We’ve all seen countless images of the proverbial empowered consumer. That mythical creature seeking convenience and instant gratification. It’s a conjured-up image of a time-strapped digital native that juggles five devices and 15 tasks, interacting simultaneously on a host of channels, using their super-human consumerism to wield terrifying powers capable of paralyzing unworthy brands.
Hyped-up as they are, these visuals still serve a healthy purpose. They remind us just how far digital bars have been raised, and that should cause pause and beg the question, “as businesses, are we measuring up?”
Collectively, the answer is we’re not. In fact, consumer satisfaction studies repeatedly confirm it. Simply search on, “consumer study poor digital experience” and voila – hundreds of examples. One study conducted by Software Advice found over 90 percent of consumers had one or more deal-breaker digital experiences when seeking customer support on mobile[i]. So, in an age with so much technology at our fingertips, why are we falling short? What can we do to fix this?
Too often, we fall short because we focus on the wrong problems in the wrong order. To correct this, it’s important to first consider a modern consumer’s mindset and what they’re demanding. With greater resolve, they’re chasing after nirvana, in a quest for brands that deliver products, services and experiences that are:
- Valuable / relevant
- Consistent / high-quality
- Enjoyable / attractive / personalized
- Familiar / trusted
- Secure / lower risk
- Compatible with values / social beliefs
- Convenient / simple / timely
Enterprises, however, can’t perfect all seven of these deadly-important areas simultaneously. So, the trick is finding what matters most, and then using AI and automation technologies to help.
AI in business won’t magically transform a company with fundamental structural flaws, such as poorly designed products, no unique selling proposition, or cost containment issues. These take great human leadership, creativity, and collaboration to fix. And it won’t manage the job of building and maintaining corporate culture. But in other cases, AI applied pragmatically to streamline processes and eradicate friction can make an enormous difference.
What’s proven to be a winning recipe in business is paying attention to customer-centric details. Brands hyper-focused on customer experience build a lasting reputation and increase in value. Look at Apple, Uber, Airbnb, Amazon, and even Booking.com. All built on the backs of nailing digital experience, often with a mobile-first mentality. Yet, with seven major areas and hundreds of experience details to consider, where should you start?
Is convenience king?
Out of the above seven criteria, convenience may be the most important in terms of driving long-term value, and the one CX professionals can influence the most. Perfecting convenience can separate winners from losers; sellers from re-stockers. Consider this quote from a CEB study[ii]:
“Brands that help consumers simplify the purchase journey have customers who are 86 percent more likely to purchase their products and 115 percent more likely to recommend their brand to others.”
And convenience contributes to and builds up other factors, such as being viewed as valuable, familiar, and trusted. It may be one of the chief drivers of loyalty. It can even trump something like price. For example, wireless carriers have learned consumers prefer unlimited communication plans because they’re convenient and simple, even though they may cost more[iii]. Consumers make impulsive and emotional purchase decisions when enough of the factors align, and tend to justify things afterwards. Since consumers’ assessment of convenience is qualitative, figuring out how to elicit positive emotional responses regarding convenience is crucial.
In a consumer’s mind, the label of convenience translates into a business being viewed as:
- Useful and suitable
- Easy to buy from, use, and transact with
- Requiring less overall effort
- Simple to understand / responsive to issues
- A time saver
Each is a judgment call by an individual, but with critical mass and time, these opinions converge to a collective market consensus (the wisdom of the crowd). They manifest themselves in the form of review scores, ratings, and tribally-shared social advice. It’s this reputation that drives commercial allegiance.
Largely, consumers make emotional decisions when they choose one product over another. Sometimes they want combinations that are seemingly impossible to get:
- A readymade desert that tastes great and is nutritional
- A car that is inexpensive, fast, great looking, economical, and durable
- A delicious pizza that comes in a few minutes, is made by an environmentally-conscience brand, and oh…costs less than $10
It’s no wonder brands struggle to satisfy whimsical consumer desires, but fickleness aside, they cry out for brands to simply simplify things. Ironically, they work longer and harder to live in a world that supplies them with exploding choices for everything but precocious little time to weigh options, which in turn drives them to crave simplicity in decision making. They demand trusted information that is easily accessible. They want user-friendly ways to weigh options, and help navigating processes. In a 2016 survey on travel shopping preferences, consumers picked ease of use as the top reason they booked using an online travel site.[iv]
AI knows there’s no second chance to make a first impression
Consumers want convenience, but which actions will achieve maximal impact? Before answering this, keep in mind a marketing 101 maxim: perception is nine tenths reality. And perception is often built-up on first impressions. Further, when an initial impression goes wrong, it takes multiple positive interactions to repair it. As such, consider using AI as tooling in helping elevate levels of perceived (and real) overall convenience in critical first-impression customer journeys such as:
- Getting a quote
- Completing an application
- Navigating a sign-up or onboarding process
- Completing an initial purchase
- Setting up online payments
And during service scenarios such as:
- Order status checking
- Lost card replacement process
- Scheduling an appointment
- Finding a doctor
How does AI support these? If we agree that AI is a mixture of automation and intelligence technologies, AI can help streamline the process for consumers getting answers such as the status of an order, return, or claim. Further, consumers can even ask these systems to schedule a store or branch appointment, find the most convenient time and location, and then add the appointment to their calendar.
AI-powered chat bots (and other self-service portals) can provide 24 x 7 first-line support for answers to questions like:
- How to transfer funds
- Make an online payment,
- Get account and policy status
In many cases, without any human intervention, bots can answer questions, close out an inquiry, and even assist with completing a transaction. In situations requiring human agents, AI-based systems can orchestrate seamless hand-offs of data and case details, allowing humans to pick up precisely where machines left off.
Make no mistake, AI skills are already going far beyond performing simple tasks. Today, AI engines can give nuanced advice, surface unique insights, and provide proactive recommendations. The most sophisticated systems even factor in customer context, such as location, weather, mood, and motivation before arbitrating on the next-best-action.
In banking, for instance, AI can help track savings and spending habits, and send threshold alerts. To illustrate, suppose a consumer has a recurring transfer from checking to savings each month. AI can monitor account balances and send an alert when upcoming bill payments are forecasted to drain a checking account beyond non-fee thresholds.
In healthcare, there’s Dr. AI from HealthTap, who can engage in conversation aimed at providing triage and care advice, using a locally-stored health profile, a network of over 100,000 doctors, and Bayesian learning AI to serve up the next-best-advice.
What’s the right set of technologies for your stack?
Well, there’s good news and bad news. First the bad news – there is no one right answer, and with thousands of vendors (6,829 in this marketing landscape), open-source packages, and resulting combinations of solution stacks possible, there’s no evidence anyone has found the absolute best combination, or ever will.
Now the good news – you have a ton of alternatives, with many combinations likely to work, but finding a stable and winning blend is tricky. Some tools, on the surface, look easy to use but aren’t. Others won’t live up (functionally) to their marketing hype. The best advice is to form a solid basis with at most one or two platforms covering essential infrastructure (that you can’t afford to switch in-out), and make sure these platforms allow for plug and play with adjacent pieces likely to have shorter useful lives.
For example, find vendors with durable connectors for wrangling data into an actionable customer profile, a real-time hub that acts as a central brain to arbitrate customer decisions, and integrated customer analytics. These components are foundational, and must be centralized so they operate in a channel agnostic fashion. New channels may spring up, and others diminish in importance, but a decision engine which feeds on key behavior data, arbitrates decisions, and renders appropriate next-best-actions is a necessary constant.
There’s a real irony forming with AI in business. We’re building and teaching computers to be more human, while as humans we’re being led and conditioned by our busy lives and workplaces to be more machine-like. The problem is computers are no humans, and humans are poor computers.
