moments of truth. 

If you don’t have an adequate relationship execution platform, evaluate those separately.  In that process, look at the strongest real-time interaction management (RTIM) platforms that major in serving recommendations on paid and owned properties.

Conclusion

Like the first minute of a roller coaster ride, the CDP train is dragging us up to the hype precipice and what’s in store when it plummets down to the trough of disillusionment is unknown.  No doubt, it will be fast, furious, and freighting, especially for those heavily invested in this technology.  Because the CDP category is a mixed bag, very different firms will shop in the bin, some making head-scratcher acquisitions.  Some CDPs will go out of business.  Quite possibly, the plunge has begun, with Mastercard’s recent purchase of SessionM, D&B’s buy of Lattice, and ARM’s purchase of Treasure Data.

Focus on the use cases (and data needed) that improves your ability to serve timely, relevant, and personalized offers and services. Codify the important data and the speed it must move into your decision-making solution.  If a CDP has components that help you, and you get those at a fair price, consider plugging them in.  But remember, you’ll inherit redundant features, so be wary of the premium you’ll pay for those and ensure you can either use them or work around them.  Further, assess how difficult it will be to pull them out should your plans change.

And if after all this you’re still confused, consider sitting on the sidelines until the dust settles, using your existing data-management technology, and watch others take the wild ride. 

[i] CDP Institute, https://www.cdpinstitute.org/directory, 2019

[ii] CDP Institute, https://www.cdpinstitute.org/cdp-basics, 2019

[iii] Forrester, https://go.forrester.com/blogs/b2c-marketers-and-cdps/,2019

[iv] Winterberry Group, https://www.winterberrygroup.com/our-insights/deconstructing-customer-data-platforms-myths-vs-realities, 2019

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Will AI in digital marketing lead to marketer obsolescence?

AI in Digital Marketing

I just returned from attending several spring digital marketing conferences – Adobe Summit and Martech West.  In both the art of the possible was on full display and got me thinking about whether fully-automated AI-driven digital marketing could ever be a thing, and what realistic automation goals look like.   

Spurred on by conference highs and three years of ramped-up writing on AI in digital marketing, it seemed the right time to step back and ask a few questions:

  • Is fully automated digital marketing even possible? 
  • Will marketers wake up one day and find themselves obsolete? 
  • What should marketers be doing today to prepare for tomorrow?

Is fully automated digital marketing even possible? 

Digital marketing continues to be on the leading edge of AI advances and high-tech innovations.  Surveys repeatedly indicate AI professionals aim their efforts at infusing intelligence into digital marketing.  In fact, for nearly 10 years running, Rexer Analytics’ Data Science survey lists these digital marketing pursuits in the top 10 analytics goals of data scientists [i]:

  • Improving understanding of customers
  • Retaining customers
  • Improving customer experiences
  • Selling products/services to existing customers
  • Market research
  • Acquiring customers
  • Improving direct marketing programs

And every day we’re inundated with news of more advances in marketing automation such as:

  • Customer behavior data that is automatically processed, like whether someone used a mobile app in the last 30 days
  • Lights-out, always-on marketing programs that are triggered automatically and run themselves, often fueled by the pre-computed customer behavior data
  • Advances in process automation and examples of programmatic marketing jobs that previously involved human intervention, such as wave/drip campaigns and paid media buys
  • Helper tools out the wazoo, like website and mobile app builders, grammar checkers, content taggers, SEO using AI [ii], and many more

Couple all that with a host of technological factors spurring on AI and automation like:

  • Digital devices, tags, and pixels gathering data at breakneck speeds
  • Falling data processing and cloud storage costs
  • Free open source tools and freemium pricing models that put more software in the hands of more marketing users
  • Adaptive algorithms that learn which offers customers respond best to without supervision or manual testing 
  • Natural language generation handling some previously thought to be untouchable human tasks, such as automated writing (although today’s NLG performs just simple writing tasks)

From all this, some might conclude the end of human-powered marketing is close at hand.  But others, unconvinced by these tenuous signs, might simply respond, “Poppycock!”

With years of history to draw on, during which digital marketing was born and came of age, perhaps the real answer lies somewhere in between.  In pouring over this progress and assessing the full landscape of today’s automated processing and AI in digital marketing, it’s clear humans have played and will continue to play a crucial role. 

In the last 25 years, we’ve seen human ingenuity cause marketing to go digital, get more scientific, and its content to get richer, more compelling, and hyper-personalized.  So, without a doubt, AI in digital marketing is a real thing.  It has improved reach and targeting, quickened the pace of experimentation, automated direct impression and response tracking, and compressed marketing program cycles.

To accomplish all that, marketers have employed a plethora of applications, from SEO tools to email tools to campaign management systems to creative suites to mobile messaging platforms.  And make no mistake – automation has paid dividends and enabled more personalized marketing at scale.  Companies that use marketing automation in the development and execution of their tactics have improved conversion rates, grown revenues, and become more efficient.  In my career, I’ve seen piles of firms lift response and conversion rates up to 3x, and in some cases as high as 12x, doing so without exponentially increasing team sizes.   And all along, technology-minded humans have remained instrumental components of both developing and executing those systems.

Will marketers wake up one day and find themselves obsolete? 

Ironically, during this period of hyper-automation, the number of digital marketing jobs has grown, not shrunk.  And the outlook is rosy, not bleak.  In fact, the US Bureau of Labor statistics projects another 24k jobs will be added by 2026 in the US alone [iii].  In 1995, there was no such thing as an SEO analyst, a social media marketer, or a mobile marketing manager.  Though some old jobs have disappeared, and some are at risk, there’s no indication that people gainfully employed in digital marketing today have anything to worry about.

If you’re a grizzled old marketing vet like me, you’ve already seen automation encroach on various tasks.  For instance, writing email subject lines, deciding the best time to send that email, or even deciding how much to bid for digital ads – can all be automated.  Yet simultaneously you’ve watched as new roles have emerged.  If you’re a little younger, you’ll see even more amazing things in the future.

As certain aspects of marketing production become machine-executed, such as when to trigger a re-marketing treatment, new marketing opportunities emerge that require human intellect, such as determining if augmented reality provides marketing lift.

It’s a given AI will improve to where more complex tasks – and whole jobs – can be performed by machines, such as completely replacing telemarketers as suggested in this landmark Future of Employment study[iv].  This phenomenon is not new.  It’s known as collective learning and has been affecting human advances since people invented language, writing, and tools. It’s one of the main reasons for societal and technological evolution as it fosters sharing and accumulating knowledge by allowing us to pass down knowledge and build on it via spoken and written communications.  And it’s what’s enabled us to refine and evolve our machines.

But collective learning is not just about machines advancing.  It’s the combination of humans evolving together – essentially becoming one system.   As such, it’s not rational to assume AI in digital marketing will cause all human jobs to succumb to computerization.  Expect instead machines to take over simpler roles just as they’ve taken over much of our manual labor in farming. They’ll ride shotgun with us, learning from us, correcting us, and mastering our simple and repetitive tasks – while we move on to create, invent, and fine-tune new, more complex ones.  And along the way, we’ll be assisted by machines as we invent those new brand-new roles.

In my recent article on AI and technology trends that are affecting marketing (5 predictions for CRM’s AI applications in 2019), I discuss the trend toward effective combinations of crowds of human and machine intelligence.  Organizations that build open systems that take advantage of this power and embrace it will outperform those that don’t.

To better understand why digital marketing won’t be fully autonomous anytime soon, it’s helpful to inspect the full journey of a marketing initiative, from idea inception to program execution.  Marketers work in two main factories: 

  1. Creative factories where ideas are born, iterated, refined, vetted, simulated, and approved for use
  2. Operational factories where select ideas are executed, monitored, tested, tracked, and retired

Creative Marketing Laboratories

Narrow (single purpose) AI systems have enjoyed some interesting achievements recently, such as painting masterpieces and writing books (this one, a riveting read on lithium-ion batteries).  In marketing, AI has written image captions, tagged content, and even helped with content generation.   But it’s far – way far – from putting all these pieces together and being the de-facto creator of new ideas.  Humans own this space and will for some time. Responsibilities in jobs such as program strategists, copywriters, SEO experts, product marketers, and graphical/interactive designers have progressively increased.  Consequently, these people do more and do it faster with automation’s assistance.

Operational Marketing Factories

Operation managers are inherently driven to streamline, rationalize, and automate processes.  And they use industrial engineering and machines to do it.  But just as an old-model car has limits on how much it can be modified, production marketing systems have the same inherent limitations.  And there is no evidence that the cloud, low code, or agile is changing that.  After about ten years, old systems need to be replaced or they reach a point of diminishing returns. 

And radical change involves teams of people organized in large efforts to transition to new technology and redesigned processes.  No machine accomplishes this without humans.  Enterprises still employ hundreds if not thousands of humans to design and test their new marketing engines.  Once in production, people feed the machines, monitor them, and fix them when they break.  There’s plenty of work to go around.  Expect digitization and human-machine process reengineering to accelerate, possibly compressing the marketing system replacement cycle from ten years to five years.

As this happens, expect marketing tasks like this to become more automated:

  • Customer data processing and summarization
  • Some feature engineering for model data prep
  • Pattern detection and event-based triggers automatically kicking off insight gleaning tasks and/or marketing treatments.  Examples:
    • Detecting customer intent, such as intent to purchase based on multiple signals
    • Predicting and detecting key life events, such as a move, and providing proactive valuable offers
  • Certain marketing programs that run completely lights out that with automatic monitoring and tuning
  • Send-time optimization for all types of customer interactions
  • Automatic impression and response capture

What should marketers be doing today?

One lesson is don’t fear automation and AI in digital marketing, embrace it.  Marketers and CX professionals must automate to remain competitive and ensure their survival.  They must reduce the cost to acquire and deepen customer relationships, or standby while their competitors do it.

Ultimately, the savviest marketers build systems that entice customers to engage in less expensive and fully automated channels.  By doing so, buying cycles are compressed, the cost to serve comes down, and customer satisfaction rises.  But marketers must carefully monitor to see that satisfaction, revenue, and market share are improving.  They must constantly ask, “Are these automated or semi-automated interactions delivering hyper-personalized, frictionless, and valuable experiences?”

So regardless of how you rate your marketing capabilities today, there’s plenty of room for improvement.  Presently, most marketers still take about three months to create and execute new programs from inception to execution.   And many interactions, whether via branch, store, call center, or email, are still inefficient and labor-intensive.

Marketers must strive for efficiency and excellence in creative and operational areas.  Here are some things to consider in both:

The Modern Creative Laboratory

Consider these checklist items and benchmarks:

  • Form small teams of five to seven using modern collaboration tools like Slack
  • Incent teams to do research before brainstorming sessions.  Don’t reinvent the wheel
  • Hold brainstorming sessions in public and private.  Limit sessions to a few hours in public and a few days in private
  • Don’t mute wild ideas; encourage them.  Don’t just end up with a better buggy whip
  • Limit testing wilder ideas to 10% of budget, time, effort
  • Ensure the process, ideas, and outputs are transparent and easily accessible by others

The Modern Marketing Operations Factory

Consider these checklist items and benchmarks:

  • Employ small teams of five to seven using modern agile and marketing operations tools that force item prioritization, templated based case management, workflow, task creation, and work sprints (for repeatable processes)
  • Integrate content management systems and processes with marketing automation.  Fuse output to final step revision and deployment management systems and processes
  • Separate business-as-usual (BAU) changes from capability changes.  For example, making minor adjustments to an existing promotion would be treated as BAU, and these kinds of changes packaged into revisions that are deployed daily.  Capability changes that require more work, testing, and approvals can be deployed weekly or monthly
  • Automate and streamline as much work as possible, looking for ways to turn it into BAU work, via templates, re-use of assets, and rationalizing unnecessary steps and approvals
  • Look for as many places as possible to reduce or eliminate key entry
  • Once programs are in production, set up ways to automatically throw alerts when things go wrong or need to be adjusted.  For example, if response rates are low trigger a notification that includes key statistics and diagnostic tips
  • Use an always-on marketing approach to most customer interactions.   In this way, there becomes just one main program: The Next-Best-Action program.   That system is simply fed new offers that come and go, but the program itself is evergreen – just on 24x7x365 -always arbitrating for the next-best-action when a customer comes in a channel, and the next-best-touch for outbound outreach.

Conclusion

Although “never say never” applies to nearly anything, it’s unlikely your marketing job will be taken by AI.   But another human that knows how to use AI in digital marketing more effectively than you, well that’s another story.

And as modern marketers design the transition and march toward using technology for increased automation and AI in digital marketing, they must ensure consumers get what they want – relevant, rewarding, and timely value exchanges.  If marketers relentlessly automate without seeking continuous feedback from customers on the resulting experiences, they’ll fail.  Great brands will be those that can think creatively, design effectively, and execute flawlessly to deliver seamless experiences woven together by machines and humans.  Using this approach, marketers and their marketing machines will stay gainfully employed.

[i] Rexer Analytics, http://www.rexeranalytics.com/data-science-survey.html, 2017

[ii] Websitepromoter, https://websitepromoter.co.uk/how-to-adapt-your-seo-for-ai, 2019

[iii] Bureau of Labor Statistics, https://www.bls.gov/ooh/management/advertising-promotions-and-marketing-managers.htm, 2019

[iv] Oxford Martin School, https://www.oxfordmartin.ox.ac.uk/publications/view/1314, 2013

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The Smart Choice: Licensing an Off-the-Shelf RTIM Engine

In today’s fast-paced business environment, companies are constantly seeking ways to enhance their customer engagement strategies. One critical component of this strategy is the Real-Time Interaction Management (RTIM) engine, also known as marketing technology decision-making engines, next-best-experience engines, or customer engagement engines. These engines play a pivotal role in delivering personalized customer experiences, optimizing marketing efforts, and driving revenue growth. However, businesses often face a dilemma: should they build an RTIM engine in-house, buy an inferior one and customize it, or license an off-the-shelf solution? This blog explores why licensing an off-the-shelf RTIM engine is the smartest choice for large enterprises.

Delivering Value to Customers

Business stakeholders responsible for customer engagement solutions prioritize delivering value to their customers. They aim to optimize customer experience efficiently to achieve high ROI. This means considering all costs associated with the solution, including opportunity costs, and evaluating the solution’s value in terms of revenue generation, customer satisfaction, loyalty, and lifetime value. An effective RTIM engine is essential for achieving these goals.

