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 (2015201620172018201920202021, 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

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

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

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,