Step back and consider what’s best for the consumer. Providing great first impressions, as well as seamless and gratifying ongoing experiences, requires well-functioning and well-behaving humans and machines working in concert. Consumers want products and services they’re proud to recommend because they make life easier and more enjoyable. When things go wrong, they expect flexible help and fast solutions. When self-service isn’t working, they demand cases smoothly transition to well-informed, caring, and compassionate humans. Brands must skillfully, judiciously, and mindfully weave together computer systems with humans as they design for convenience in all the complexities of customer journeys.
Delivering convenience must be a paramount goal, so reflect on the unique characteristics of the individuals you serve and the nuances of their voyages. Dry run how each will navigate your services: some will be older and less familiar with technology; some will be capable of juggling five devices on five channels; sometimes technology will fail and require fallback processes.
Ultimately, your convenience reputation will be defined by a diverse set of consumers steering through a wide variety of conditions and processes. Use AI and humans to start off on the right foot, deliver consistently under normal operating conditions, and to proficiently handle the inevitable miscues.
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.
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:
- 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.
- 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.
In 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:
- Collect interaction intelligence and contextual data from each touchpoint, and connect it directly to a system that can leverage that information immediately.
- Be brain-like, tracking behavior patterns in real-time, sensing needs, and using analytics to dynamically calculate value, comprehend preferences, and predict intent.
- 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.
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.
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
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
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.
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.
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.
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:
- Enter name and dates for campaign
- Select audience by city, interests (mix of music, pop culture, shopping, sports, etc..) or look-a-like targeting; age (typical bands); gender; language
- Decide on display ad on desktop or mobile or both
- Specify budget (e.g., $1000)
- Upload display ad creative image
- Add social media promotional ad (if desired)
- Add URL for click through (analytics tracking automatically setup in Google Analytics)
- 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.
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.
Apparently 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:
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.
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.”
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.
[ii] Forrester Total Economic Impact (TEI), https://www1.pega.com/insights/resources/forrester-total-economic-impact-tei-pega-marketing, 2016
[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,
The Formidable Years
As a child, Marketing was always loquacious, a doodler, and squirmy – voted “Most Likely to Be Told to Shut-Up,” she sharpened her latent skills in secret, occasionally posting her art on billboards, doing voice overs on an old cassette recorder, and even making a fluke cameo in a school play with a minor sing-song role. But no one noticed.
Generously provided a diploma and sent on her way, after graduation she meandered through part-time jobs – waiting tables and moonlighting as a street artist selling a few pieces to magazine execs who used them in ads. Family muttered at dinners and gigs were scarce.
Single with Jingles
Her first good-paying job was as a broadcaster on a local radio show. She loved the writing and performing – being able to broadcast her message to a larger audience. She came up with little ear-catching jingles that were memorable to some. Still, she felt only “half successful” – never sure which half of her act worked.
Soon, she enrolled in night school, studied statistics (of all things), and brought to the station the idea of doing surveys to see which bits the audience liked. It worked. The station manager even coined a new term for the audience breakdown by age groups – calling them “segments.” They crafted shows to cater to different segments that listened at various times. The station quickly became tops in the market. Yet, something was still missing – that direct connection with her listeners. She’d do local events, having drinks with fans, but habitual cocktailing was exhausting and not scalable.
One night, at one of those station events, Marketing met Technology. Tech gushed about emerging addressable channels, world-wide webs, email at scale, and mobility, making an impression on Marketing. Although he was socially awkward, they hit it off, further confirming the Laws of Adjacency Physics that complementary opposites attract. A few months later they married and the rest, as they say is Database Marketing, Content Marketing, and Martech history. The they went on to have a few offspring… well over 5,000 in fact.
Thanksgiving season calls on us to be thankful for things we otherwise take for granted. Marketing, eternally lambasted by non-believers as that group that creates logos and pretty slides, home of the artsy-fartsy types, creators of junk mail, and hosts of the two-drink minimum parties (they’re just jealous) – deserves better this year.
Like so many things, there’s good, bad, and ugly. Surgeons are not all good. Neither are marketers. But how many times have you ever heard someone thank a great marketer. Here’s an argument for why you should this year.
The collected evidence, submitted for your consideration:
Great marketing doesn’t happen by chance. It takes devoted and creative people – brilliant, diverse, methodical and collaborative people, many with incredible range of art-science motion, who come together from all walks of life: artists, sociologists, journalists, improv artists, movie producers, broadcasters, computer scientists, data scientists, quants, researchers, linguistics experts (and occasionally a trained marketer) ….and together they’ve brought us…
~ Humor and entertainment:
Exhibit #1: Arguably all starting with this Fedex ad in 1981, opening a new advertising chapter using laughter to engage us.
Exhibit #2: Anheuser Busch’s Bud Light TV ad oeuvre not only makes us laugh, as they pitch a watered-down lager, they also put commercials on center stage with edgy material constantly pushing marketing’s comedic and acceptable lingo boundaries. Case in point…check out this one, taking the liberalization of profanity to new levels (up or down – depending on your view).
It’s strange nowadays to see any successful ad that doesn’t have some wit, jocularity, or chuckle-worthy aspect. Admit it – half the reason you tune into the Super Bowl is for the commercials – and it’s not because you’re hoping to discover a more absorbent paper towel to wipe up your coveted light beer.
~ Amazingly eye-pleasing art and creativity in ad visuals – meaning we don’t hate the ads we view.
~ Innovative products, that we want, because someone cared to listen to us or went out of their way to push their corporate culture to innovate. Such as:
- Better ways to make reservations, vacation, get from point A to point B, shop, find a job, keep in touch with family and friends, and watch movies
~ A better more personalized experience with products and services…
- You like personalized music consumption – thank marketing
- Enjoy your video-on-demand with recommended content – thank marketing
- Dig the nudges you get to exercise more, so you don’t waste away on a couch – thank marketing
- Fancy discounts, rebates, and points for stuff you buy and use – thank marketing
Presently, I’ve got no glib prognostications, no “Five Marketing Best Practices,” and no “2017’s most disruptive Martech startups” (maybe next post).
Rather, today I’m pausing to admire how far marketing’s come, how much smarter she is, how attractive she’s become (she put blood, sweat, and tears into that beach body), and how proud we should be of her when she does excellent work.
So, this thanksgiving season, go out of you way to thank someone you probably have never thanked before. Thank a great marketer. I will.
Source: Exploration of Saturn’s Moon’s by Kacper H. Kiec
As a Marketer, when you craft successful promotions, you’re especially proud of their creative aspects. And it’s understandable because creativity seems our last bastion against the perceived onslaught of machine domination, so we fiercely defend that turf. The tenuous argument being, “robots are no match for human creativity!” This viewpoint, besides inviting a cage match between humans and machines, also smacks of keeping math and machines out of any solution, lest boring and stiff digital influences ruin the warmth of our marketing art and experience show. However, for all the aspiring “Michelangelos” out there, it’s time to rethink this, lest you find yourself selling one-off ad creatives at street-side craft shows.
A promotion is fundamentally your story; your pitch in a nutshell – delivered through a channel to an audience of one – assuming it gets through. And the fact that it oozes creativity and garners the right emotional response can be critically important to a customer’s reaction. But what is its true worth? Compared to what? Is there a chance that for most eyes it will succumb to fading into the backdrop with all the other one-size for all advertising clutter?
With a fickle, time-pressed consumer, your promotion has – at best – a fleeting chance to capture an individual’s attention, make an emotional connection, explain a deal, plus convince that person they should care. On average, you’ll get about five seconds to grab interest; succeed and you may earn another five to emotionally connect, and so on. In most cases, no matter the channel, you’ll be afforded about thirty seconds, a few minutes tops – to deliver the goods.
Given this, every top-line pitch needs a “No Boring Zone” mentality with visually appealing facets – nonetheless cookie cutter theatrics alone won’t win the day. You need to get serious about how to use math along with machines (artificial intelligence) to radically fine-tune sales messages and custom-fit them for individuals – in other words, personalize them. To do that requires scaling up a promotion production and testing factory.