The Pitfalls of Building In-House

Building an RTIM engine in-house might seem like a viable option for some companies, but it comes with significant drawbacks:

  • Higher Costs: Developing an in-house solution often incurs higher costs due to the need for specialized skills, ongoing maintenance, and infrastructure1.
  • Longer Time to Market: Custom development can take significantly longer compared to purchasing an off-the-shelf product.
  • Complexity and Risk: In-house projects can be complex and risky, with potential for delays and cost overruns.
  • Lack of Expertise: Many IT departments may lack the necessary expertise in marketing technology, leading to suboptimal solutions.
  • Maintenance and Upgrades: Ongoing maintenance and upgrades can be resource-intensive and costly.
  • Integration Challenges: Integrating a custom-built solution with existing systems can be challenging and time-consuming.
  • Opportunity Cost: The time and resources spent on building an in-house solution could be better utilized on core business activities.
  • Scalability Issues: Custom solutions may struggle to scale effectively as the business grows.

The Advantages of Licensing an Off-the-Shelf Solution

Licensing an off-the-shelf RTIM engine offers numerous advantages that make it a more attractive option for large enterprises:

  • Specialized Expertise: Vendors provide specialized expertise that ensures the software meets industry standards and regulatory requirements. This expertise is often difficult to replicate in-house1.
  • Reduced Risk: Licensing software from a vendor reduces the risk associated with custom development, including potential delays and cost overruns. Vendors have established processes and support systems in place.
  • Vendor Support and Updates: Licensed software often comes with vendor support, regular updates, and maintenance. This ensures that the software remains secure, up-to-date, and compliant with evolving standards.
  • Cost-Effectiveness: For certain applications, licensing software can be more cost-effective than building a custom solution. This is especially true when considering the total cost of ownership, including maintenance and upgrades.
  • Integration and Modularity: Modern SaaS solutions offer modular architectures and APIs, making it easier to integrate with existing systems and customize as needed. This flexibility allows businesses to tailor the software to their specific needs without starting from scratch.
  • Scalability: Licensed software from vendors is often designed to scale effectively as the business grows. This ensures that the software can handle increased demand and complexity over time.
  • Innovation and Ecosystem Support: Vendors often have robust ecosystems that provide additional tools, services, and integrations. This ecosystem support can enhance the functionality of the licensed software and reduce redundant technical work1.
  • Speed of Implementation: Licensing software from a vendor allows for faster implementation compared to building a custom solution. This speed can be crucial for businesses looking to quickly address market demands and opportunities.
  • Focus on Core Business Activities: By licensing software, businesses can focus their resources on core activities rather than diverting them to software development. This can lead to better overall business performance and efficiency.
  • Compliance and Security: Vendors often have dedicated teams to ensure that their software complies with industry regulations and security standards. This reduces the burden on the business to manage these aspects internally.

Addressing Pain Points in Marketing and Sales Processes

Any RTIM solution must address key pain points related to improving marketing and sales processes, focusing on data integration, customer journey, and campaign optimization. Here are some of the critical areas that need attention:

  • Data Points for Training Models: Understanding the data points available for training models for MQL to SQL conversion, including events, sources, categories, segments, and nurturing activities, is essential.
  • Understanding Upsell and Customer Journey: It’s crucial to understand upsell opportunities and customer journey stages to improve upgrades, downgrades, and churn rates.
  • Challenges in Data and Process Flow: Companies often face challenges in documenting buying patterns and process flows, with significant delays in onboarding and disparate messaging across different channels.
  • Marketing and Sales Treatments: Effective customer engagement involves various treatments, including tickets, events, in-house production, trips, client promotions, emails, premiums, and charitable donations, all managed through Salesforce campaigns.
  • Churn Model and Customer Segmentation: A churn model built by the data science team can output monthly churn propensity for customers, helping to identify those who need specific treatments.
  • Guided Selling and Next Best Actions: Implementing guided selling with next best actions, using data from platforms like Snowflake, can enhance customer journey stages without relying solely on historical data.
  • Operational and Data Integration Challenges: Addressing operational challenges due to disparate actions between brand marketing and field marketing teams, and resolving data integration issues across various systems, is vital.
  • Enterprise Architecture and Marketing Automation: Integrating various marketing and sales systems into a cohesive enterprise architecture, with a focus on automating processes and improving data visibility, is necessary.
  • Customer Engagement and Dashboard: Documenting customer engagements and aggregating client-level data into platforms like Snowflake, with a dashboard to provide insights and guidance on effective treatments, can drive better decision-making.
  • Future Steps and Roadmap: Blueprinting solutions, conducting executive briefings, and scoping sessions to align on the marketing and sales automation roadmap are essential for successful implementation.

The Fine Line Between Customization and Restrictions

When deciding between customizing a packaged app or composing software components, businesses must walk a fine line. On one extreme, highly customizable software components may incur hidden costs, such as maintaining customizations and updating them. On the other hand, licensing components with restricted customizations may limit a company’s ability to differentiate itself from competitors. Striking the right balance is crucial for maximizing value and minimizing costs.

The Role of SaaS Solutions

Software-as-a-service (SaaS) has revolutionized the way businesses approach software customization. Leading commercial off-the-shelf RTIM solutions offer standardized architectures and robust ecosystems, including partners, communities, training, and educational services, to support their integration and use. This makes SaaS solutions a sustainable and cost-effective choice for many businesses.

Conclusion

In conclusion, licensing an off-the-shelf RTIM engine is a strategic choice for large enterprises looking to enhance their customer engagement strategies. The advantages of specialized expertise, reduced risk, vendor support, cost-effectiveness, integration and modularity, scalability, innovation, speed of implementation, focus on core business activities, and compliance and security make it a compelling option. By addressing key pain points in marketing and sales processes and leveraging the benefits of SaaS solutions, businesses can achieve their customer engagement goals more efficiently and effectively.

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From Ancient to Modern Martech Stack – 10 Immutable Laws

It’s a Data Collector & Cruncher, Insights Producer, Real-Time Processer, and Channel Connector – But wait!  There’s more!

Since the Dawn of Martech Times – The Goals & Principles Remain the Same

For a lifetime (mine anyways) marketers have sought the holy grail of one-to-one customer engagement: Right Customer, Right Time, Right Message/Offer, Right Channel/Place.

In pursuing that goal, those working for enterprises knew to scale beyond mom/pop audience size they needed big tech help –  big data & insights about their customers, at-scale machine learning to calculate what to say and offer, a large & dynamic curated library of messages, as well as direct connections to the ever-growing channels to deliver them – both ones where customers were in channel (inbound) and ones where nudging was required (outbound).

Bringing at-scale tech to this goal started by using big databases.  Those databases held customer account and transaction data and used queries achieving the first step – finding sets of customers with differences in customer behavior.  And those differences proved to be great insights to train models and predict future behavior.  Next, the pioneers matched messages to the predicted behavior segments.  A likely buyer of a certain product just needed an offer for that product.  Then, send that message, get a response, and chalk up higher conversion rates.  

Take, for example, credit card marketers.  They segmented their base into 2 main categories: Transactors and Revolvers.  Transactors paid off their bill.  Revolvers didn’t.   So based on this, they offered transactors incentives to transact more so they would increase revenue (from fees charged to the merchants) and offered revolvers more credit – balance transfers / credit limit increases (where they made fees on the growing balances).  The results:  higher response rates, campaign lift (over controls) and more revenue.

That’s it.  For the next 30 years, businesspeople in a variety of industries – from banking to telco to insurance and others – built out these systems and simply sought more data, improved prediction models, and connected their offers to more channels.  Sounds simple right?  Oh, but it’s not.

Why?  Because big data got bigger, messier, and harder (and more important) to carefully manage.  Getting the right insights from that data was (and still is) tricky.  Establishing data lineage, privacy, and proper governance became crucial.  Algorithms to accurately predict intent, and the right action to take next, evolved – and required controls.  New AI methods sprung up along the way (and continue to do so – with GenAI another example – we’ll get to that later).  And we all know about the proliferation of channels and digital devices.

Martech Stack Ecosystem – Then and Now

Ecosystem wise, not much has changed in the basic framework of a Martech stack since the early 90’s.  Figure 1 shows the main components.  Data is collected from a variety of sources at different velocities.  Some data is distilled before it’s sent, providing insights.   The stack itself produces both data & insights, and those are made available to other systems.  A plan programs the content, data, and strategies employed by the decision system, and gets informed by the results.  AI, both inside and outside the stack, powers predictions.  Finally, ranked recommendations and messages are activated, and married (using metadata) with the appropriate content and delivered to channels.  There, an orchestration layer may dip down to get actual content (e.g., digital images), and then consumers get the output (and hopefully react positively).  Dashboards, reports, and analysis tools help marketers understand the results.

And the goals remain unchanged.  Provide personalized experiences.  Doing so generates lift (higher response and conversion rates) because more relevant offers are presented to customers who are more likely to want them.  It’s not rocket science it’s just marketing science.

modern martech stack

Figure 1: Conceptual Martech Stack and Surrounding Ecosystem

At the high-level, yup that’s pretty much it.  I’ve researched and witnessed this pattern for 30 years.  Attending conferences, following Martech Stackie Awards (2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023.), reading countless analyst blogs, and working with hundreds of enterprise clients across the globe.

The 10 Immutable Laws of Martech Stacks

So, what have we learned?  Since there is an overabundance of data, and technologies of various kinds come & go, lock in on designing the modern Martech stack so that it adheres to principles that have withstood the test of time.

  1. Collect the right data. You don’t need a huge number of customer behavior attributes, but instead the right ones for the business problems being solved (e.g., reduce churn) and so reflect customer intent and cause and effect with likelihood to respond to offers to solve those issues.
  2. Make sure collected data is accurate and, in as much as possible, feed it into your Martech stack in real-time. 
  3. Use segments to study common traits and behaviors.  Assign segment attributes to customers, not the other way around.
  4. Make decisions on individuals, not on segments. 
  5. Use adaptive models to calculate “offer propensity.”  Establish that these models are learning continuously on data you are collecting.
  6. Use one set of engagement strategies and rules for inbound & outbound decisions.  Do not separate this logic and place it into channel systems.
  7. When making inbound decisions, send them immediately. Do not cache decisions into channels waiting for a customer to appear.
  8. With outbound marketing, only send permission-based relevant messages to customers on channels they opt-in and respond to, at times they prefer, and with content that is relevant.  
  9. Use behavior triggers, not pre-set schedules, to determine the right time to send.
  10. Select the latest content just prior to presenting offers (e.g., versions of your offer, that include creative and language variation tests).

And here are a few more pointers: 

You need a few good foundational software platforms (linchpins) that integrate, not 10k technologies (https://chiefmartec.com/2022/10/why-there-are-10000-martech-products-that-kinda-all-do-the-same-thing-but-not-really/). 

Which ones?  Follow this basic advice for the 4 main ones you need, and that must operate well together:

https://customerthink.com/the-final-4-martech-platforms-and-ecosystems/

Compare your design to others that have been successful.  Here is a 2023 stackie winner.  Notice the biggest bubbles:  Content, Execution Platform, Analytics (Insights).

https://chiefmartec.com/wp-content/uploads/2023/04/itau-unibanco-martech-stackie-1456px.jpg

And here is another, centered on using data & AI to power a brain to make decisions during the customer journey cycle (awareness, consideration, decision)

https://chiefmartec.com/wp-content/uploads/2022/05/verizon-martech-stack.png

Don’t drop what you are doing to chase the latest fad.   In other words, don’t fall victim again to the “shiny new object syndrome.[i] ”  Stay the course and be sure to devote some of your tech budget to innovation testing (maybe 10%).   Hopefully, you were already doing that prior to the GenAI hype setting in. 

Speaking of GenAI, test it to see if it helps with Law #5 (finding features for models to learn on) and Law #10 (challenger tests for creative & language variations)

Old saying but it holds true: You can’t manage what you can’t measure.  Measure your program effectiveness by looking at champion creative, promotions, and messaging, and then try new variations, and measure again.

Conclusion

The evolution of the Martech stack over the years has brought about significant advancements in data collection, insights generation, interoperability, and customer engagement. The fundamental goals and principles of delivering personalized experiences to customers remain unchanged. However, as technology and data have become more complex, it is essential to adhere to the 10 immutable laws of Martech to provide the right ingredients for success.

Collecting the right data and ensuring its accuracy in real-time is essential. Segment customers to understand behaviors but make decisions at the individual level in real-time.  Use adaptive models that continuously learn from data and help calculate offer propensity accurately. Inbound and outbound decisions should follow unified strategies and use behavior triggers for timely engagement. Outbound marketing should focus on sending relevant messages on preferred channels.

Build a Martech stack with a few foundational software products that provide insights, content management, and decision management.  The emergence of GenAI offers opportunities to enhance model learning and conduct challenger tests for creative and language variations. However, it is important to test and measure its effectiveness before fully adopting it into the Martech stack.

Finally, measure program effectiveness and iterate with new variations for continuous improvement. By following these principles, businesses can achieve personalized customer engagement and drive higher response and conversion rates.

[i] HBR, https://hbr.org/2021/07/dont-buy-the-wrong-marketing-tech, 2021

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Martech Strangelove: How I Learned to Stop Worrying and Love the GPT Bomb

Secrets to Martech Success with GPT

“I can no longer sit back and allow GPT infiltration, GPT indoctrination, GPT subversion, and the international GPT conspiracy to sap and impurify all of our precious human thoughts.”

“Well, boys, I reckon this is it – combat toe-to-toe with the GPT Robots.”

Intro

Some of us are contrarian humans and it’s not in our nature to follow fads.  And so, in November of last year when these pre-trained AI transformers – lurking backstage for years — took the main stage, and then threatened to hack into nuclear codes, those of us so inclined raised our eyebrows and dug in on the side of us carbon-based thinking units.  

I’ve been to two tech conferences in the last month and witnessed firsthand the growing mayhem around this new-age AI.  No doubt one big difference between now and the emergence of the 90’s AI is the speed at which this hype cycle reached a crescendo, and the extent to which GPT is being sold like snake oil to cure all ailments.  And like search in the late 90’s, the speed and ease at which it has entered into the public conscious, and even the accessibility for anyone to use it, has got everyone’s attention.