A canvasing of the available marketing automation tooling finds that very few help solve for the bona fide business problem of creating and testing a wide variety of promotions across a plethora of channels. In fact, most simply give you a facility to manually key enter the metadata for each version, creating them from scratch – calling this Offer Management or an Offer Library. The problem is as an artisan, you basically run out of material and time in a futile attempt to manufacture a decent collection for the library. Thus, the conundrum – to cut through the noise, and find the right version that resonates for each nuanced individual, you must create and test thousands of versions, but old-fashioned human means alone cannot keep up. And if you muster the means to produce numerous alternatives, it’s equally difficult to monitor their effectiveness and pick the winners. You need tools that automate mass testing and response tracking, and math to tell you exactly what’s working and why, yet few such tools exist.
Everyone talks about knowing customers better; using that knowledge to personalize. It’s an admirable aspiration. However, commendable goals don’t necessarily translate to better outcomes. In this case, it doesn’t matter how well you know customers if you can’t hyper-customize content, messages, and other creatives – and produce tailor-made promotions that really fit what customers expect in the moment of interaction.
You won’t entice my response by extrapolating from a few of my preferences and placing me into some huge segment. All the “Hey, Vince wouldn’t you love to travel, drink exceptional wine, and eat at these fine places” in the world won’t matter if I don’t get something that is fabulously timed, speaks directly to me and visually jumps out, elicits an emotional connection, stays engaging, and commands attention due to its specific relevance – in other words the message needs to be personalized to my promotional preferences and exact product needs. In fact, the promotion itself (in its entirety) must be an enjoyable experience.
Moreover, the same goes for financial services, transportation, telecommunications, insurance, and healthcare promotions.
Marketing’s 4th Dimension – Promotions
Marketing technologists (martech types), and the automation applications available to them, tend to focus mainly on these big three dimensions that drive response rates:
~Data: Stockpiling and codifying key customer data
~Behavioral Analytics: Gleaning intent and preference, scoring response propensity, and segmenting
~Channel & Time Optimization: Delivering messages through the right medium at the right time
All of these dimensions are important pieces to solving overall marketing optimization. However, without the ability to generate thousands, if not millions of promotions (with varying copy options, incentive levels, calls to action, creative versions and such), about one third of what drives response and conversion is woefully underserved in assuring messages are noticed, relevant, and responded to.
Presently, this 4th dimension, promotions, has received practically no attention from marketing automation technology and AI – and instead marketers merely accept that snail-like non-scalable A/B testing is the best way. The fact is, even with armies of humans crafting variations and A/B testing, the number of manageable versions you can juggle will be in the hundreds at best – when what you need to compete is the ability to create & test thousands of these.
Ok, not convinced yet? Then perhaps a little math is in order (as he locks the classroom door and places nails…I mean chalk… on chalkboard):
Problem: Calculate the number of email message variations.
Email promotion components:
- 100 products to sell
- 10 images per product
- 10 subject lines
- 100 email templates (to test fonts, color, container locations, call-to-action button)
100 x 10 x 10 x 100 = 1 million promotional variations
News Flash! You have no chance with just brute human force to create and test this many variations.
Lucy & Ethel couldn’t keep up – and neither can you
In this famous Chocolate scene from I Love Lucy, an illustrious TV series from the 1950’s, Lucy & Ethel prove that manual human labor, no matter how clever, can’t keep up – quickly becoming the bottleneck in an otherwise automated system.
Given this seventy year old lesson, why do we think that humans alone can drum up and test an acceptable level of promotional assortment? They can’t. But still, stubbornly, we hand-crank creative versions, accepting less variation. Yet the better way is to let people fashion the promotional raw materials as re-usable creative elements, combined with letting artificial intelligence test the combinations – surfacing the winners – automating and individualizing the wrapping of your chocolates.
Marketers, as well as many businesspeople, are warming up to the current power and future potential of AI and what’s at its core – Data Science. In fact, in a recent study by the Boston Consulting Group of more than 3000 executives, 61% of those surveyed see developing a strategy for AI as urgent[i]. And in this case, machines and math can assist. As a marketer, you already know the power of AI and machine learning. It’s what helps you calculate customer value, score a customer’s propensity to respond to a given incentive for an applicable product, and even predict when to present the offer. And to get started, you don’t need a million options. Instead, use human judgement to field a reasonable set of challenger creative components (perhaps a dozen of each), then use AI to perform champion – challenger tests on the combinations.
Exactly how will AI and machine learning help generate and test copious quantities of creative offer variations? Enter natural language generation and automated (multivariate) testing.
Natural Language Generation (NLG), Visuals, and Templates
In our email example, we discussed written variants (e.g., different subject lines), various visuals (fonts, graphics), and template alternatives (where to place the copy and graphics). Let’s break these 3 elements down:
Natural Language Generation (NLG)
Computers can generate language. In fact, they’ve been doing so for over 30 years. Today, they can even take into consideration emotional aspects. In 2015, Gartner went on record forecasting that by 2018, twenty percent of all business content would be computer generated[ii]. Although aggressive at the time, and unlikely now, it still highlights the potential of NLG, and progress nonetheless has still been impressive.
For marketers, there’s already good examples of how NLG is used today, and can be helpful in solving for the promotional version dilemma.
For example, Persado Go uses NLG to generate variations of email subject lines, and then records performance broken down by specific elements such as emotions, formatting, descriptions, and so forth. Candidate subject lines are generated from a huge database, and a sixteen-version test is setup.
Visuals are combinations of text aspects (font type, styles, size), color, video, pictures, and graphics. A picture is not only worth a thousand words it’s also capable of sparking an emotional connection. And although AI is encroaching on even this human endeavor, for now people (assisted by AI) are still superior to pure machine generated creative assets.
Templates drive how you both organize and showcase content. For an email, it controls where recommended content will display, where a call-to-action button will be placed, what font will be used for written copy, and so on.
As with any element, a wide assortment of templates should be tested, each with innovative ideas about where containers should be located, and which font and color scheme will work best.
Now that you have all the ingredients, just mix and serve. Except how will I know which versions work best in which circumstances?
Multivariate Testing & Adaptive Machine Learning
Enter multivariate testing – which sounds complex and geeky – but it’s not that difficult (although admittedly the term is geeky). A multivariate test is simply a series of A/B tests, done simultaneously – which means you won’t spend months testing; instead doing one test (testing a string of modifications all at once) in as little as a few days or weeks.
And using an adaptive machine learning approach, such as this one available from Pegasystems (in full disclosure I do work for Pegasystems), the whole testing process can essentially run automatically, as the machine (the math algorithm) determines the eventual winners by ranking them higher as the digital response evidence pours in on which promotional variant get the best take-rate in which situations.
You and The Machine will go far
Too often we fall victim to pitting ourselves against machines, rather than exploring a symbiotic relationship with them – like the one we have with our smartphones. As marketers, we need to think the same way. AI can assist us, and we must embrace that. Exploit technology for what it does well, and weave that into your promotional factory, leveraging its ability to scale things to new levels never imagined with manual methods.
[i] S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, “Reshaping Business With Artificial Intelligence,” MIT Sloan Management Review and The Boston Consulting Group, September 2017.
Thirty years ago, when I unpacked my first computer, a Commodore 64, rigged it to my 13-inch tube TV, and wrote my first program, the process of creating a digital experience hooked me. That I could design and assemble mere bits and bytes, package them up into an asset, refine it, and eventually share it for the benefit of others – for entertainment or problem solving – just enthralled me.
With time and market efficiency sorting who gets paid to do what, I altered my path away from programming and toward design and consulting, leaving the coding and compiling jobs to those more talented than me in that trade. That wonderful feeling of accomplishment, however, never left me and still drives me today. Whether it’s creating visual concepts, designing software, or producing media, creating a re-usable asset with experiential worth (striving to be a CX transformer), for me, is a universal and time-tested motivator.
Experiential assets, originally made from scratch, must evolve to the liking of their benefactors. They invariably play a role in nearly every commercial experience. For example, a vehicle manufacturer produces a physical product, but the agency who markets it as well as the dealer who sells and services it – all add crucial elements into the customer’s journey of shopping for, buying, and owning that vehicle – all contributing to (or subtracting from) accumulated impressions of overall worth and value.
Organizations are either born with this mentality, where it’s baked into the fabric at every level and function of the organization, or they must transform. Startups who don’t adopt this mentality burn through money and soon dissolve. Legacy firms are faced with odds not unlike that of a recovering addict. Most hit bottom, before they realize the extent of their problem, and by then it’s often too late. Few are afforded the chance to recover and most who try will regress. In fact, a recent Forrester study [i]indicated as many as 77% of those who embark on CX transformation will fall short.
With all this buzz, don’t we already get great CX?
The short answer is, not really. According to a global survey [ii]of 7000 consumers, 89% “think brands need to work harder to create a seamless experience for customers.” There’s lots of talking about seamless and personalized experiences, and less walking the walk. And consumers continue to report a deficit of it, as evidenced in an Infosys survey [iii]indicating that 73% have never experienced online personalization. Here’s the reason: Many of us, and the firms we work for, aren’t practicing what we preach.
Regardless of what you do, you’re in the business of creating customer experiences. Whether in sales, marketing, service, or operations; whether you set vision, do design work, code, implement, consult, or the like, your ultimate mission is creating something that someone else appreciates and finds value in – because it makes their life better. It improves their experience. If you can’t tie what you do and why you do it back to that, your mission is misdirected.
The only reason customers buy, use, or recommend products or services is because they experience value. So, if you simply blabber about CX but don’t improve it, you’re subtracting value, like in figure 1:
Figure 1: All Talk Equals Value-Subtracted from CX
Everyone plays a role in experience management. For example:
- If you’re a banker, during any interaction, clients are judging each aspect of your services. When they point out friction, dissatisfaction, annoyances, frustrations – they aren’t being pests – they’re handing you gold.
- If you manage a telco’s call center, though one step removed from direct feedback, front-line agents will hand you that gold. Will you ignore it, or will you investigate, catalog it, document it, and act on it?
- If you design software used by that banker or agent, you’re instrumental to how the total experience comes off when moments of customer truth occur. Software augments customer facing CX delivery, either enhancing it or contributing to its malfunctions.
Software and AI technologies have already changed our lives, and continue to transform how we experience life. From when we wake to the minute we doze off, the way we interact with the world, for business and pleasure, is vastly different now from the day I cracked open that Commodore box.
Data is abundant and the right intelligence in software is available. Yet how both are captured and deployed is what spells the difference between memorable moments versus forgettable incidents. Dated advice, cloaked as sage recommendations, abounds on what data to tap and which AI technologies to trust.
Beware of the CRM “Catchers in the Rye” who have a vested interest in selling old software disguised as AI and one-to-one personalization, spruced up with fancy new names like Customer Data Platforms, but stuck in a forgone era. Peel these back and see if they rest on an old batch and blast architectures with no real proven use cases for predictive analytics, built essentially for pushing emails to segments. You’re sure to hit a wall with these, since they were never built to handle real-time, analytics based one-to-one contextual engagements. If you’re interested, I cover this topic in more depth in this article.
Or worse still, beware the do-it-yourself CRM & AI pushers, selling piles of new programming gadgets with exotic names such as Python, Storm, Spark, and Kafka, but missing the warning label that says, “Much assembly required.”
CX Transformation Process
The transformation process, contrary to overhyped tales of sudden disruption, is mostly evolutionary. It involves creative minds with an unwavering and relentless obsession to improve experiences – as measured by customers. But today you must do everything you can to go through this process fast.
Iteration (figuring out how to improve) means executing various steps in succession – speedily and repeatedly to learn fast. It also takes a flexible methodology and tools supporting rapid revisions. Each time Thomas Edison’s filament didn’t work, he wasn’t failing, he was learning. When asked about racking up so many failures, Edison replied, “I have not failed 10,000 times. I have not failed once. I have succeeded in proving that those 10,000 ways will not work. When I have eliminated the ways that will not work, I will find the way that will work.”
Be unyielding in finding gaps, filling needs, overcoming shortcoming, and plugging them with an improved asset. Find the simple stuff, that exacerbates customers, but is easily addressed. Do ten thousand little things right – and fast.
To succeed, you’ll need to be well-equipped with the right CX transformation methodology and technology. Speed to market and economies of scale matter now more than ever. It takes steadfast customer centric vision, modern tooling, and an agile methodology. Let’s explore the four key steps shown in figure 2.
Figure 2: Depicting the CX transformational process steps
CX Transformer Step #1: Conceive Innovation
As you come up with a concept, consider the objectives…. making things better, faster, cheaper. Ideally, you’ll eventually address all these, but practically you’ll need focus. Will the proposed innovation fix something that is terribly broken? Better yet, will it preemptively address a shortcoming. Often, fixing inadequacies is simple, yet the consequences of not fixing them are huge.
To find opportunities for CX innovations, use analytic heatmaps fed by behavior data on websites and mobile devices to zero in on where customers struggle or bail out. Mine reviews, comments, call logs to find repeating themes.
Here’s an example I heard from a person I sat next to on a flight. He had booked a trip to Dubai, but the travel service never proactively alerted him that travel to UAE requires a passport that doesn’t expire in less than six months. On his departure day, he couldn’t check in, and subsequently was on the phone for hours, working the problem and seeking amends for this horrible experience. The root cause was recorded in logs. The fix (innovation if you will) was rudimentary and excruciatingly easy.
“If customer books trip to country X, and passport expiration date is Y, alert customer about passport rule.”
In this case, the customer placed a gold nugget into the lap of the brand, begging them to fix it for future customers. Will they? Only if they’ve institutionalized collecting hiccups like this, and weaving them into the innovation and improvement process.
Think of innovations in sets. Will the CX innovation set be press-worthy; will the total experience be unique and better? Take the innovation set and break it down into manageable chunks. To improve service usability, for example, consider whether the specific design is elegant, visually appealing, modern, stylistic, easily navigated, intuitive, and so forth. Remember, even when just creating a form, such as an insurance policy application, all the above matters in CX.
Spend three times as much effort on design versus construction. If service improvement is your aim, pick (as your innovation set) a critical customer journey that cuts across various functions and channels, and obsess with its design. While iterating on the design, always apply a range of customer sniff tests tied to customer personas. How would customer X use this? How would customer Y perceive this?
Just as incentive drives employee behavior, it drives customer behavior. Customers are motivated by the value they both perceive and achieve from using your products and services, regardless of the organizational excuses they encounter along the way.
CX Transformer Step #2: Judge Harshly
Critique innovations, not just with self-criticism, but with the varied feedback of others. Compare to market alternatives and what big competitors are doing and what customers complain about. Once again, view the current state of the experience through customer eyes. Clients not only measure success, they also give clues about required innovations. If an asset works they use it, open it, share it, like it, and buy it.
Watch exactly how customers use the innovation. Designers call this usability testing, and too often, it’s shortcut out of the development process in the name of speed. Watch how customers interact, how they shop, how they decide, whom they consult with, and why they buy. Look for where they struggle, the questions they ask, why they need help, and ask what went wrong. Then go back to the drawing board to create a new experience, craft a new email, create a form, redesign a web page, or work on ideas to improve how agents engage with customers.
Use a basic four quadrant Risk / Reward matrix, as shown in figure 3, to prioritize a backlog of CX improvement opportunities.
Figure 3: Risk (Effort) / Reward (Value) matrix used to prioritize innovation ideas
Don’t make your goal mimicking competitors, but instead to gauge your inferiorities to them, study their winning ways, and chart your course – but dare to be different – then test and learn. Compare your asset to others available in market. This guides, both in terms of whether you’re behind, but also what hasn’t been done – thus presenting opportunities to do something new, something unique.