Accompanied by Langchain, AutoGPT, autonomous agents, prompt engineers, embeddings, and hallucinating large language models – GPT along with a whole new universe of techniques and terms have exploded onto the scene.  Within months all major Martech vendors scrambled fighter jets to launch press releases on yet to be released GPT-infused software.

But then a few days ago, 8 months after G-Day, I popped onto my bank’s website, and the same old UI with the same old poorly personalized experience, sorely lacking self-driving marketing features or an optimized customer experience stared back at me.  Seems maybe deep-learning NLP wasn’t the only thing taking LSD.

The coming out of these inventions is interesting – captivating in fact.  And misunderstand me not, as a big lover of GPT’s rhymes, poems, pictures, and puns, I was very intrigued by its chic performances.  Equally fascinating is that as pack animals, it’s in our human DNA to follow; to want to be part of the latest fashions.   And so GPT gets my alpha dog / tech beauty queen of 2023 vote. 

Still, coming to terms with this latest knighted AI craze — “Sir GPT” — might be prudent.  Without kneeling at its throne, even non-conformists might have to accept it as an eventual buddy, same as happened with smartphones and search a quarter century ago.

So here are 4 tips for Martech leaders on how to approach the GPT mania.  

Tip 1: Do your GPT homework – Separate fact from fiction

With each Martech vendor offering GPT, consider asking these questions:

  1. Show me the GPT features in your software?  When and in which version will this be available?  Can you give me some precise plans around availability?
  2. Can you show me the top features you believe will produce a quantum leap in impact in terms of either lift or productivity (versus features that are simply automating legacy processes)?
  3. What about the value I will get from using these features?  If you can’t show me actual case studies yet, show me projected value, and the math used.
  4. I work in a very large business, so how can I use these features practically and responsibly within an enterprise change management process?
  5. How much will this cost?  Not just the GPT subscription cost, but any other costs that will go into putting this into production.
  6. Can I trial some of these features today?  How much will that cost?
  7. How can I set up a fair test of these features against my baseline?
  8. What are the top 3 features on your roadmap that have nothing to do with GPT?

The answer to that last question will be telling for several reasons.  First, it will give insight into the overall Martech roadmap and strategy, whether balanced — or worse, hastily constructed.  Second, the features mentioned will help with a comparison to your goals and priorities, and how well aligned they are.

As a marketer, consider these as potential use cases:

  • Safely generating alternative subject and body copy for existing promotional offers.  For example, new subject lines for emails, website tile and call to action labels, and offer descriptions.
  • Built-in assistants to automatically configure marketing components that previously required manual set up.  For instance, when creating a campaign in a certain category, and providing basic information, GPT assists in pre-populating fields to minimize manual key entry.
  • For inbound channels, GPT provides more intelligent chat bot assistance.   It does a better job answering basic questions, searching knowledge bases and documentation for answers, and learns from feedback on whether its answers were helpful.

Tip 2: Run some experiments

Consider running a few controlled experiments to gauge GPT’s applied value.  

To ensure the tests are useful, make sure to measure against a known benchmark.   Ask if the experimental design is reproducible, fair, scalable & cost effective.  In other words, will the test be easy to set up?  Can it be run repeatedly in a short period of time (producing adequate experimental results to measure against the benchmark)?

  • To test new copy, pit GPT’s proposed writing against the champion.   Randomly assign GPT’s offer to a 2% sample of customers.  Does it outperform the champion over time?
  • Do the built-in assistants really save time in configuration?  Or is it a bell/whistle that looks good in a demo, but that real-life users won’t use?  Do usability testing with the real users to find out.
  • Do the chat bot interactions result in higher NPS scores?   After each chat bot session, ask for an NPS score for the session, and compare that with current scores.

Tip 3: Study the results and invest accordingly

This tip may seem obvious.  However, the knee-jerk reaction might be to adopt the new feature(s) if the test results show any improvement over the baseline.   But make sure to weigh the total costs versus the actual benefits.  If the lift is meager, but the costs high, the ROI might not be there.

In addition, network and study your peer’s experiences as the Guinee pigs.   Are the projects going well?   Are they producing the expected results?   Is there a stark difference between what was on the glossy brochure versus how the real-life experience turned out?  In other words, learn from the mistakes of others.

Tip 4: Stay the course – with a solid overall DNA – Data aNd Analytics – Modernization Strategy

Massive data and fine-tuned analytics are the core materials of GPT (or any successful application of AI), and both must be the genome that fuels every company.  Firms must weave data & analytics into their organizational fabric to fundamentally transform two aspects of their business in careful balance: a hyperfocus on personalized customer experience while becoming operationally efficient.

But where to start?   Here are three things to concentrate efforts on:

  1. Implement advanced analytics to improve customer experience: By compiling and analyzing customer data across a variety of channels, you’ll gain insights into their preferences and behavior. This will support personalized services, development of appropriate products and services, and improved customer engagement.  In addition, use predictive analytics to identify the best channels to reach your customers.
  2. Optimize operations and reduce costs through data-driven decision-making: By analyzing operational data, you can identify inefficiencies and bottlenecks in your processes. This can help streamline operations and reduce costs. For example, banks can use data analytics to optimize loan approval processes, improve credit risk management, or reduce fraud.  Telcos can analyze real-time network performance data and even identify potential issues before they occur and take proactive measures to prevent them.
  3. Build a data-driven culture and foster data literacy among employees: To fully leverage the power of data and analytics, you need a culture that values data and encourages data-driven decision-making. This requires not only the right technology and infrastructure but also a workforce that is knowledgeable about data and analytics. Winner brands will provide training and resources to help employees develop data literacy skills and create a culture that encourages experimentation and innovation.

Conclusion:

In the end, though there will be a few lucky hares, for the majority the tortoise-like approach is the winning strategy.

Oh, by the way, I took my intro words, plugged them into GPT, and gave it the following prompt. 

————————-

GPT prompt: Write me a pun based on the fable of the tortoise and the hare using this prose <inserted above intro words>

————————-

GPT response:

In the world of AI, a contrarian thought I bore,

As GPT took the stage, its hype soared and more.

But now I’ve come to terms with this AI craze,

Sir GPT, they call it, deserving some praise.

I won’t kneel at its altar or its throne,

But I’ll let it into my life, like Google once known.

Tech conferences showcased mayhem and noise,

With Langchain, AutoGPT, and AI toys.

Martech vendors scrambled, their releases to boast,

Yet my bank’s website remained a poor host.

No self-driving marketing, no personalized flair,

Perhaps deep-learning NLP was also on a wild tear.

These recent inventions captivate, it’s true,

And I love a good pun, rhyme, or verse that’s new.

But as pack animals, we follow the alpha’s lead,

And GPT claims my alpha dog award, indeed.

So here are four tips for Martech leaders to heed,

Do your GPT homework, experiment and succeed.

Study the results, invest wisely in this race,

But remember, the tortoise’s approach sets the winning pace.

————————-

This robot is kind of a cute poet.  But Sr GPT, you didn’t exactly write a pun, so allow me to assist you:

If one takes the hare’s approach you may be “hare today but goon tomorrow.”

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Trusting Personalization without Sacrificing Privacy

In today’s digital age, consumers are increasingly seeking more personalized products and services, and a customized experience.  And although personalization is nothing new, it has evolved radically in the last 30 years with large businesses able to use AI and technology to cater to these demands.  Just look at the latest frenzy, GPT, to get a sense for how technology is accelerating its impact on marketing, customer service, and personalization.

Balancing Personalization & Privacy

Already a wide variety of personalization techniques are employed by businesses to tailor to individual preferences and improve the customer experience.  It’s been 30 years in the making, starting with early adopters using simple techniques such as addressing a direct mail letter with first name rather than “Current Resident,” to today’s savvy enterprises using massive databases of consumer behavior and advanced analytics to provide hyper-conditional content, individualized promotions, and concierge-like digital services.

But a counter dynamic is also at play.  As personalized products and services have become more prevalent, consumers have also awakened to how their data is collected and even misused.  Because of this some are less likely to share information, push for more legislation, more frequently opt out, and even ask for their data to be deleted.  Clear battle lines have been drawn between hyper personalization and privacy.

This presents a dilemma for businesses, as they attempt to balance providing personalization in a responsible and controlled manner.  Consumers are fickle.  They want great experiences, but they also expect that any data they turn over is secure and is used in compliance with their wishes.  In this context, it’s crucial for companies to strike the right balance between personalization and privacy protection. 

The Value of Personalization

When asked, consumers repeatedly respond that they want more personalization (in many cases greater than 75%), especially younger people. [i]   When they see customized ads fitting their preferences and behaviors, they’re more likely to engage and then purchase. This benefits both the consumer, who gets offered interesting products or services, and the company, which increases its revenue and profits.  Moreover, personalized recommendations can create a sense of loyalty and trust between the consumer and the company, leading to repeat business, long-term customer retention, and positive word-of-mouth marketing.

Personalization has become a principal factor in consumer decision-making. A study by Epsilon found that 80% of consumers are more likely to do business with a company if it offers a personalized experience. [ii]  Personalization has many benefits for consumers, such as saving time and increasing convenience. For instance, personalized recommendations on e-commerce websites can help consumers find and then ask for products that they may not have otherwise considered. Similarly, personalized apps can help users achieve their goals by providing services like tailored financial plans and wellness insights & activities.

Some consumers may view the collection of their personal data as a fair trade-off to get more personalization.  They accept that the exchange of their data is worth getting good recommendations, more perks, and a better overall experience.  And these consumers assume and inherently trust that businesses will use their data responsibly and will take the necessary steps to protect their privacy. 

But not everyone thinks that way about turning over their data and trusting businesses.

The Need for Privacy

There is another camp of consumers that prioritize privacy and may view personalization as a threat to their rights. They hold that their personal data is being exploited without their consent and that businesses are profiting from their information.  And they are very vocal about this, and are influencing their friends, followers, and even law makers.

These consumers are skeptical of businesses’ ability to protect their data and worry that their information could be used against them in the future.  As a result, they have pushed for more protections and use various mechanisms (ad blockers, opting out, surfing incognito) to avoid sharing personal information. 

They are very hesitant to share what they consider extremely personal information, such as their location, browsing history, or purchase behavior. And they are not a small faction. In fact, a Pew Research Center study found that 79% of Americans are concerned about the way their personal data is being used by companies beyond what they intended.[iii]  Years ago, the Cambridge Analytica scandal highlighted these fears, when the data of millions of Facebook users was harvested without their consent and weaponized for political purposes.

Consumers believe they have a fundamental right to privacy and are increasingly demanding more control over their personal data.  In recent years, there have been several high-profile data breaches and scandals involving the misuse of personal data. These incidents have increased consumer awareness about the importance of privacy, and they are becoming more vocal about their concerns. Consumers want to know how their data is being used, who has access to it, and how it is being protected.

Consumers are also becoming more aware of their digital footprint and the potential consequences of sharing personal information online. They’re concerned about identity theft, fraud, and other forms of cybercrime. In addition, they worry that their personal information could be used against them, such as by insurance companies or potential employers. As a result, consumers are becoming even more cautious about sharing personal information online, and as such only do it with businesses they trust.

The Role of Trust & Regulation

Trust is a crucial factor in the relationship between consumers and businesses. Consumers are more likely to share personal information with businesses that have not breached that trust and that prioritize their privacy. Even so, trust is fragile, and businesses must work hard to never violate it and always maintain it.

Businesses can build trust with consumers by being transparent about their data collection practices and providing clear explanations of how they use consumer data. They should obtain explicit consent from consumers before collecting any personal information and should provide consumers with the option to opt-out of data collection. And they must ensure that data is protected from data breaches and cyberattacks.

Privacy concerns can also impact consumer behavior in a more general sense. A review of the literature by Taylor and Francis Online found that online privacy concerns can lead to reduced trust in online transactions and lower engagement with online platforms.[iv]  This can have significant consequences for companies that rely on online channels for marketing, sales, and deepening relationships with existing customers.  What’s more, privacy concerns have a ripple effect across businesses and industries, as consumers become more skeptical with each new incident.

Best Practices

Here is a baker’s dozen of best practices to effectively balance personalization and privacy.

For Personalization:

  • Rollout individualized personalization – Individualized personalization uses preferences and behaviors of individuals (not segments they belong to) to custom tailor products, servicing, and messaging.  It affords numerous benefits, including increased convenience, engagement, and ultimately overall satisfaction.  And done right, it will build long-term trust.  When personalizing for pre-login purposes (such as for unknown browsers or mobile devices) pay careful attention to whether adtech data collection vendors are using tricks, like cname cloaking, to mask a 3rd party domain still receiving data when the end consumer may not approve of this.  Using a trick like this might erode trust and ultimately backfire.
  • Build cross-functional personalization teams – Personalization requires input and collaboration from multiple departments within an organization, including marketing, IT, data & analytics teams, and customer support. Building cross-functional teams can help companies break down silos and ensure that all stakeholders are aligned on personalization goals and strategies.
  • Adopt agile methodologies for feedback and testing – Agile methodologies enable companies to iterate quickly and respond to changing customer needs and preferences. Adopting agile methodologies can help companies test and refine personalization strategies and ensure that they are delivering the right content and experiences to the right customers at the right time.  Pick an agile personalization platform.  It should be capable of always-on variation testing (runs champion / challenger experiments automatically) and able to deploy necessary adjustments to programs in a day or less not weeks.

For Privacy:

  • Appoint a Chief Privacy Officer – Appointing a Chief Privacy Officer (CPO) is a critical organizational change that can help companies balance personalization and privacy. The CPO is responsible for ensuring that the company’s data privacy policies and practices align with industry standards and regulatory requirements, while also driving data-driven innovation and personalization.
  • Establish a clear data governance framework – Companies must have a clear governance framework for data management that outlines data privacy policies, data protection practices, and compliance requirements. This framework should be regularly reviewed and updated to ensure alignment with changing regulations and best practices.
  • Use privacy nudges – Nudges can be an effective way to help consumers make more informed choices about their data privacy, while still allowing for customization.  Privacy nudges are interventions designed to influence behavior without restricting freedom of choice.  For example, a company could use a privacy nudge to encourage consumers to read the privacy policy before accepting it. A study by Balebako et al. (2015) found that privacy nudges can be effective in improving privacy outcomes for consumers on social media platforms. [v]
  • Conduct regular privacy impact assessments – Privacy impact assessments (PIAs) can help companies identify privacy risks and implement appropriate controls to mitigate these risks. Conducting regular PIAs can help companies stay ahead of changing privacy regulations and address potential privacy concerns before they become major issues.
  • Foster a culture of privacy first – A culture of privacy first starts at the top of the organization, with senior leadership setting an example and emphasizing the importance of privacy in all aspects of the business. Companies can also provide privacy training and awareness programs to all employees, to ensure that everyone understands the importance of privacy and how to protect it.