Pattern yourself on proven winners, not just in your industry, but also in very different ones. Why? Because that’s where unique ideas come from – not from copying your competitors, but from proxies that when applied to a different problem become a new idea.
For instance, to transform the branch experience for its customers, Capital One recently introduced café style locations, drawing on a combination of Starbucks and Apple store concepts.
CX Transformer Step #3: Apply a Value Test
Determine whether your innovations improve experience. To do this, perform behavior tests and not just surveys. People don’t always do what they say they’ll do. Test your innovation by getting real customers to use it in production pilots, and then measure whether, for instance, the task was accomplished faster.
Getting there may not be easy, cheap, or fast, but if your product isn’t passing these tests, you haven’t improved your customer’s experience. Each innovation should pass at least one of these tests, and collectively overtime, it must pass all three.
At this stage, the test is if your customers are buying or using your asset. If they see value, they’ll do these things, so measure for it, and use this as your ultimate yardstick.
CX Transformer Step #4: Analyze Objectively
Once you release your concept into the memorialized world of production, objectively (and recurrently) evaluate its worth. What works today may not work tomorrow. In addition to pure customer feedback, consider getting an objective third party to scrutinize it, since creators as well as customers have blind spots and biased views.
For all its advances, and there are many, CX today – when analyzed objectively – is still mostly choppy, dysfunctional, too slow, and places too much burden on the customer. Admittedly, some industries (such as banking and telecommunications) have made more progress than others, yet largely, especially for massive enterprises, CX is frankly still very siloed.
Firms spend millions of dollars on data collection, design thinking, journey mapping, voice of customer, CRM systems, employee training, and so on. Yet when these efforts are not coordinated around a systematic process, data, technology, and culture – hyper coordinated and committed to improving CX –most of that investment will be for naught.
It’s human nature to either ignore feedback or want to defend your baby’s looks, and if you’re busy defending versus fixing simple things, CX won’t improve much. It’s also human nature to pass the buck – meaning no one will take responsibility, because even though at our core we’re pack animals, it’s ironically not in our nature to communicate issues across organizational pillars.
CX transformation doesn’t come easy and it doesn’t come cheap, and rarely comes fast. But for those who listen to and watch customers, fix ten thousand small things fast, live by the adage innovate or die, and cross-functionally collaborate on behalf of better customer experience, the rewards will be plenty.
[i] Forrester, http://www.datastax.com/wp-content/uploads/resources/whitepaper/Forrester-CX-TLP_DataStax.pdf, April 2017
[ii] Zendesk, http://d16cvnquvjw7pr.cloudfront.net/resources/whitepapers/Omnichannel-Customer-Service-Gap.pdf, November 2013
Lately, if you’re like me and enjoy following the AI narrative (even if just for grins & giggles), you’re inevitably sucked into philosophical wormholes that always seem to pop you out at the same place – a world where machines rule all.
Strangely, though, we rarely encounter future scenarios that follow a path we’re already on, where machines are but tools used to assist us. If we project this scene forward, some interesting questions to ask are, “What does that world look like, and who are its haves and have-nots? Are AI titans forming?”
AI, for all its hype and promise, is still very much in its infancy. Far from being able to get up, put on its clothes, and take your job, AI today is less of a super scary robot, and more like a smart washing machine (funny you should ask, as there is one of those). It can help us conserve resources and do specialized tasks more efficiently, like getting clothes clean using fewer resources, but it really can’t do higher order thinking we take for granted like abstract judgement and reasoning. However, that super smart washing machine (and all its other specialized variants) has an owner, and together they can wield tremendous influence. And anti-trust laws (put in place over 100 years ago to prevent corporate behemoths from controlling entire markets) may be full of loop holes in the digital age.
Using a singularity argument where machines alone rule provides a convenient escape from a more complex debate about a future where various human and machine forces collide and collapse together. In this scenario, a select set of firms use walled garden data to feed their AI, and as such, seize unprecedented levels of control, influence, and power.
Here’s an example. We’re already seeing a massive rationalization of power and influence collapsing into AI titans like Google, Facebook, Apple, Microsoft, and Amazon (controlled by surprisingly few individuals); not pure machines, but formidable entities nonetheless, fueled by AI, and directed by small pools of mighty people already circling their wagons around a plethora of data.
In the short run, we (the consumers) seem to benefit, getting innovative little features and conveniences such as travel guidance and digital yellow pages, but unbeknownst to most, to get these we sacrifice gobs of data and hence privacy. Each time we travel with GPS on, our whereabouts are tracked and stored. Each time we search, we provide preference footprints. Meanwhile, the behemoths rack the data up, building behavior and preference repositories on each of us.
So what’s the rub?
First, it’s our data. Thus, it would be nice to be able to view it, and if it’s wrong, correct it. The European Union passed a law recently that goes into effect in May 2018 called GDPR – General Data Protection Regulation. Its intent is to give consumers more rights and transparency with their digital data. Other consumers outside the EU could use similar privacy protection laws.
Second, to some extent, without being cognizant of it, our choices are already being limited. For example, when you search in digital maps, perform online comparison-shopping, or ask a voice pod for restaurant recommendations, the top options returned may not be calculated objectively. Ranking algorithms already place higher emphasis on businesses that pay more to play, and search conglomerates, like Google, rank their interests (including businesses they have a stake in) higher.
Each time we purchase something, we’re casting a vote. When we go through a buying cycle, we are creating implied demand, and when we purchase we’re reinforcing that the supply is meeting the demand we created. When this cycle is cornered, choice becomes an illusion. To illustrate, on June 27, 2017 the EU slapped Google with a record-breaking $2.7 billion fine, charging the tech titan with doctoring search results giving an “illegal advantage” to its interests while harming its rivals.
Third, firms can and will use your data for their benefit, and not necessarily yours. Prior to the digital age, people stereotyped others by their physical choices such as their house, car, job, shopping habits, and clothes. Although today those choices still factor in, we also project digital personas: where we surf, what we share and like on Facebook and Instagram, what devices and channels we use, how we interact online, and so forth. When these behaviors are crunched and codified, they become rich fuel for algorithms that can manipulate, discriminate, or even do harm, without the algorithm’s owners having any concerns for side or after effects. Show preference for fast cars and thrill-seeking vacations, and not only will you receive more of those offers, but you might also receive higher insurance premiums. Share enough medical history, and an insurer’s algorithm may score you at high risk for a chronic disease, even when there’s no medical diagnosis, and there’s no certainty you’ll ever develop that condition. That might make it very hard to get medical coverage.
Admittedly, not all of the use cases lead to undesirable outcomes. In late 2016, American Banker ran an article on next-gen biometrics detailing how banks use consumer digital behavior signatures to detect fraud and protect consumers from its effects. And although consumers initially do benefit from such a service, what’s interesting (and concerning) is the nature of the behavior data fed to the fraud detection algorithm: the angle at which the operator typically holds the smartphone, pressure levels on the touch screen, and cadence of keystrokes.
Unquestionably, the bank’s primary goal is predicting whether an imposter is behind the device in question. Nonetheless, what’s stopping this same bank from using that data to predict a consumer’s likely mental state, such as likelihood of inebriation, legal or otherwise? Moreover, whether that prediction is ultimately accurate is irrelevant to the immediate recommended action and the subsequent consequences. We have little protection from the effects of algorithmic false positives, and today, except for credit scores, few brands have any accountability for model scoring accuracy.
Here’s a scenario. An algorithm thinks you’ve been drinking based on your smartphone behavior and flags you as too drunk to drive and disables your car, forcing you to find another way home. That’s one thing, but think about this – that same data might also be available to prospective employers, who use it to forecast your job performance, scoring you lower than other candidates based on its dubious drug use prediction.
Who owns and manages your digital behavior data? Are they subject to use restrictions? The answer is (although the data is about your profile and your behavior) – you don’t own it and your rights are limited. And although some of the more inconsequential data is scattered about (such as name, address, date of birth, and so on), the deeper behavioral insights are amassed, stored, and crunched by the AI titans, with seemingly no limits or full transparency, and with little insight into where its shipped, and who else might eventually use it. They suggest we simply trust them.
Those that ignore history are doomed to repeat it
History is always an amazing teacher. In the 19th century, railroads consolidated into monopolies that controlled the fate of other expanding industries, such as iron, steel, and oil. They dominated the distribution infrastructure – just as today’s AI titans, in many respects, control the lifeblood of modern day companies – their prospect and customer traffic. And those other expanding industries (iron, steel, oil) were no different. They too controlled the fate of other expanding industries, which all needed their materials.
Soon after their start, Google’s founders adopted a mantra, “Don’t be evil.” In October 2015, under the new parent company Alphabet, that changed to “Do the right thing.” Although the revised phrase still rings with the implication of justice, it raises the question of who benefits from that justice, and if there’s a disguised internal trust forming.
Everyone knows that business, by its very nature, is profit driven. There’s nothing wrong with that, yet history teaches us that we need checks and balances to promote a level playing field for other competitors or potential entrants, and for consumers.
In his 1998 book “The Meaning of it All,” Richard Feynman, a famous scientist, tells a story of entering a Buddhist temple and encountering a man giving sage advice. He said, “To every man is given the key to the gates of heaven. The same key opens the gates of hell.” Unpacked and applied to AI today:
- The term “every man” can imply an individual, or organization made of people, or humankind as a whole.
- Science, technology, data, and artificial intelligence are but tools. As history shows, humans use them for good and evil purposes.
- AI’s impact on the future isn’t pre-determined. Each of us can play a role in shaping how it turns out.
Let’s ensure we live in a world where many (not a select few) benefit from AI’s capacity and ability to improve lives, and that those responsible for its development, evolution, and application are held to fair and ethical standards.
Can AI be the rising tide that lifts all boats?
The power and potential of artificial intelligence technologies is clear, yet our ability to control it, and deploy it sustainably is not. Who should regulate and control it (and its fuel- our data) is an evolving and ongoing debate.
Used responsibly and applied democratically, we all stand to benefit from AI. Paradoxically, while it renders some of our old jobs obsolete, it retrains us for a new world where it and we play new and more rewarding roles – where living standards rise and mortality rates fall.
What’s our guarantee we’re marching toward that future?
Honestly, there are no guarantees – our world is devoid of certainty. However, we can influence likely outcomes by advocating for practical checks and balances. Call me a dreamer, but I envision a world where our privacy is valued and respected. Where we better understand the value of our data and get a reasonable exchange in return when we share it. Where we appreciate what happens when we release it, and can hold those accountable that illegally mangle or pawn it; and a world where we have assurance that when we share data, others uphold their end of the agreement, and we have recourse if they don’t.
If you would like to continue contemplating some of the top ethical implications of AI’s evolving story, click on this link:
Here’s my favorite quote from it:
“If we succeed with the transition, one day we might look back and think that it was barbaric that human beings were required to sell the majority of their waking time just to be able to live.”
When we’re on journeys, we often encounter decisive moments, and how fast we get accurate and relevant information and the decisions we subsequently make can have a huge impact on the outcome. Take, for example, one of your vacations. Your airline cancels a flight leaving you precious seconds to react and find other options before they evaporate; Or, if you’re upset with your wireless service and decide to reach out – calling to give them one last chance. Every case is different in terms of the people involved and the situational context, but timing and the flow of relevant information always plays a crucial role.
Time Is Of the Essence
If you’ve ever managed a project or worked with a project manager, one of the first things you learn is what’s called the triple constraints: the scope (quality), the cost, and the time (schedule) for a project. Project managers always manage a project using this idiom:
“No problem, I’ll deliver as long as I can control at least one of these 3 constraints. Deliver by Jan 1 you say. Fine it will cost X and we can deliver Y scope. Change the scope and the cost will go up. You want to hold costs to $50k, then we’ll deliver X scope by Jan 1. Change the scope and the date will slip.”
It seems lately in projects and commercial transactions we’re trying to break these laws. We want everything faster, cheaper, and more of it at higher quality. Why not, it sounds great. Although the quest for instant gratification isn’t new, it certainly has upped its game.
It’s obvious why we want things faster – because time is a scarce resource and finishing something can mean the difference between opportunity and obsolescence. Since we can’t pack more minutes into a day, we squeeze more tasks into limited time usually sacrificing scope / quality, and all in an attempt to push things to faster completion. With continual pressure to increase productivity levels, technology helps accomplish more with the same resources. Clearly, time is of the essence.
However, there is another reason time is so precious. Time is the enemy of information’s value to decision making. When something happens, information about it may be extremely valuable seconds after it occurs, yet through time that value decays (see Figure 1) at rates that are different depending on its importance to a given decision.
For example, when someone has a heart attack an enzyme called creatine kinase (CK) appears in the blood. Detecting the recurrence of that enzyme and passing that information quickly to a doctor could save that person’s life. Much later, that information may still be valuable in a post analysis of that patient’s situation, and years later, it could still prove valuable to researchers performing wider studies on congestive heart failure. Nonetheless, its value diminishes considerably over time, eventually relegated to but one data drop in a vast data lake, and ultimately some information even becomes a cost and liability with no corresponding value.
In CRM, real-time data can be of corresponding value and the context of the situation determines its value and decay curve. While on buying journeys, consumers come to decisive moments. When considering what consumers expect at those moments, brands must assume they are impatient, easily distracted, and likely to act suddenly. Once again, speed becomes paramount.
When Do Customer Interactions Need To Be Real-Time?
Honestly, there’s no such thing as real-time information. When an event occurs, time immediately elapses so, at best, a device can detect an event and pass that as information in near real-time to a decision maker. In some cases to be of optimal value, this information needs to reach the decision maker and the decision made – all in less than 1 second:
Given that understanding, what’s most important is that the information makes its way into a decision management system that can factor this into making a decision and in enough time to affect some other event that hasn’t yet occurred.
In our heart attack example, the presence of the enzyme CK is the initial event followed by some device that detects it, followed by that information passing to a decision maker who takes action to prevent a subsequent heart attack:
For CRM, a customer may be on the verge of switching service providers and the event may be one last contact with the existing provider:
Depending on the problem at hand, having real-time information and using it in a dynamic way may be critical to positively affecting customer experience and satisfaction.
In general, customers are less willing to wait for service or answers than ever before. When someone decides to research a potential purchase and browses a website, if it’s not responding nearly instantaneously, it’s likely the consumer will move on. Moreover, if the customer calls into a contact center and enters a long wait queue, again it’s likely the customer will balk. In other words, you literally have seconds to make the right impression and provide them with relevant content.
As we’ve just seen, the situations the events occur within, and how events relate to each other are crucial to understanding the full story. The circumstances that form the setting for one or more events are its context, and the data that comes from them contextual data.
Feeding this data into a decision engine can be essential so decision makers understand the full context of each event before making subsequent decisions.
Take another use case. Again, the event is a call coming into customer service. Here’s the setting for that event:
On a rainy day on May 22, 2017 at 4pm, while stuck in a traffic jam on 1-81 in southern VA, a certain customer (Mary) places a call to her credit card provider. Having just learned of late fees, she is not happy. She’s a loyal customer of 23 years who always pays on time and her aim is to get these fees removed.
Consider some of the contextual data in this scenario:
- Historical background of customer – e.g., family history, purchase history, engagement history – i.e., knowledge of this customer as an individual (23-year loyal customer – Mary)
- Date / Time (May 22, 2017 at 4pm Eastern)
- Location / Proximity (northbound on 1-81 at x lat / y long)
- Environmental conditions – e.g., weather / traffic (raining – stuck in traffic)
- Preferences (uses phone to call in)
- Emotional state (unhappy with fees; may be further agitated by weather & traffic conditions)
- Journey stage (just received bill and is inquiring about fees)
- Current intent / agenda (aims to get fees removed)
Think about how important each piece of information is to the scenario and how all of it, when factored together, forms a more complete picture. For instance, if the call started at 4pm and the conversation with Mary is still in progress at 4:30pm, it’s likely she’s antsy and probably tired from a long day. Now combine that with the weather, traffic conditions, journey stage, current intent, and historical background, and now it’s very likely her patience is wearing thin.