For making the right technology choices:

  • Invest in a consent management platform – These platforms allow companies to manage user consent and data collection preferences, enabling users to choose what data is collected and how it is used. Consent management platforms can also help companies comply with regulations such as the GDPR and CCPA, which require informed and explicit user consent for data collection and processing. Invest in a customer data platforms (CDPs) only if first-party data and device identity management is scattered or missing.   CDPs are a centralized system combining customer data from multiple sources (such as transaction & behavioral data), device identity, and consent. Companies must be transparent about their data collection and usage practices and provide clear and concise information about how data is being used. This can be accomplished through user-friendly privacy policies, clear consent mechanisms, and open communication channels.  
  • Leverage the right artificial intelligence (AI) – AI can help companies analyze vast amounts of customer data and identify patterns and trends that can inform personalization strategies. Companies can use the right AI for the right job do better. 
    • For example, firms using Bayesian models to deliver personalized next-best-action recommendations tailored to each individual customer (that adapt in real-time as preferences shift) report up to 6x lift in response rates. [vi] 
    • They also enjoy an added advantage that these models are transparent and can be pre-checked for bias, responsibly deployed, and explained.  Compare that to this statement in a recent research paper on using GPT: “We do not intend for the model to be used for harmful purposes but realize the risks and hope that further work is aimed at combating abuse of generative models.” [vii]
    • Use other responsible techniques, such as federated learning and homomorphic encryption, which enable machine learning models to be trained on user data without accessing or exposing individual user data.
  • Use anonymization and pseudonymization techniques – These are methods of data de-identification that can be used to ensure that sensitive personal information is protected while still allowing for effective analysis to improve personalization. Anonymization removes all identifying information from a dataset, while pseudonymization replaces identifying information with a pseudonym, allowing for the data to be re-identified if necessary.  If data is shared externally with other parties, use clean rooms.
  • Consider differential privacy algorithms – Differential privacy is a technique that adds noise to a dataset to protect individual privacy, while still allowing for effective data analysis and personalization. Differential privacy algorithms can be used to provide personalized recommendations, while still ensuring that individual user data remains private.
  • Employ secure data storage and transfer protocols – Companies must ensure that user data is stored and transferred securely, to prevent unauthorized access or data breaches. Technologies such as encryption, secure sockets layer (SSL), and transport layer security (TLS) should always be used secure data storage and transfer.

Conclusion

Being customer-centric is about adopting a mindset that improving customer experience is paramount to business success. Personalization is an essential strategy for businesses to improve the customer experience. However, as personalization becomes more prevalent, consumers are also becoming more concerned about their privacy.

As the debate between personalized products and services and data privacy rages on, companies must navigate a delicate balance between meeting consumer demands for customization and respecting their privacy. Failure to do both right could have profound consequences, including loss of trust and customer loyalty, as well as legal and regulatory penalties. However, those companies that can offer personalized products and services while also prioritizing transparency and data privacy will be well-positioned to succeed in the digital age.

The future belongs to those companies that can harness the power of data-driven personalization in a responsible and transparent way, while respecting the privacy and autonomy of their customers. In short, the choice is clear: companies can either embrace data privacy as a core value and use it to build lasting relationships with their customers, or risk being left behind in an increasingly competitive market.

[i] Capco. (2021). Insights for Investments to Modernize Digital Banking. https://www.capco.com/Intelligence/Capco-Intelligence/Insights%20for%20Investments%20to%20Modernize%20Digital%20Banking

[ii] Epsilon. (2018). The power of me: The impact of personalization on marketing performance. https://www.epsilon.com/-/media/files/epsilon/whitepapers/emea/the-power-of-me.pdf

[iii] Pew Research Center. (2019). Americans and privacy: Concerned, confused, and feeling lack of control over their personal information. https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/

[iv] International Journal of Human-Computer Interaction. (2020).  Online Privacy Breaches, Offline Consequences: Construction and Validation of the Concerns with the Protection of Informational Privacy Scale. https://www.tandfonline.com/doi/full/10.1080/10447318.2020.1794626

[v] Balebako, R., Danish, R. K., Hong, J. I., & Cranor, L. F. (2015). Privacy nudges for social media: An exploratory Facebook study. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, https://dl.acm.org/doi/abs/10.1145/2487788.2488038

[vi] Pegasystems. (2022).  Coutts: Delivering world-class banking experiences at scale.  https://www.pega.com/customers/coutts-customer-decision-hub

[vii] Semantic Scholar. (2023). Structure and Content-Guided Video Synthesis with Diffusion Models. https://www.semanticscholar.org/paper/Structure-and-Content-Guided-Video-Synthesis-with-Esser-Chiu/07be0ec1f45e21a1032616535d0290ee6bfe0f6b

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CRM Magic or Smoke and Mirrors?

Old stuff is commonly stamped as long in the tooth, antediluvian – to be face-lifted, remade, or simply discarded after years of service.

Amazingly in the CRM world, some things that never even get full adoption or wide-spread use, still get per annum marketing make overs – no doubt aimed at luring buyers with brand new fairytale names and future promises.   Take for instance CDPs (Customer Data Platforms), modules offered by most of the CRM vendors.

Is it truly CRM magic or just hocus pocus?

Genie, the latest announcement by Salesforce, is a recent example of this trend and hard to size up.  Is it just a new data model or worse, just a fancy new name for an existing CDP product?  Or is it really a new & shiny customer data platform?  Or is it something different?  Perhaps a bundle of existing offerings with some minor enhancements.  As always, time will tell.  When the smoke clears, will we realize there’s nothing new and exciting available today – but instead just new promises. 

Salesforce isn’t the only vendor guilty of polishing old code, announcing ahead of the curve, or re-packaging existing product with new marketing wrappers and new names.  Many other web analytics, content, and data management vendors are constantly renaming products to jump on messaging bandwagons to announce the next magic potion.

Reading the headlines, here’s the takeaways so far on Salesforce Genie:

  • Salesforce suggests it’s closer to assembling and updating customer profiles in real-time now, but it’s not real-time.   Commonly accepted definitions of real-time are that processing happens in under 1 second.  But at Dreamforce we heard, “real-time is 5 min ago not 5 days ago.”  
  • The Marketing Cloud Genie seems to be a bundle of the Salesforce CDP, Personalization (Evergage), Engagement (Journey Builder and Email Studio), and Intelligence (Datorama).
  • There is a direct integration with Snowflake which sounds interesting but unfortunately not much detail was provided.
  • Amazon Sagemaker can directly access Genie data.  This could benefit data scientists working in this tooling, to get data prepped easier and faster for model building purposes.
  • Einstein powered AI-content selection was discussed.  Is Einstein considered part of Genie?  Not clear.  This allows personalizing the content selected based on a consumer’s location & associated weather data.    
  • It’s not clear how Salesforce will price Genie.
  • Einstein Engagement Frequency Reporting with “What If” analysis – this is depth of file analytics (how many targets to include in campaigns) and fatigue reporting – the announcement of a “what if” capability allows for some basic scenarios to be run.
  • Salesforce users can now bulk import customers (called contacts in Salesforce lingo) into Salesforce Engagement.  
  • Various enhancements to Salesforce Intelligence (Datorama) were announced, including a control center for data governance.

All of this, including the last two points beg a major question.  How many CDPs does Salesforce have now?  By one count there may be as many as four:

  1. Salesforce CDP (formerly called Salesforce 360)
  2. Marketing Cloud Engagement Datastore
  3. Marketing Intelligence Datastore
  4. Genie (which by some accounts, may include an upgrade of some of the Evergage CDP capabilities)

Some tips:

Instead of banking on promises and new names, focus on outcomes and what can be achieved with proven solutions.  Chasing wet behind the ears data management technology, or worse vaporware, can be expensive, frustrating, and fraught with tremendous opportunity costs.  We should have learned by now that data management technologies in and of themselves won’t return value.  Build it and they will come doesn’t work.  You need good data, but it alone has no value until you activate it.  And you’ll need the right decision engine tightly integrated with it to get value.

Instead of the marketing headlines and superficial news stories, look for product documentation and actual training materials that describe the actual GA product, how it’s configured, and what features it contains.

Look for real customer accounts of using the software and the value they got in return.

Read crowd review sites, such as Trust Radius, G2, and Gartner Peer Insights to get real user feedback.

In summary, buy real working products not promises.

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CDPs Then & Now – The Customer ID (Identification & Data) Problem

In November 2019 perhaps you caught this article: “To CDP or NOT – 3 tips – then you decide.”  The main takeaway – the CDP space is a quasi-market with a mixed bag of firms coming from different lineages and different levels of capability, maturity, and focus.  The conclusion: buyer beware and standby.

That was BC – Before COVID-19.  Since then, what hasn’t changed about the world?  And like everything in 2020, the CDP market was not immune to upheaval.  And although the basic premise for adding a CDP into the Martech stack is still the same:

  1. Help resolve customer identity
  2. Rationalize and manage customer data
  3. Make that data accessible to other systems

…what’s changed are the vendors involved, and their core and extended capabilities, which are substantially different nearly three years later.

Most markets appear as nebulous categories, and the CDP market was no exception.  But as buyers and vendors evolve, dust settles, and the picture becomes clearer.  Still, two important aspects of what a CDP should supply loom large and are worthy of close inspection.  Namely, providing customer recognition/identity management and distilling the right (and righteous) customer data into meaningful insights.

Considering those key features, let’s explore a few of the big changes since November 2019:

  • The huge marketing cloud players entered the market:  Adobe, Oracle, and Salesforce
  • More consolidation took place, with small CDPs swallowed up by an interesting mix of companies
  • Perilous new milestones reached for third-party cookies and stealth consumer tracking

Stick with me.   You’ll get insight into these three changes, three tips, and some final thoughts.

Marketing Cloud Titans Enter CDP Fray

Adobe Real-Time CDP

In early 2020, Adobe entered the CDP ring with Adobe Experience Platform’s Real-Time CDP, promising to “Combine all individual and company data — internal and external, known and unknown — into a standard taxonomy that can be activated in real-time.”[i]  A tall order indeed.

Although certainly set up to collect digital data by way of Adobe Launch & Analytics, Adobe’s aggressive mission to combine “all data” for B2C and B2B across known and unknown, lacks focus and gives reason for pause.

Adobe has fared well in providing digital marketing data & support for early-stage customer journey activity, with its first-generation web analytics and tag management (by way of its Omniture acquisition over 10 years ago), followed by its subsequent purchases of Demdex (third-party cookie data-management platform), and marketing automation firms like Neolane (B2C) and Marketo (B2B).  Yet with the third-party cookie tracking foundation crumbling as the final browsers outlaw it, they’ve had to look for another way.  So far, that appears to be using CNAME record cloaking, which in effect is just a clever DNS hack to circumvent gaining explicit permission to track. 

The ultimate jury and judge (the consumer) may not approve of this tactic (once they discover it).  Further, with a shortage of direct access to first-party behavior data, customer analytics depth, and channel breadth, Adobe still struggles to develop deep customer understanding and natively/performantly enrich its customer profile.  And other than collecting raw digital data in real-time, not much else about Adobe’s CDP is real-time and insightful.  Adobe nonetheless plows forward with bold statements of real-time and unity that potential CDP buyers should take with a grain of salt.

As a major marketing cloud player, Adobe will eventually amass more digital data, improve its signal detection, and get more apt at activating those signals and audiences in acceptable ways.   But for now, buyers should beware of completeness claims, tracking practices, data feeds and speeds, and external integration features.

Oracle’s CDP

Interestingly, googling with the term “Oracle CDP” yields a top result pointing to an Oracle whitepaper-like webpage espousing that a “customer data platform (CDP) is software that collects and unifies first-party customer data.” [ii]  So far so good.  

Reading on, the article mentions “first-party data” 11 times, never mentioning third-party data until the final punchline at the end, where the author claims that a Customer Intelligence Platform (CIP) is different from a CDP because it “incorporates anonymous, third-party data as well as first-party data.”    It’s here that Oracle tries to differentiate its CDP, Oracle Unity, from all others.  That differentiation attempt falls flat, and is oddly fascinating on three fronts:

  • Oracle has almost no choice but to take this approach, since it spent $400m on BlueKai in 2014, one of the world’s leading third-party data trackers.  As such, Oracle wants the buyer to believe they get a premium from contracting with a CDP that can merge third-party data.
  • Oracle claims it’s not really a CDP, but instead differentiates as a Customer Intelligence Platform (CIP), and not just for marketing.   Amusingly, in my June 2019 article I advocate for a CIP – The Final 4: MarTech Platforms and Ecosystems –  yet with the middle letter short for insights about individuals attainted from first-party data, not general intelligence.  Very different CIPs indeed.
  • The reason for the demise of the cookie-based cottage industry and third-party data is that it was built on a house of cookie cards, gathering and brokering consumers’ data without explicit permission, and inherently unreliable as a good proxy for consumer intent & behavior – one of the major tenants for a CDP.

Given this, be careful with Oracle’s CDP (or CIP) solution, with its bias toward third-party data, paid media channels, and early-stage acquisition use cases.  Purchasing one means buying into the value of third-party data and acquisition use cases, while not solving for data-driven, real-time 1-1 customer engagement use cases, deeper into the relationship, on owned channels.

Salesforce’s CDP

Late in 2019, as the virus was unknowingly spreading, Salesforce began spreading the news about its new Customer 360 Truth, claiming it had a product with “a new set of data and identity services that enable companies to build a single source of truth across all of their customer relationships.“ [iii]  And although at the time they didn’t call it a CDP, they were quacking as if it were one, and funny enough in April 2021 relaunched it as a CDP. [iv]

In 2019, in classic Salesforce fashion, they announced a not-ready-for-prime-time CDP-like product, C360, with pages of fine print.  Like a theater stage with a kitchen viewed from afar, it might have appeared fully equipped.  However, on closer inspection, some of the supposed appliances were but props with no cords to plug in, no motor to run them.

And even on re-launch in May 2021, they simply slapped existing separate products such as Tableau and Mulesoft onto the wrapping paper of the Salesforce CDP.  Further, like most CDPs (except ones that come from the web analytics space, such as Tealium and Celebrus) everything is based on creating customer segments and sharing those in less than real-time for activation instead of taking an individual personalization approach and sharing in real-time.

Thus, rip off the cartoon marketing wrappers, and look inside the box and inspect all the parts for function and fit before buying.

CDP Market Consolidation

In addition to the entrance of the above big three, Microsoft and SAP also announced CDP solutions.  Before November 2019, 18 acquisitions took place. Since November 2019, 8 more further transformed the CDP landscape:

  • IgnitionOne bought by Zeta Global – December 2019
  • AgilOne bought by Acquia – December 2019
  • Evergage bought by Salesforce – February 2020
  • Segment bought by Twilio – October 2020
  • Exponea bought by Bloomreach – January 2021
  • BlueVenn bought by Upland Software – March 2021
  • Boxever bought by Sitecore – March 2021
  • Zaius bought by Optimizely/Episerver – March 2021

What’s the takeaway?  Dust is still flying in this market.  And if you are betting on one of the 100+ vendors calling themselves a CDP to plug key gaps, especially in foundational areas such as identity & data management, consider whether their future is secure, and they’ll continue to go in the same direction, as it could impact yours.

The Calamitous Cookie Crisis – Customer Identification and Tracking

In January 2020, Google announced plans to end support for third-party cookies in Chrome in two years.  Late-breaking news is that in June 2021, Google said they will delay until the middle of 2023.  But cookiepocalypse is still coming.  With less than two years until that deadline, ad-tech companies, and ad agencies alike are scrambling to find workarounds for web behavior identification and tracking. 

Case in point – The Trade Desk and ad agency Publicis (who bought the database marketing firm Epsilon in 2019) are teaming on a digital advertising solution built around the new open-source identification scheme called Unified ID 2.0.  Initially developed by The Trade Desk, Unified ID 2.0 obfuscates a consumer’s email address, using a technical hashing technique to protect consumer privacy.[v]  

As of May 2021, The Trade Desk says it already has over 170 million profiles obtained with consent.  But long-term success depends on an even bigger pool of email addresses (e.g., more consumers opting in than opting out), and that means enough publishers adopting the standard, and obtaining consumer consent.  Since history has shown consumers will opt-in without reading terms and conditions, it may have hope, especially in places like the US and Asia, so stay tuned.  My advice – read before you click, as it’s essentially agreeing to be a target of every participating company. 

In addition, SAP and Akamai bought traditional sign-on companies Gigya and Janrain respectively, going the route of obtaining social sign-on solutions to gain access to customer identification and tracking capabilities.  And although Okta, who acquired rival Auth0 in May 2021, hasn’t called itself a CDP (yet), they are a force in the customer authentication and identity space.  

What does this have to do with CDPs?   Well many ad-tech companies, formerly calling themselves data-management platforms (DMPs) during the third-party cookie era, now claim to be CDPs.  Keep in mind, however, they built their solutions to manage third-party data and cookies and to target based on these spurious methods, and not on first-party data and known identities.  Ultimately, without a strong first-party data foundation, those DMP CDPs have a limited shelf-life and are poor investments.

CDP Selection Tips

Tip #1 – Study their specialty

Keep in mind that all vendors started with a core offering.  That tells a lot about what they’re probably good at.  When interviewing a job applicant, there’s a reason why we inspect someone’s background (work history, school they attended), as it gives insight into how they’ve honed their craft. 

No vendor (not even the big ones) will be able to supply best-of-breed capabilities to handle all stages of a journey, from the anonymous browsing steps to phases deeper in an authenticated relationship.  Nor will they be able to major in more than a handful of the dozen or so capabilities the collective CDP market covers:

  1. Data collection
  2. ETL – Extract, transform, load (including cleansing and householding)
  3. Identity stitching and management
  4. Real-time data insights
  5. Predictive analytics
  6. Recommendations and decisioning engines
  7. Journey (cross-channel) orchestration
  8. Owned channel marketing automation and e-message services
  9. Digital advertising
  10. General (business intelligence) customer data activation
  11. Internal query, reporting, dashboards, and attribution analysis

Most native CDPs came up focusing on one or more of the first 3.  And with no official CDP magic quadrants or waves by major analyst firms, many others have conveniently slapped the CDP label on themselves. So, decide where you have the biggest capability gaps and needs along the customer journey, where a data-driven solution will drive better outcomes and more value, find matches, and select accordingly.  Also, if gaps exist mainly in areas 4 – 10, look beyond the CDP market, as there are a multitude of vendors not calling themselves CDPs that major in these areas.

Tip #2 – Demand real-time response times

When considering the claim of “real-time,” (which is a critical capability to take CX to another level) look beyond single components, such as the speed of data collection, or placing data onto a customer profile record.  Instead, inspect the entire data/event -> insights -> decision journey and ask:

  • “Can that entire trip be accomplished in an SLA (Service Level Agreement) under 200 milliseconds?”
  • “Can the vendor do that at scale, for millions of customers and 1000’s of interactions per second?”

Why 200 milliseconds you ask?  Because as a consumer, do you want websites you use to be slower?   As a person responsible for the website, will you allow anything new to slow down page loads?   I bet the answer to both is no.   So if your new CDP is going to play a role in providing better real-time digital experiences, it better not take up much of the two-tenths of a second response time budget.

Tip #3 – Demand real-time insights

Look for a CDP that can supply real-time data insights, with a library of these for your industry.  This looms so largely in reaping unfair benefits from a CDP investment because not many CDPs do this, and it’s how you’ll move the needle on customer experience.  Can you do this today?  Can you find customer behavior diamonds in the deep mines of digital data, surface it, polish it, and immediately pass it to a customer decision hub?  Not many can.

For instance, detecting consumers’ heightened but fleeting interest in specific products, refining that raw data into curated signals, passing them to a decision engine in real-time, so it can trigger special and immediate actions.  Very few CDPs can do these things – in that order – fast enough.  An example: a consumer on a banking website, researching mortgages [again] in the final stage of selecting a mortgage provider. 

So, look for a CDP that can solve this problem. There aren’t many.  You’ll add something special and unique that few can do.  Celebrus is one solving this exact problem:  collecting the right behavior data, making sense of it in the form of a signal library, passing those signals to a decision authority in real-time, so it can act in the moment.

Conclusion

Big is not always better, but it’s always bigger.  And although selecting a large outfit as a CDP provider gives some assurance that the solution will be around in a few years, that doesn’t necessarily equate to the best CDP solution.  Doing business with a mega-CDP vendor rarely means faster, more seamless interfaces and deep expertise.  On the contrary, expect bigger integration costs, longer wait times, custom work, and more patience required.  And if selecting a big CDP is for “one throat to choke,” try finding that elusive throat inside a tech behemoth with 50,000 employees who have swallowed up 20 companies on the way to building their marketing stack and CDP.

Conversely, using a smaller player has its tradeoffs.  Besides the risk of being bought, or folding up, inevitably their capability focus will be esoteric.  So, carefully inspect core competencies. Look for a CDP that compliantly tracks customers, collects data in real-time, has a signal library fit for purpose, and can interoperate with a decision hub. That way, you’ll get differentiation leading to better customer experiences from your CDP investment.

[i] Adobe.com, https://business.adobe.com/products/real-time-customer-data-platform/RTCDP.html, June 2021

[ii] Oracle.com, https://www.oracle.com/cx/customer-data-platform/what-is-cdp/, June 2021

[iii] Salesforce.com, https://www.salesforce.com/news/press-releases/2019/11/19/salesforce-announces-customer-360-truth-a-single-source-of-truth-for-every-customer-across-the-worlds-1-crm-2/, November 2019

[iv] Salesforce.com, https://www.salesforce.com/news/stories/salesforce-cdp-innovations-make-customer-interactions-smarter*/, May 2021

[v] The Wall Street Journal, https://www.wsj.com/articles/publicis-groupe-signs-on-to-use-trade-desks-alternative-to-cookies-11617883217, April 2021

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Don’t fall into the “we need a CDP first” trap

Introduction

Over the last three decades, marketers and customer experience experts learned the importance of employing data in data-driven customer decision making.  With the right data, they realized, machines could assist them in running better programs.  The result was more customers receiving relevant offers, and in turn leading to improved response rates and increases in customer satisfaction and retention.

This journey, however, wasn’t short on painful and costly lessons.  Stories were common of virtually endless data warehouse projects seriously behind schedule and overbudget.  In some of the most infamous cases, $10’s of millions were spent over years, with little to show for it.  Why?  Because from the onset the goals were misguided, and in many cases the wrong people drove the project. 

What went wrong?  Simply put, project sponsors set out with the wrong sequencing of goals – trying to solve for the ultimate data repository first and putting the most important aspect, who would use it and how, on the back burner.  In other words, they set the priority on sourcing data, cleaning it, and structuring it, and put off concerns on which applications would leverage it.  Build it, they posited, and they will come.

Challenges:

Sadly today, many embarking on CDP projects are falling into this same trap: 

Select the best Customer Data Platform (CDP) first, build it to solve for nagging problems of fragmented data and cross-device identity.  Later, help customer decisioning applications get connected to it.

The problems with this approach are:

  1. Without considering first which specific outcomes are crucial to success and working back to the data needed to support those, chances are extremely high the CDP won’t have the right data.
  2. History shows it could take years to agree on the right data, amass, cleanse, stitch, and organize it into a brand-new platform.
  3. Nearly every vendor calling themselves a CDP is now also claiming to solve for enterprise customer decisioning requirements.  Yet selecting the same vendor for both means a direct dependency on this repository, where the CDP must be up and running before the business can run its first new customer engagement programs.

Twenty years ago, at Unica, we saw this exact same problem.  The business was waiting for IT to complete the never-ending data warehouse project.  Or worse, they took matters into their own hands and selected a tool like Epiphany that required all the data structured and uploaded into its marketing data model (essentially a CDP – just not called it at the time).   Sound familiar? 

Again at Unica, to tackle this problem, we designed a different solution and approach.  We called the solution UDI (Universal Data Interconnect) which allowed marketers to map to existing data sources and run campaigns leveraging that data in place. 

We advised frustrated clients to set goals such as improving promotional response rates and urged them not to wait for data warehouse projects to complete.  The advice we gave them –  focus on redesigning campaigns, use advanced analytics to improve lift, and connect only to data sources required for those redesigned campaigns.  Essentially, let the new campaign rules drive the data source requirements.  References reported running successful campaigns shortly after project inception.  In just months they touted tangible economic benefits, bolstering their case to expand rollout.

CDPs are all the rage – what should I do?

First, the fact that CDPs are “all the rage” is part of the problem.  Upon closer inspection it’s the CDP vendors generating the hype, and not the paying clients.  Oddly missing are stories of resounding project success and massive ROI, and instead infamous stories of CDP projects failing to meet goals are piling up.  In Gartner’s 2021 Cross-Functional Customer Data survey, just 14% of respondents that reported having a CDP also reported achieving a 360-degree view. [i]  What we’re witnessing is the classic Gartner technology hype cycle, with CDPs now passing peak hype, and falling into the trough of disillusionment. [ii] 

In my 2019 article, To CDP or NOT – 3 tips – then you decide, the advice was beware of the hype in a poorly defined market.  Now, in 2022, vendors are trying to differentiate in a still nebulous market.  Here are some of the CDP subcategories that have emerged since 2019 [iii]:

CDPs selected primarily by Marketing and Business buyers:

  • Smart Hubs / Hub & Spoke CDP
  • Real-Time CDP
  • Marketing Cloud CDP (e.g., Adobe AEP, Salesforce CDP)
  • Campaign & Delivery CDP

CDPs selected primarily by IT, Data, and Analytics buyers:

  • Data Integration and Management CDP (focused on data collection and identity management)
  • CDP toolkits (used by IT to build a CDP)
  • Customer Analytics & Insights CDP

Certainly, the right answer isn’t to buy multiple CDPs.  Yet that is exactly what’s happening.  And for larger enterprises, some are buying as many as three, simply proving poor alignment between the business and IT. [iv]  Having lived through those days, be assured, the result is not alignment on outcomes, rapid access to the right data, and improved customer experience.  

At the same time, the right answer isn’t to let the business (or IT) solely determine the selection.   Although the business must have primary responsibility and control, it also must tightly collaborate with IT where both parties understand their roles and stick to them.  Though unfortunately not common, brands that get this right, and take inventory of what data & systems they have and what roles each party should play, report better success and ROI.  As such, follow these rules:

Do –

  1. Establish a strong partnership between the business & IT, align on use cases, outcomes, and how to measure success. Take inventory of existing capabilities and chart a roadmap together.
  2. Work back from the highest value use cases and desired outcomes and map out the data needed to support them. 
  3. Make it a requirement to be able to iteratively add to the data repository, as new programs might demand new data sources.  It won’t be instantaneous (think in terms of quarterly releases for production data source changes).
  4. Insist that the decisioning and execution capabilities and the CDP solution be evaluated on their own merits, and if in the end different vendors provide what’s best and can be integrated without herculean effort, select accordingly. Demand references that attest to their enterprise decisioning operational use, scale, and effectiveness.
  5. If evaluating (or already embarking on) a CDP project, simultaneously consider a re-vamped RTIM project. [v]  If a CDP project is ongoing, let the RTIM’s data requirements feed into the CDP’s, not the reverse.  And don’t wait for the CDP project to complete.  Select an RTIM vendor that can map quickly to existing data and can provide tangible proof of fast time to value and ROI.

Don’t –

  1. Accept at face value that the CDP’s RTIM engine will be “good enough.”  Rather, insist the vendor demonstrates unified inbound and outbound decisioning, real-time re-decisioning at scale, advanced analytics features, and capabilities to incorporate contextual streaming data.    
  2. Don’t accept that having a single vendor will outweigh the benefits of having a best-of-breed real-time interaction management (RTIM) engine.
  3. Wait until teams agree on all the right data.  That day won’t come.  Instead, if a CDP has been selected, demand an agile approach for how to enhance the CDP over time.  Ask the vendor of choice for RTIM to provide plans for running before and after the CDP project is done.
  4. Make the mistake thinking that a CDP Smart Hub can deliver scalable and maintainable RTIM decisioning.  None can.  Most rely on traditional segmentation and scripted / deterministic rule-based journey orchestration – all fraught with old problems of static segment definitions, deterministic offer assignment, and hard to maintain eligibility and engagement rules.  A modern RTIM engine with a 1-1 personalization approach solves for all these traditional limitations.