In short, situational context answers these questions about an event.
Brands that can factor in this kind of contextual information are more likely to address immediate sensitivities and handle customer interactions accordingly. Human agents, unlike artificial ones, have the potential for very high levels of emotional intelligence (EI) [i]but still need assistance compiling the information and making sense of it. So far, artificial intelligent (AI) bots aren’t that advanced in EI and humans have the edge in relating to people, applying soft skills, and making complex judgments. Thus, it’s crucial to realize the full situation when designing systems that will include both self-service options and escalation to humans, and when humans are involved, providing them with augmented information so they can serve customers better.
In this scenario, as with most others, the contextual information becomes practically useless the next day. Unless the systems involved can detect these events and the context in near real-time, and pass it on, the value of this information decays rapidly. Imagine the customer service person saying, “If you just call me back tomorrow morning Mary, my system will run an overnight batch job, and I’ll have a sentiment and value score for you so I can help you better” – said a successful customer service person never.
And systems can’t pre-calculate conditions or recommendations either, hoping that works. How would you rate a pizza delivery company if you ordered a pie, and delivery came in 15 minutes (a near real-time pizza order), but they cooked it the night before?
One Contextual Brain Making Real-Time Decisions
When businesses engage with customers – or vice versa – such as in the examples given, they’re expecting an ongoing conversation regardless of the initial channel. In some cases (such as a phone call), the exchange begins and ends on the same channel, with stimulus and response occurring dynamically and repeatedly. In other cases, such as a website visit or chat session, conversations may take longer, and experience periods of pause, delay, or even happen over multiple channels and sessions.
Unfortunately, many brands treat each interaction as an isolated transaction instead of viewing them as parts of a continuous conversation that builds toward a deeper relationship. When viewed from the latter lens, each interaction nudges the relationship in a positive or negative direction, in effect adding to a “Good Graces” balance, or subtracting from it (see Figure 2). Clearly, the goal of any customer-centric minded organization is to evaluate each interaction’s contribution, monitor this balance, and work to strengthen the relationship – improving it with each engagement.
This is more than a loyalty point balance, an NPS score, or a sentiment score. It’s more like a credibility score, measuring the overall standing your brand has with a given consumer.
Consumers reward companies with more business when they provide convenience and simplicity, can be trusted, and help improve their lives. Amazon is a prime (pun intended) example. Consider the Amazon Dash button, which really isn’t the revolution in buying experience originally promised, at least not yet. This is the internet-connected button you place, for example, on your washing machine, and you push to reorder detergent. However, many customers purchase the button (for $4.99, which comes with an equal credit upon the first purchase) but never use it. They just love Amazon, trust them (i.e., they have a high good graces balance), and go for their innovative ideas – and this idea smacks of convenience, simplicity, and real-time engagement.
Customers also keep score in terms of brands that just get it. What this means is buyers notice if they get superior, well-coordinated services, regardless of the channel, time, location, or representative involved. For a business to do this, it must have a culture that oozes of a customer-obsessed mentality and have systems that support its people in providing outstanding services at scale. If those systems are in silos (see Figure 3), each commanded by a different brain, achieving that goal becomes impossible.
Can Anyone (Or Any System) Do This Really Well?
The short answer is yes, companies should be able to do a decent job of this, but they aren’t. Amazingly, a DataStax commissioned Forrester survey of 206 organizations across four major countries revealed, “95% of organizations are currently unable to make sense of customer data, and struggle to gain real-time insights from this data.”
When agents serve customers, to provide great service, they need technological assistance just as a pilot needs instruments. Flight deck instruments must depict actual conditions, with little latency, or they’re useless. Finding out too late that your altitude is 2000 feet could spell disaster. When on chat or a call, consumers expect immediate responses to each question and have a threshold for the total time they’re willing to devote. In theory, whether the agent is a bot or a human makes little difference, what matters is whether the agent provides authentic empathy, good judgement, relevant responses, and resolves the situation quickly.
Today, offloading some of this to machines is possible (sensing conditions, detecting patterns, managing decisions, and triggering actions), but seemingly only with CRM rocket surgeons behind the curtains.
That shouldn’t be the case, and it’s holding back brands from providing exceptional customer experience. Some vendors, nevertheless, are working to change that and provide tooling that allows for real-time interaction management or RTIM[ii] controlled by business users. Though the right tooling alone won’t bring success, as firms must align data, organization, and processes also, choosing the wrong technology will introduce barriers and rigidity that will be difficult or impossible to overcome.
When considering the entirety of the data, insights, and technological solution needed, keep in mind that each vendor has evolved their platform from very different starting points. Some have cobbled together solutions by acquiring smaller firms and in fact don’t even have a single platform. Others have built out their solution, appending capabilities to legacy outbound and batch oriented architectures (often based on email campaign blasting), making it nearly impossible to handle enterprise real-time processing requirements. It’s likely you’ll hit a wall (see Figure 4) with these types of solutions in attempting to transform to real-time 1:1 contextual engagements.
Thus, look instead for help from partners that have extensive experience with real-time customer interactions and have built solutions from the ground up to handle the complexities of real-time data wrangling, dynamic analytics, and scaling for the instant gratification response times required by today’s customers.
If you have a smartphone, laptop, tablet, or all of the above, it’s no longer a question if you’re addicted to being online, it’s a question if you recognize the problem and are in control of it. And if you don’t have any of these devices, I’d love to meet you and even shake your hand (since you’ll actually have one free) – before you become extinct that is, since your breed is already on the brink.
Why are we so addicted to technology? Perhaps there isn’t one simple answer, but an emerging hypothesis mainstreamed recently when CBS ran a segment on 60 minutes titled “Brain Hacking.” If you haven’t seen it, it’s worth a watch. Not surprisingly, yet ironically, to get to the replay, CBS forces you to watch ads.
In the segment, Tristan Harris, a former Google employee, refers to our digital devices as slot machines, and in a recent blog claims, “…technology hijacks people’s minds.” Ramsey Brown, co-founder of Dopamine Labs, calls the developers responsible for making the apps we use everyday “brain hackers” – essentially meaning they employ techniques designed to get us hooked and to alter our behavior. Arguably, these ubiquitous methods have already succeeded in causing habitual behavior. And if you’re not convinced, unglue your eyes for a moment from your device du jour, peer up, and notice everyone else’s heads buried in digital appliances, and how antsy they’ll get when unplugged for only a few minutes.
Essentially in concept, it’s similar to a virus invading the body (hence the hacking metaphor), with its mission to reprogram us to crave constant online activity. Behind each antigen are hackers, engaged in a form of biological warfare, engineering their payloads to infect our brains to crave more activity – on their sites and apps. Though it’s debatable how much of the altered conduct can be directly attributed to just a few hackers, the pervasiveness of the behavior is indisputable.
Race to the bottom of the brain stem
Tristan refers to this battle as a “race to the bottom of the brain stem,” implying these cerebral hackers are sparring for our attention, and will do anything to get more of it by appealing to a range of our human needs, even the most primitive ones.
So, are all CX pros guilty of this practice, or just a select few in Silicon Valley? In terms of marketing techniques, is this something new or merely old methods with new names?
Perhaps guilty isn’t the fairest word (with its implication of wrongdoing), but I’ll posit the first answer is yes, most professionals tasked with generating demand are trying to do this – with the huge caveat that some think (operative word “think”) they are close to solving an ageless puzzle of how each human mind operates and how to manipulate it. Further, it’s not the impact of a few, but instead the collective efforts of many demand generators, as well as our growing dependence on technology, that’s contributing to our hyperactive online behavior.