Conclusion

A CDP project, aimed at rationalizing customer data, improving identification, providing segmentation, and streamlining access seems a worthy cause.   Yet history teaches us that chasing a complete view of every single customer across all their devices and interaction points is an elusive goal.  What’s more likely is a CDP project turns into a giant hole, sucking time and resources.  And its expected benefits, like the light bent back on itself by a black hole’s vortex, may never emerge.

Instead, if a CDP project is ongoing, set it on careful rails, and manage scope.  Meanwhile, evaluate RTIM capabilities and embark in parallel to address those shortcomings and gaps.   Research ROI evidence from CDP and RTIM projects and compare.  If resources to fund both projects compete, pit them against each other based on business cases and prioritize investments accordingly.  And remember the lesson of sunk costs, and don’t be afraid to adjust project plans and budgets already in flight.  Many who have placed bets on RTIM cite quick successes that propel massive long-term returns — some with 10x ROI and more than $500 million in incremental revenue. [vi]  Don’t make the mistake of waiting and suffering huge opportunity costs.

[i] Market Guide for Customer Data Platforms, Gartner, March 2022

[ii] Hype Cycle for Digital Marketing, Gartner, July 2021

[iii] Customer Data Platform Industry Update, CDP Institute, January 2022

[iv] Ometria, https://ometria.com/blog/5-reasons-standalone-cdp-might-not-right-solution-retailers, January 2022

[v] What is RTIM, https://www.teradata.com/Glossary/What-is-RTIM, 2022

[vi] Forrester RTIM Wave, https://www.pega.com/forrester-rtim-2022, Q2 2022

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The Hyper-Personalization Paradox: being relevant without crossing the CREEPY LINE

Brands are using AI to drive hyper-personalization, but can it also help them avoid being hyper creepy?

hyper-personalization

Source: https://www.adclarity.com/2015/04/digital-marketing-2015-hyper-personalization-display-ads/

Apparently, I have 8 seconds to grab your attention, so here goes.  What if I personalized every aspect of this blog for you?  That is, I knew so much about you – your reading behavior, the writing style you prefer, subjects you love – took all of it into account, and assembled these words and pictures just for you?  Would you find that creepy or cool?

At our conference in Las Vegas recently, I was a guest on Sam Charrington’s, podcast series “This Week In Machine Learning and AI.”  In that episode, we discussed a similar hyper-personalization scenario, where an automotive company used intimate knowledge about a consumer and her connected car to custom-tailor each marketing and service treatment[i].  And half-way through (at 23:07), Sam observed that although “consumers appreciate personalized experiences,” it can go too far and “sometimes come across as creepy.”

And suddenly, we both realized something.  Customer experience experts haven’t used AI to govern this.  In other words, CX pros personalize without recognizing if their personalization levels are approaching creepiness.

Which led to this question: can creepiness be quantified?  And if so, with that knowledge, could a company effectively use it?  With the right tooling, could they safely test and simulate how far personalization should go, carefully delivering each customer a tailored experience with the right level of relevance and value, without crossing into their creepy space?  Simply put, hyper-personalizing without being hyper-personal — the personalization paradox.

You’re marketing is creeping me out

Creepy land is that forbidden zone where consumers call out businesses for using personal data and revealing insights that are a bit too private.  And though consumers increasingly want personalized experiences (according to a recent Epsilon study[ii], 90 percent of consumers find it appealing), ironically, they will happily make examples of brands that invade their personal space.

No brand wants a creepy reputation as it implies:

  1. Stalking, snooping, or spying; collecting personal data and invading privacy
  2. Revealing something private, no matter how valuable the insight
  3. Not having customers’ best interests in mind
  4. Ill-intent, even when there isn’t intention to do harm

With big data galore, a culture of a data sharing, and pressure to mass personalize to remain competitive, you need ways to safely and systematically explore the creepy line’s location without ever crossing it.  Understanding what customers expect and why they love a product (or don’t) is crucial to great personalization.  Avoiding a creepy moniker means effectively steering clear of areas that are, frankly, none of your business.  And if the customer says it’s none of your business, it’s none of your business.

Today, the digital world abounds with copious quantities of demographic, psychographic, and behavioral data.  There’s a sea of it, because for decades companies have wired up clients and monitored them like lab rats.  And with more IoT tech and data coming every day, firms increasingly misuse it, giving customers more reasons to demand privacy.  The problem is the definition of what’s private and sensitive can be different for each person.  Hence the dilemma: under personalize and risk being labeled clueless, not cool, and worse miss out on revenue; over personalize and risk breaking trust and doing irreparable damage to your reputation.

Sorry we’re creepy. We apologize for any inconvenience 

Customer engagement professionals need new and scalable ways to survey buyers, collect preferences and permissions, sense their intent and moments of need, and personalize appropriately.  So, they need ways to test where that creepy boundary is.  That line is fluid and ever shifting and finding the right level of personalized insights and recommendations without crossing into risky territory is never without some uncertainty.

Where that line lurks changes with time because initially customers may be leery of something, then later adapt to it.  It also changes because privacy legislation changes, individual consumers have distinct levels of sensitivity, and varying levels of awareness. It can even differ by geography.  For instance, a 2016 study of 2000 consumers in Europe found that 75 percent were uncomfortable with facial recognition software used to target them with personalized offers (consumers in the US were much less sensitive)[iii]

Data-driven marketers have evolved their practices (Figure 1) using data to acquire more customer knowledge which in turn powers more personalization.  Over time, more marketers have evolved their practices, from the general advertising Mad Men approaches of the 1960’s to the super-personalized, AI-Powered approaches possible today.  It also highlights how that pushes them closer to the creepy space.

hyper-personalization evolution

Figure 1: Evolution of Data-Driven Marketing

Here’s the bottom line: if a given customers perception is it’s creepy, it’s creepy.  And depending on who slaps that label on, and whether their rights that have been violated, firms may face legal battles, fines, and reputation damage leading to significant commercial impact.  For instance, potential fines for GDPR privacy law violators can reach 4 percent of a firm’s revenue (up to a maximum of €20 million).

And none of that is music to a businessperson’s ear.

Creeping toward creepy

In 2014, Pinterest managed to spam a major segment of customers when they sent emails to unengaged women congratulating them on their upcoming weddings.  And Shutterfly made an even bigger spam faux pas that same year, congratulating women on the birth of babies they didn’t have.

In Figure 1, these events fall into the SPAM circle because marketers placed people into the wrong macro segments, and the resulting emails were both irrelevant and hilariously erroneous.   Clumsy customer experiences indeed, but not creepy-smart marketing.

Here are some other examples of Mad Men SPAM marketing:

  • You market wedding offers after a wedding – low sensitivity
  • You market wedding offers after a cancelled wedding – high sensitivity

On the other hand, the risk of being labeled a creepy marketer increases when knowledge of customers goes up, insights increase, yet marketers fail to understand an individual’s sensitivity to certain marketing actions.

For each marketing treatment, you need to determine if it will be creepy to everyone or only some:

creepy meter

Figure 2a:  Creepy Meter detecting creepy treatments

If it’s clearly creepy to everyone, during the pre-market approval process you should reject it.  But, if its potentially cool to some, and creepy to others, then provided you can discriminate at runtime using eligibility rules, you can approve its use for those who will find it cool.

To do this, get a readout on consumers’ sensitivity to hyper-personalization.  Build a model that learns this, and use this score to select, by individual, the levels of personalization they’re eligible to receive.

creepy index

Figure 2b:  The Creepy Sensitivity Index readout on each consumer

Here are a few examples of events, corresponding covert marketing approaches, and creepy readings:

 Event Covert Marketing (but not illegal) Creepy Meter Approve?
Hospital admittance / serious health issues detected Mortuary makes discount offers Extremely creepy Reject
Conversation recorded (without clear permission to use for marketing) Ads for products related to keywords in the conversation (e.g., pet toy video recently, which illustrates the point yet is likely a hoax) Very creepy Reject
Facial recognition or location detection Upon a patron entering a branch or store, their profile & preferences are relayed to a salesperson Borderline creepy Conditional
Consumer traveling; recent activity and calendar scanned Push notifications offering travel recommendations based on triangulating travel intent and destination Borderline creepy Conditional
Consumer browsing a web page with product offers Website background, images, language, offers, and other page fragments hyper-personalized Borderline creepy Conditional

Table 1: Examples of potentially creepy marketing

Leading-edge 1:1 marketers are constantly listening for keywords, tracking interaction device, time & location, codifying behavior, sensing mood, recording preferences, and using that knowledge to hyper-personalize with content variations in the millions.  The risk, however, is meandering into that forbidden creepy zone (even if it’s legal), so discerning this by customer by treatment is vital.

Suggestions

As you move into deeper levels of hyper-personalization, do so deliberately and methodically, fully grasping the implications before rolling out.  Consider taking this approach:

  • Collect only data that matters to your ability to personalize specific experiences – that your customer will value. For example, if you sell insurance, you don’t need to understand pet preferences unless you’re selling pet insurance.
  • Start with simple / minimal risk personalization strategies. These should easily pass the creepy test.  For instance, if you can tune you web experience to shopper color preferences, do it.  No one will find that creepy.
  • Gradually apply regional and demographic personalization strategies.
  • Use AI to crawl your products and content to extract taxonomies, attributes, cross-classifications, and descriptions. This will help better match customer intent and preferences to products that will match needs.
  • Use AI to match the right products to clients (making relevant recommendations) and doing so in a personalized way that enhances their experience
  • Use sampling to test hyper-personalization treatments, selecting a wide variety of customers.  Essentially, you get a stratified sample of creepiness raters.
  • In general, avoid even borderline covert marketing unless you have a firm handle on any backlash that might result if customers discover it. In a recent survey, most consumers (81%) think firms are obligated to disclose they’re using AI – and how they’re using it.[iv]
  • Be sensitive to consumers’ preferences for public recognition.  Some might love it if you great them by first name and show appreciation for their loyalty in public.  A few, however, may be mortified.

Hyper-personalization requires great data, great technology, and great sensitivity.  With GDPR now in effect, most businesses are proactively disclosing their data collection practices and privacy policies.  As consumers, we’re consenting to and accepting new privacy policies more than ever before, and in some cases, we’re even reading and understanding them.  Less clear, however, is exactly how that data is used, combined with other data, and when it might show up as an insight, recommendation, or hyper-personalization – and again, which of us might be freaked out by this personalization.

AI is driving personalization to new levels.  There’s no stopping that.  It automatically figures out what works and what doesn’t.  Techniques, such as Bayesian algorithms, quickly learn which offers work, when, and in which channels.  Others, like collaborative filtering, find which products pair best, that in turn drives cross-sell and bundling strategies.  Design of experiments and monitoring devices measure the impact and enable fine tuning.

What’s missing, however, are tools to sense consumers’ sensitivity to personalization, so overt practices are optimized with the right people, and so covert methods are prevented from ever reaching production, or if they are approved for use, are carefully applied.

The study shown in Figure 3 provided some proof that overt personalization pays off.   Yet the very definition of overt blurs as AI improves, content becomes hyper-conditional, and levels of personalization get more complex.  Thus, you’ll need more sophisticated ways to gauge levels of personalization relative to creepiness, and the sensitivity levels of different people.

personalization

Figure 3: Overt vs covert personalization performance[v]

Conclusion

Great marketers push beyond perceived barriers by understanding customers, knowing products, and then elegantly combining creativity and technology to provide valuable recommendations and experiences to customers.  Ironically, when done right in the eyes of the receiving consumer, they don’t appear to be selling anything; instead simply providing a service.

With website personalization, one-to-one content, natural language generation, image recognition, and countless other AI tools, businesses inexorably march toward hyper-personalization.  Make sure you manage it, so you’re always cool and never creepy.

Endnotes:

[i] https://www1.pega.com/insights/resources/pegaworld-2018-pegas-ai-innovation-lab-sneak-peek-and-your-vote-counts-video, June 2018

[ii] http://pressroom.epsilon.com/new-epsilon-research-indicates-80-of-consumers-are-more-likely-to-make-a-purchase-when-brands-offer-personalized-experiences/, January 2018

[iii] https://www.forbes.com/sites/fionabriggs/2016/07/04/fingerprint-scanning-is-cool-but-facial-recognition-creepy-new-richrelevance-survey-shows/2/#493b953f3d68, July 2016

[iv] https://www.richrelevance.com/blog/2018/06/20/creepy-cool-2018-richrelevance-study-finds-80-consumers-demand-artificial-intelligence-ai-transparency/, June 2018

[v] https://www.sciencedirect.com/science/article/pii/S0022435914000669#abs0005, March 2015

 

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My 2019 Martech articles

To CDP or NOT – 3 tips – then you decide

4 Golden Rules for Knowing and Honoring Thy Customer

Dear CMO: Sorry, but we need a CVO (Customer Value Officer)

A 6 pack of tips when replacing creaky MRM software

The Final 4: MarTech Platforms and Ecosystems

Will AI in digital marketing lead to marketer obsolescence?

One-to-One Marketing: 20 years later, are we there yet?

Marketing’s Strangelove: How I Learned to Stop Worrying & Love Service

5 predictions for CRM’s AI applications in 2019

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4 well-intended Marketing Automation BAD HABITS to break

Let’s face it.  No one sets out to botch something up or fall short of reaching a goal.  When marketing automation was in its infancy, and pioneers like Don Peppers, Martha Rogers, Tom Siebel, and Paul Greenberg envisioned marketing and CRM systems in the mid 1990’s, they set the right vision, believing great customer relationships could be initiated, fostered, and brought to scale with the right data and technology.  Essentially, their collective creed was:

  • Focus on the individual customer (e.g., be one-to-one and customer centric).
  • Manage the relationship by understanding customers’ buying cycles, needs, and behaviors across the marketing, sales, and service functions.
  • Use that knowledge to custom-tailor and personalize the experience.
  • Use technology to deal with the scale required by larger businesses.

Thirty years later, sadly, this vision still seems out of reach, or at best, only partially realized.  So why is that?  What’s held back the realization of the vision?  What are we still doing wrong?

Here are four unhealthy habits of nearly every marketer (so the good news is you’re not alone).  Fix these, and you’ll get a distinct advantage, and get closer to marketing optimization and CRM nirvana.

 

Bad habit #1 – Focusing on customer segments and not individuals

Customers are individuals.  Each has unique characteristics, nuances, and contextual needs that define who they really are.  And though we’re awash in a wealth of unique behavior data, it’s a common mistake to continue trekking on the beaten path, making decisions based on segment characteristics rather than individual ones.  For years, we’ve slotted customers into segments because we had no other choice, oversimplifying who they really are.

1 to 1 marketing automation

It’s understandable in the initial stages of relationship management that businesses make broad customer classifications such as:

  • Returning visitors
  • Mobile visitors by geography and device type
  • Registered users by gender and age (leading to segments like Millennials, Gen Zs, and Gen Alphas)
  • Non-responders to an email campaign

Yet after these customers repeatedly interact and transact, clearly stating their implicit and explicit preferences, continually handing over lifestyle and contextual data, there’s no excuse for still making generalized, segment-based decisions.  We’re spending millions collecting, storing, and refreshing specific behaviors and preferences, so we should use this data to drive individualized decisions and to customize treatments.

In a recent paper titled “Crossing the chasm: From campaigns to always-on marketing,” [i] Rob Walker and Matt Nolan contend that “building audiences using segmentation is a process that introduces severe challenges such as compromised relevance, unscaled labor, and collisions and conflicts.”  They go on to suggest using a next-best-action approach, describing it as one that “targets individual customers, rather than segments – leveraging their unique needs, preferences, and context.”

 

Bad habit #2 – Focusing on selling products instead of customers’ needs

Sounds crazy, right?   How else will we make money if we don’t sell products?

Still one of the cardinal sins holding back modern marketers is focusing strategy and tactics solely on selling products.  By doing this, we’re exasperating two problems:

  1. Product owners, incented to relentlessly push their products, bombard consumers with ill-conceived campaigns containing messages and offers that conflict, overlap, or worse, aren’t even applicable. When viewed through a customer’s lens, these promotions have little to do with their actual needs.  As such, marketers often completely miss the relevance mark.
  2. Even when a product fits, companies fail to provide well-timed promotions, convenient services, and a context-sensitive experience. Oblivious to the individual’s situation, they make company-focused timing and interaction decisions, such as blindly promoting a product simply because ad budget might otherwise expire, or failing to promote crucial services in conjunction with the product..  Consequently, tactics are entirely out-of-synch with the customer’s buying cycle and experience expectations.

Together, these problems compound customers’ negative brand perceptions.  Rather than providing a stellar buying service, well-intentioned marketers inadvertently (and increasingly) overwhelm, turn off, and tune out consumers.  Essentially blind to journey requirements, marketers miscalculate customers’ value calculus, timing preferences, and the overall interaction experience they need and expect.

In study after study (year after year), consumers and brands acknowledge these issues, both resoundingly stating their desire for solutions.  For example, in 2012 the Corporate Executive Board (now part of Gartner) surveyed more than 7000 consumers and 200 CMOs, finding that what consumers want from marketers is relevance and “simply, simplicity.”[ii]  That was 2012.  It’s 2018, and not much has changed.

If corporations keep strategy oriented on selling products, customer relationships will remain transactional and experiences sub-optimal for many more years.  Maybe we’ve forgotten what the R in CRM stands for.  It was put there to remind us that what matters most is long-term relationship building.  Our quest should be to unravel the mystery of a customer’s ever-changing needs, their journeys, and what drives their loyalty.  Our job is to use that knowledge to create custom-tailored experiences.

 

Bad habit #3 – Building channel-based versus coordinated intelligence

Shortly after September 11, 2001, the US government came to a stark realization that its various intelligence agencies were massively disjointed and compartmentalized.  This hadn’t happened overnight.  It was years in the making, and although for decades ample resources were poured into each agency, no one agency was responsible for coordination.  Attempting to solve this problem, the government established the Department of Homeland Security.

channel intelligenceIn a similar vein, some firms have built up marketing automation and CRM intelligence in silos for over 30 years.  In each channel (e.g., email, contact centers, web), they’ve poured substantial resources into projects aimed at beefing up customer intelligence.  Each channel amassing data, rules, and intelligence, but no one designated as the coordinator, and information rarely shared.  Subsequently, as more channels emerged, the problem grew larger. Today, many companies have 15 or more channels to manage, and no coordinating function.

To provide wonderful experience, brands need a function responsible for coordinated customer analytics, intelligence, and decision making, such as depicted in Figure 1.  Its role is straightforward:

  1. Collect interaction intelligence and contextual data from each touchpoint, and connect it directly to a system that can leverage that information immediately.
  2. Be brain-like, tracking behavior patterns in real-time, sensing needs, and using analytics to dynamically calculate value, comprehend preferences, and predict intent.
  3. Play the arbitrator, weighing an individual’s needs against corporate initiatives, policy, risk tolerance, budgets, and economic goals. Make instant and well-balanced decisions, track the results, and learn from each decision.

 

engagement hub

Figure 1: Engagement hub provides coordinated omnichannel intelligence

Think of this, not as another physical department, but instead as a virtual customer-centric hub. Designed from the ground up to be connected to all customer touchpoints, it’s journey oriented versus channel centric.  Cognizant of what transpired, why, and what’s best to do next, its embedded strategies and rules act as a real-time arbitration committee – making data-driven decisions in milliseconds versus months.

This hub is also more than a customer data platform.  It’s an end-to-end engagement hub responsible for not only gathering and coordinating intelligence, but also gleaning real-time insights and taking action.  To deliver on that, it manages key data, customer analytics, corporate rules and processes, and channel interfaces.  In a calculated and auditable fashion, it makes recommendations, delivers them to touchpoints (the channel apps fine tune the experience), and it learns from a systemic set of impressions and responses.

 

Bad habit #4 – Worrying primarily about marketing automation and technology, instead of experience

Automation, and the technology that enables it, efficiently repeats tasks.  That’s great, if you computerize the right tasks that deliver the right experience.  Look at it this way:  spammers are very effective at marketing automation.

Above all, to achieve lasting loyalty and build value, avoid the temptation to recklessly make existing marketing processes more efficient.  Granted, some existing tactics may work, yet chances are many need to be revamped (or ditched entirely), and recognizing that requires reframing priorities.  Preferably, focus on customer journeys, and ask if marketing tactics contribute to a better experience.  Consider journeys such as:

 

  • Prospects searching for products to discover and learn more
  • Customers seeking out trials to test those products
  • Customers embarking on a buying or upgrade process
  • Customers doing research on price, available incentives, and financing options
  • People filling out an application, making a booking, or redeeming rewards
  • Consumers getting stuck, struggling, or in need of assistance
  • Clients reaching milestones, entering new life stages, or affected by key events

No organization can serve its customers without supporting people.  To illustrate, assume your kiosk has a reasonable self-service experience, but then something goes wrong.  The technology hiccups, and a customer begins agitating.  Without back-up mechanisms, this situation can quickly turn disastrous.  To avoid it, you need reasonable levels of redundancy, well-tested cut-over processes, and intelligent detectors that gauge the need for human intervention, and then bring the right human into the loop.

Brands that thoughtfully consider these scenarios, elegantly weaving together marketing automation, people, and processes, will deliver better customer experience.

But how can you be sure you’re improving experience?  In short, hyper-focus on one journey at a time, pick metrics to measure each, and correspondingly measure overall satisfaction.  Once more, here’s where many firms trip up.  Instead of measuring whether the customer is fully satisfied with, say, the onboarding journey, they only measure certain tactics, like whether a welcome email got sufficient opens and clicks.

Conclusion

Be honest. We all have some bad habits that admittedly we should give up for our own good.  But breaking old habits isn’t easy.  And like any habits, we’re comfortable with our marketing automation traditions because the outcomes are predictable.  Nonetheless, just because they’re predictable, doesn’t mean they’re best for our customers.

When we force-fit customers into segments, push products on them that we want to sell, confuse them with conflicting and poorly orchestrated channel messages, and hyper-focus on our efficiency (versus their experience), the results will be predictable alright – in other words, we’ll get our anemic 0.5% response rates and slow growth.

If you think, however, you can do better, then take a chance.  Collect as much individualized data as you can, use it to personalize customers’ experiences, coordinate decisions with one principle engagement hub, and as Steve Jobs said, “…start with the customer experience and work backwards to the technology.”

[i] Crossing the chasm: From campaigns to always-on marketing, https://www1.pega.com/insights/resources/crossing-chasm-campaigns-always-marketing , December 2017

[ii] CEB Press Release, https://news.cebglobal.com/press-releases, May 2012

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8 AI trends for Martech

In this 8th and final short video in my Machine Marketing Series, I give my views on the “The HOTTEST AI trends for Martech” to keep your eyes on in 2018.

 

I cover eight key AI trends to keep a watch on:

  • AI data and processing speed
  • Natural language processing (NLP)
  • Image recognition
  • Natural language generation (NLG)
  • Automation and process management
  • Transparent / Explainable AI
  • One AI brain
  • AI organizational dynamics 
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8 MACHINE LEARNING for marketing areas to watch in 2018

If you’re like the unbreakable Kimmy Schmidt and got stuck in a bomb shelter in 2017, it may be both a blessing and a curse that you missed the machine learning for marketing media frenzy.  Machine learning showed up everywhere, rivaling electricity’s systemic emergence a century ago, allegedly injecting sage-like wisdom into everything from sales forecasting tools to email subject lines generators.

machine learning for marketing trends

But buildup and hype aside, real progress was made in using machine learning for marketing purposes, infiltrating impactful areas as unprecedented investments poured in.  More resources supporting great minds pushed forward innovation in areas like image recognition, voice technologies, and natural language generation (NLG).  And savvy brands that mindfully wired these into marketing applications boosted performance, in some cases realizing 400 percent ROI.  Here are eight areas worth watching in 2018 that saw significant advancement and are well-poised to advance further.

 

Big data and a need for speed

Like real estate’s mantra of location, location, location…. machine learning’s very foundation and success are predicated on its thirst for big data and its need for scaled-out, lighting-fast processing speed.

But for data lovers, just as the internet giveth, during its unabashed wild-west data rush era, privacy laws spurred on by libertarian outcries soon may taketh it back.  So, keep an eye on data privacy regulations, such as GDPR (which takes effect in the European Union in May 2018), as they could seriously impact future data availability.

Regulatory environments notwithstanding, with abundant data stockpiles and processing speeds continuing an inexorable march forward (vis-a-vis faster GPUs and cloud computing), expect more advances.  For example, firms will latch onto progressive profiling and incremental data hygiene methods to refine first-party data, as emphasis shifts away from second and third-party data sources subject to stricter privacy regulations.

Capital One did just this in a routine email sent in late 2017, when they requested members update annual income data on file (previously obtained by appending from a third-party source), suggesting that if customers cooperated, higher lines of credit would be their reward.

2018 will see more of this.  Organizations will harvest their big data crops, sifting off customer behavior insights aimed at deepening relationships and selling more products faster using less resources.  Anticipate more investment in customer data platform, compiling, virtualization, and rationalization initiatives, with more computing horsepower and human capital helping the harvesting efforts in 2018.

 

Marketers!  You need bionic ears & AI voices

As humans, we’re obsessed with creating and perfecting tools that overcome our limitations, take our skills to new levels, and make our lives better.   And last year marked the point that AI devices such as natural language processing, text analytics, and language generators stormed the commercial scene and provided marketers with enhanced listening and speaking abilities.

Listening means understanding not just hearing.   Enterprise marketing experts were graced with technology that can listen and understand millions of customer inputs simultaneously across a plethora of channels.   Call scripts, reviews, complaints, social posts, and a host of other forms of feedback can be ingested, concept labeled, checked for sentiment, and gleaned for intent.  Look for more applications and advances that propel the viability of using tech to listen and understand the voice of the customer at scale.

CRM AI - Voice recognition

Source: http://www.scmp.com/news/hong-kong/economy/article/2080503/hsbc-launch-voice-recognition-hong-kong-phone-banking

Although Siri, Alexa, Amelia, Cortana, and other AI assistants weren’t born in 2017, they arguably came of age, infiltrating our homes, and entering the workplace.  If you didn’t catch it, Amazon announced Alexa for business at its re:Invent conference in November.  Machine voices will continue to spread to business places like conference rooms, service channels, products, and kiosks.  And companies (such as HSBC, Citi, and Barclays) found voice signatures another reliable biometric authentication tool to streamline digital transactions.

In 2018 machine learning may not replace you, but using it to handle routine tasks, listen to and converse with customers, and accept it as part of your marketing, service, and sales team will be essential to your survival, as you’re asked to up your productivity and customer experience enhancing game.

 

Put machine learning’s eyes on customer data, journeys, and marketing content

Discovering, understanding, and learning from customer journeys requires mechanisms to observe and quickly answer question such as:

  • Which customers are eligible for offers, got them, and responded
  • Where do customers struggle, pause, or get stuck in their journeys
  • What sequence of offers and channels lead to conversion (attribution)
  • When do certain customers show up on the marketing radar; and when do some drop off and why

Marketing specialists started using journey analytics to piece together the customer behavior puzzle, and the tech got better at going beyond business intelligence guesswork to prescriptive AI.  More AI vendors bubbled up offering solutions that don’t just sum and sort data, but provide an analysis layer peppered with NLG narratives (such as Narrative Sciences and Arria).  Others majored on providing better path-to-purchase journey visualizations, like Clickfox and Pointillist (although its arguable whether these are really AI tools).

And some focused efforts at bringing image recognition to real machine learning for marketing use. Deep learning and image recognition applications went far beyond surfacing that labradoodles and fried chicken appear related.   AI image processing proved its mettle for filtering and categorizing marketing and sales content, helping marketers better understand customers’ content needs and serve them appropriate and relevant content and offers.  Brands began expediting and personalizing services using the ubiquitous smartphone and AI’s ability to pinpoint products and people in pictures and video.  For instance, Aurasma launched an app that democratizes adding augmented reality to a consumer experience by simply triggering a video or animation overlaid on a smartphone screen based on recognizing a pre-defined image.

 

“Hey AI!  Create me some emotionally compelling content”

Marketing pros earn their pay by crafting compelling content using words and visuals to express value and elicit responses.  They dance their evocative content lures in front of consumers knowing those customers will strike if needs are met and emotions satisfied.  But up until just recently, most of these assets were home spun.