Nevertheless, today only a few dominant firms enjoy the majority of the economic rewards, since users spend the majority of their online time in select applications such as Facebook and Google. Call this the tech titan factor, a few gargantuan companies controlling the vast majority of user interactions that attract eyeballs to digital advertisements.
As for the novelty of this approach, the core practice is actually as old as direct marketing itself. Like doctors, trained direct marketers learn early on that diagnosis, problem understanding, and treatments followed by continuous application of test & learn methodology, are time-honored principles proven to attract attention and optimize engagement. What’s changed are the tools CX pros have to administer continuous and tailored therapies (see my article on the use of Prescriptive AI in CX), and as consumers how we’ve unknowingly given up more data about ourselves and increased the quantity of our online intake. Further, this medication being administered to us comes with no warning labels or explicit documentation as to the harmful side effects.
In terms of affecting consumer consciousness and behavior, marketers have again followed long-standing hard and fast principles. Take Maslow’s hierarchy of needs:
The reward consumers get from checking their devices depends on the individual’s specific needs. For instance, one person may be in the pursuit of self-actualization, and as such may be constantly using a gadget for educational discovery. Another may be in search of esteem, and becomes hooked on social media in a constant quest for recognition. No matter the reason for being online, advertisers track, analyze, and subsequently prescribe remedies squarely aimed at selling us goods and services they’ve ascertained we need.
Moreover, take the streaks tactic that Snapchat uses. This is simply age-old marketing gamification at work. It’s true today’s games are digital, more dynamic, mobile, and played by all ages, but S&H had consumers playing very similar marketing games in the 1930’s, with the goal of creating green stamp junkies.
Whether a modern game, or a game from the 1930’s, the basics of this approach are similar. Entice someone to play but don’t let them win out (making the game incremental – in this case the increment is days), luring them back in, and make the game length seemingly infinite (e.g., collect stamps; cash them in; collect more).
The persuasive fight for our attention
Have newly minted CX pros devised new sinister methods of mind control? Has a new economy suddenly emerged centered on getting attention at all costs, hooking people into using products? I don’t think so.
As far back as the 1950’s, fears about mind control perpetrated by marketers were already spreading, and various theories, many of them hoaxes, began to crop up. And the commercialization of everything, from historical sites to holidays, can be traced back to right after the American Civil War.
In the late 1950’s, rumors abounded such as stories of theaters lacing film with stealthily implanted single frames of subliminal messages such as “Eat Popcorn” and “Drink Coke” supposedly engineered to stimulate instantaneous demand for these products. In 1957, Vance Packard wrote a groundbreaking novel titled, “The Hidden Persuaders,” making an original argument that organizations are born to manipulate, and had moved from overt tactics to clandestine ones, with hired agencies as the evil genius behind it all. Quite possibly the only difference today is that we carry around in our pockets millions of commercials, and check in constantly, making us continuous targets for impressions. Unscrupulous marketers, as well as those with shreds of decency, have existed side by side since the dawn of time. They simply have more access (by virtue of over 150 years of marketing, commercial, and technological evolution) to more minutes of our waking attention, and will always vie for a slice of that bandwidth with newfangled material engineered to break through the clutter.
When you reflect on it, marketers seek attention and puff their wares – it’s what they do. How and where they’ve sought it and how much they’ve puffed has always defined the extent to which they further commercialize our environment and how far they push ethical and legal boundaries.
This fight to own a share of our precious attention itself contributes to a further lack of focus and increased distraction. I wrote a related piece on this (Contextual Incremental Marketing), from the point of view of the marketer, at the time not fully grasping that my tips about the phenomena were in fact recursive, that is, further reinforcing and encouraging the behaviors that I suggested were simply a facet of the modern world.
In a sense, it’s a vicious circle, but not a new one: CX pros stalking consumers, contending for their attention via an ever-exploding channel continuum, employing any means to engage their reptilian brain and interrupt them, persuade them, adding to attention deficit disorder. For consumers, the antidote is the same as it ever was – common sense, education (with reliable and readily available sources of accurate information), balance and moderation, free will, and self-control.
The surveillance economy
Like crime scene investigators (for more on this, read my blog: The CSI Guy – Customer Success Investigator), CX pros seek clues to solve the mysteries of making best guesses about the likely behavior, needs, and actions of customers.
Those involved in pure acquisition have little to go by, and as such, stretch for data and surveillance methods, test data privacy, ethical, and permission boundaries, and often still miss the relevance mark. In many respects, they are like matchmakers, casting a wide net, and hoping to bring in a few choice prospects. Those tasked with building on relationships, often called relationship or loyalty marketers, have it easier, with a treasure trove of owned media behavior data at their fingertips collected by modern digital tracking sensors. In either case, it should come as no surprise that gathering evidence is a top priority.
In 1992, Eric Lawson wrote a book called “The Naked Consumer.” It was an excellent account of the growing problem at the time of personal data sold as a commodity on the open market, and its lessons and conclusions are as germane as ever.
So what should we do?
Like any history, there always seems to be the appearance of it repeating itself, but invariably with evolving twists. In this case, some of the twists are:
- We can take devices with games and reinforcements anywhere, and often do. Mobility means more chances to be online. In contrast, when TVs first appeared, they were stationary. And radios were too bulky to carry, until transistors transformed them into the iPod of the 50’s.
- Because this digital drug is available constantly, and there are no official regulators, many of us are unconsciously overindulging. Like any addiction, step one is problem recognition, and for most of us, we haven’t admitted there’s a problem, let alone embarked on a recovery journey.
- For digital natives (those who have grown up with smartphones and social media), there are new pressures and social dynamics many of us that are older can’t fully appreciate. This has resulted in massive numbers of teens afflicted with anxiety and depression (see this Time Magazine article for an in-depth look). That’s sad. There’s no easy answers, as these issues are rooted not only in technological realities, but interwoven with deep seeded tribal sociological phenomena.
- Impatience thresholds are down to seconds, partly due to the availability of technology itself and our dependence on it, and on industrial productivity pressures.
What should CX pros do?
There’s no disputing that businesses need customers and have to make money to survive. How they play the game, the rules they follow, and the cultural approach they use defines both their character and destiny. When plotting how to engage customers with artificial intelligence and automation technology, consider the following:
- Those who play the long game win the long game. If the ultimate goal is improving customer experience, then factor customer quality of life into the long-term value equation. Depending on the definition and time horizon for winning, chances are good consumers will recognize (and reward) you for considering their best interests.
- Regulate, or be regulated. Incidentally, industry in general doesn’t have a great track record for self-regulation, so prepare eventually for some regulations in this area to emerge.
What should consumers do?
Throughout recorded history, hucksters have been selling unsuspecting consumers products they really didn’t need. That doesn’t mean every modern day CX pro inherits the label of huckster.
Quite the contrary, those who exchange value with consumers, and provide them with solid recommendations of products well suited to their requirements are effectively service providers. Those taking the easy path and simply pushing and deceiving others toward a clever sale, will rightfully earn the dubious timeworn label.
Consumers need to:
- Shop around. Although it can be a hassle, weigh the pros & cons of moving to another provider, versus amassing more points or transactions with a single provider. Be sure, nonetheless, to factor in all switching costs, including your time.
- When you shop, think outside the box to get a list of alternatives. The path of least resistance these days is to search on Google, but that list of both the paid results as well as the first page of organic ones is a limited (and often highly biased) set.
- Take occasional breaks from technology – Simply put, you don’t need to be online every minute. Don’t expect to completely kick the habit, same as you can’t stop eating food altogether. Research already shows, however, you should use technology in moderation or your long-term health may be at stake. A recent survey of 3500 adults shows stress levels likely rise when alerts go off, such as new emails or text messages. Like getting adequate sleep is necessary for good health, you’ll probably be more productive (and live a longer, heathier life) if you’re offline periodically.