Yet last year, avant-garde marketers began applying AI to content generation, realizing that to compete in the new world (where content must be both mass produced and highly personalized), old tools must give way to new ones.

CRM AI - Natural language generation (NLG)

Source: https://blog.7mileadvisors.com/natural-language-generation-the-game-changer-for-the-21st-century-16b5a7ed3336

And firms like Persado began facilitating the march toward marketing’s creative nirvana, using NLG, emotional science, and machine learning to optimize (down to the preferences of an individual) the attractiveness of marketing offers by altering language, font, color, position, and other creative formatting.  Results are not just encouraging, they’re somewhat staggering:  click-through-rates (CTR) increased by 195 percent; conversion increased by 147 percent.

In one case using this technology, Amex Rewards generated 393,000 versions of engineered copy for its banner ads aimed at getting a customer to burn down their rewards points.  The winner drove an 8.6% conversion rate, thumping the control’s 3.5% rate.

 

Self-driving marketing – Your AI digital agency

Practitioners continue to debate whether machine learning data prep, analytics, and marketing in general can be fully automated (particularly at the enterprise level), but nonetheless, the tools keep coming.

To this end, an interesting arrival on the scene was a vendor called Frank.ai, albeit clearly for down-market marketers.  It’s literally 8 steps to setup and run a multi-channel campaign:

  1. Enter name and dates for campaign
  2. Select audience by city, interests (mix of music, pop culture, shopping, sports, etc..) or look-a-like targeting; age (typical bands); gender; language
  3. Decide on display ad on desktop or mobile or both
  4. Specify budget (e.g., $1000)
  5. Upload display ad creative image
  6. Add social media promotional ad (if desired)
  7. Add URL for click through (analytics tracking automatically setup in Google Analytics)
  8. Enter payment method (credit card or PayPal)

Simple and unsophisticated?  Check.  Will this kind of tech put further pressure on enterprise vendors to make their tools easier to use?  Check.

 

Explainable machine learning for marketing

As machines crunch data, score customers, make predictions, and automate marketing, being able to explain to humans what’s going on and why is becoming more important.

Some models are very opaque, and simply can’t explain themselves.  Given this, firms will need AI controls in place (such as offered by Pega) to prevent opaque models from being deployed in certain situations. Others are more transparent, easier to tease apart, and safer to unleash.  Research and applications are stepping up in this area, so stay tuned, especially as more regulations emerge such as GDPR, that dictate data rights and demand algorithmic transparency.

 

Building one machine learning for marketing brain

Like opinions, everyone seemed to have an machine learning software brain to peddle in 2017 including:

  • Watson from IBM
  • Einstein from SFDC
  • Sensei from Adobe
  • DaVinci from SAP
  • Magellan from OpenText
  • Always-on Customer Brain from Pega

What was less clear, however, was if each had one coherent well-integrated brain – or instead a multitude of disparate intelligence modules from the various acquisitions.  In the case of SFDC, for example, between 2012 and 2016 they acquired 21 companies, of which at least nine had some form of machine learning for marketing tech.

Stay tuned to AI developments from these and other leading marketing technology vendors, and pay close attention to whether they demonstrate real intelligence integration in the solutions they sell.

 

Machine learning for marketing organizational dynamics

Accomplished scientists and artists have rarely been cut from the same cloth.  In 2017, Walter Isaacson released his long-awaited masterpiece, the biography of Leonardo da Vinci, adding it to his corpus of history’s best examples of exceptions to this rule (Ben Franklin and Albert Einstein being other similar biographies he’s written).

So rather than wait for enough Leonardo types to come along, organizations would be wise to work toward making connections across machine learning and creative disciplines, which will be key to maximizing their capacity to innovate.

Along with attracting, merging, and retaining the right talent, brands must also acquire the right machine learning technology, but even more important is having a concerted AI strategy closely coupled with business objectives and marketing improvement goals.   It’s imperative to work from well-defined use cases and clearly articulated outcome definitions backward to the technological and data solutions necessary to support them.   Further, firms must use nimble organizational structures with small teams made up of artists and scientists; IT and the business; re-aligning resources into small digital factory teams that are wed to agile methodologies and collaborative approaches.

2018 and beyond

In all, 2017 was a banner year for machine learning for marketing, in terms of both hype and legitimate commercial progress.  Keep track of these eight areas, and you’ll be following the most interesting and promising leading-edge AI technologies and trends that will prove paramount to success in improving and automating marketing and customer experience.

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Customers Are INDIVIDUALS Not Averages | How RTIM Treats Them Special

Real-Time Interaction Management (RTIM) delivers personalized experiences to people.

Earlier this year, I signed up for a points program with a large hotel chain, and somehow my last and first name were reversed in the enrollment process.  The next day I noticed the welcome email message started with, “Jeffs, we’re so happy you joined the fam.”  Figuring it was my botch I went online and fixed it in my profile – problem solved – or so I thought.

awesome not averageApparently the erroneous data instantly had spread, like a venomous bite, and propagated to other databases.  My feeble attempt to suck it from the source was too late and didn’t work.  Still, nearly a year later, I still get messages starting with, “Hello, Jeffs” which rather than setting an intimate tone for the oncoming interaction, sets a grating one.  I may still read on, yet I’ve been reminded upfront I’m essentially a bunch of bytes to the interactor on the other end.

I get it – mistakes happen; systems are stitched together, and people (and the systems they use) are under enormous pressures to share data, scale, and automate.  Nonetheless, when firms chose to operate this way – neglecting to fix little things, they’re failing to measure the impact of the most fundamental flaws that often make or break an entire customer relationship.

Traditional Marketing Technology (Martech) vendors espouse solutions allegedly providing personalized communications.  And when their clients deploy these systems, they assume they’ll develop meaningful relationships with individual customers but, the fact is, most won’t.  Customers still routinely report broken processes (like my example), one-size fits all treatments, non-individualized experiences, and very few (only 27 percent) think AI will help.[i]  So, if you’ve been entrusted with helping achieve loyalty-building relationships, that’s more than a little discouraging, since it’s not for lack of will, good intentions, invested time, or resources.

As consumers, we browse, research, shop, and purchase constantly – sharing our information freely (sometimes unbeknownst to us).  We surrender our identity, intentions, preferences, history, location, and so on – often repeatedly, yet we see little in return in terms of well-tailored products, services, and experiences.  And this goes beyond the obvious, such as in self-service experiences (where our expectations for personalization are low), into human-assisted channels where our expectations are higher, but paradoxically we often encounter robotic-like agents.

Consider how brands place customers into huge buckets that dictate treatments:

  • Most loyalty programs have about four tiers. If a program has 10 million members, that’s about 2.5 million members per tier.

 

  • When a data scientist builds a decile-based RFM model (RFM stands for an algorithm that scores based on recent transactions, frequency of them, and their monetary values), that’s 10 segments, and again about 1 million customers per segment.

 

  • And even when zip code level data is used, that’s still about 8,000 people to a segment – and let’s face it, as much as you love your neighbors, you know how different you are from them.

 

Rarely do we enjoy being stereotyped.  When we’re assigned to a troupe, and approve of it, it’s usually because we made a conscious choice.  We find more differences than similarities when we are forced into artificial groupings, and we get rightfully grumpy with being pigeonholed.  Conversely, we rave when companies celebrate our uniqueness, and we love to tell these stories.

 

RTIM – Your AI ROI Machine

AI, arguably the most overused and abused word of the year, particularly among Martech vendors, does have in its midst the underlying technology to begin to solve for improving and individualizing customer experiences, and in techno-geek terms it’s known as RTIM (Real-Time Interaction Management).  Businesspeople using RTIM, however, would rather focus on results versus names, and the reality that these systems consistently generate 300 percent plus ROI [ii] – in other words, they are AI ROI Machines.

Why do RTIM systems outperform traditional marketing automation systems?  Simply put, it’s because they make decisions one individual at a time, hence delivering one-to-one interactions.  Figure 1 depicts the difference between many of today’s Martech systems and an RTIM system:

RTIM

Figure 1 – Typical Martech system versus RTIM system

Figure 1’s top lane depicts how traditional Martech systems place customers into segments, assign offers to those segments, and execute treatments in each channel.  On the other hand, RTIM systems act on behalf of each customer (instead of tranches of them globed into segments).  Moreover, RTIM systems operate based on one set of coordinated rules and analytics linked into a set of arbitration strategies – for thousands of customers per second.

For example, a company with five products marketed by five different divisions uses a single decision engine to resolve the best thing to do for the customer.  Consequently, an RTIM approach enables a brand to act as one organization instead of many disparate companies with dozens of conflicting rule engines.

RTIM-based systems recall an individual’s history – instantaneously – each time a customer interacts, factor in new (contextual) information, and calculate the best action.  They execute real-time analytics to determine an individual’s propensity to respond to a candidate list of eligible offers, then consider customer value and the economic benefits of the offers before rendering a final decision.  Granted, they don’t have perfect knowledge of the person, yet just like a human brain, they remember past interactions and learn from them, and place a premium on the most up-to-date information.  They’re also agile enough to perform dynamic recalculations (in less than one second) to further improve the pending decision and enhance the relationship.  Through this two-way iterative approach, they essentially carry on a real-time conversation in a single session as shown in Figure 2.

Conversational Marketing

Figure 2: RTIM’s iterative two-way conversational approach

Regardless of the superficial popularity and obfuscation of the term AI, it’s incumbent on us as marketing professionals to inspect the value-added by the underlying CX technology.  Earlier this year, Forbes did just that, citing the Forrester Tech Radar on AI technologies, which found decision management as the top hot AI technology (Figure 3).[iii]   And decision management is the central capability embedded in RTIM systems.

With RTIM and its decision management, brands can personalize in real-time, improving on legacy and static Martech systems and processes, and reinvent how customers are treated.   Decision management enables companies to make analytically arbitrated evaluations during every customer contact, treating each person based on their constantly changing context, fluid needs, and demands for relevance and continuity.

In his article, What is RTIM, Barry Levine calls it “Right Now Contextual Marketing” and goes on to cite work by Forrester analysts Rob Bronson and Rusty Warner – who both helped establish RTIM market awareness that culminated in the RTIM Wave[iv].   Levine describes how RTIM enables marketers to perform “a continual negotiation — a kind of dance — happening in real-time between all available data and all available offers/actions.”

AI tech

Figure 3: Forrester AI Tech Radar

Sounds obvious, and aren’t brands already doing this?  Well, not really.  Consider that on many channels:

  • You see the exact same style screen and get messages identical to those of every other visitor.

 

  • You can’t set and store preferences or alerts for receiving communications.

 

  • Call center, branch, and store agents seem ill-equipped to set, store, and recall even the most basic details about you, like how many children or pets you have, and what their names are.

 

  • When you receive an email, it’s maybe one of a handful of different versions, so again, if the brand sending it has a list with millions of email addresses, you’re receiving the same content as thousands of others.

 

  • Advertisements stalk us for products we just bought or already own.

 

  • When you place a call into a service center for the 10th time in 2 weeks (and you’re feeling obligated to invite them to dinner because how much time you’re spending with them), it’s clear the vibe from the agent isn’t exactly a personal one.

 

  • When you start a process on one channel and bail out, and then later reconnect, you’re forced to repeat steps.

 

As marketing practitioners, we can do better.  And as with any road to improvement, it must start with an admission that issues exist and they are negatively impacting others.

 

Impactful Customer Engagement

Great customer engagement starts with customer understanding.  And tiny details matter – things that on the surface seem trivial, although later may turn into a customer testimonial like this:

“Yes, that pet store remembered me, thanked me for my business, remembered my dog’s name breed, and age (Sandy, our Westie, is 14 now). They seemed genuinely concerned for her health and status.  They provided me valuable insights into her dietary considerations, and their app sends me reminders for refills that I might otherwise miss.”

Unpack that and contemplate what it’s implying – a memorable personal experience with an aura of care, empathy, and value.  Remembering names, age, past purchase history, applicable products – admittedly is basic stuff.   But take that basic data, and in combination with other factors, use it to systematically treat each customer’s situation uniquely, and you’ll put information and technology to beneficial use.

So, forget whether this approach is using AI or not, and start worrying about whether what it’s doing makes common sense for your customers at the moment of interaction.  What matters is not whether you store these details, but whether the underlying technology mines that data, learns customer preferences – and with each transaction gets smarter about optimal timing and consumption patterns, and realizes when to trigger meaningful messages.

Frontline staff are already busy, and they’re increasingly asked to be super-human and to provide white-glove treatment at scale.   To do it, they’ll need support from technology that stores and surfaces critical insights at the right time, so they can buck the tendency to treat customers as averages – because, what you don’t want are segment-oriented attitudes like this:

  • Hey since winters coming, everyone needs a coat so we’re pushing winter parkas.
  • She’s one of my older fixed income retiree types – they all love that annuity product.
  • Millennials love iPhones, and tweens always buy that pink Otter case.

 

You want individual-oriented sentiments:

  • That was Jim and he’s 62, and you’d never believe that Jim loves ziplining, has a Shih Tzu, and listens to Dubstep late at night while he reads his email.

 

  • Rosemary says she’ll never retire. She loves her job, loves to day trade, reads email at lunch, and will likely work for her entire life.

 

  • Yes, Sara is a unique. She’s 21 and never responds to text messages, unless from close friends; she gets up early, reads email before work, and is into Hello Kitty, guinea pigs, and Thrash Metal.

 

Each one of your customers are unique people, not customer id’s in cluster codes.  Treat them as such.

Continuous CX Improvement

Back to our little story of the inverted last name.  You’re probably wondering, couldn’t that company have solved this problem without an RTIM system?  Maybe.  But outfits that work from a common customer database, understand the true meaning of “system of record” and synchronize data when its distributed, and use RTIM to operate from an organized set of rules and analytics and make the best possible decisions in the moment are much more likely to consistently get CX right, and to improve it – one person at a time.

[i] What Consumers Really Think About AI: A Global Study, https://www.pega.com/ai-survey, 2017

[ii] Forrester Total Economic Impact (TEI), https://www1.pega.com/insights/resources/forrester-total-economic-impact-tei-pega-marketing, 2016

[iii] Forbes, https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/ , 2017

[iv] The Forrester Wave: Real-Time Interaction Management, https://www.forrester.com/report/The+Forrester+Wave+RealTime+Interaction+Management+Q2+2017/-/E-RES136189, Q2 2017,