What it Takes to be a CX Transformer

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.

CX Transformer

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Experiential assets, originally made from scratch, must evolve to the liking of their benefactors.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:

CX Talk vs Walk

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.

CX Transformer

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.

Value Matrix

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

[iii] Infosys, https://www.infosys.com/newsroom/press-releases/Documents/genome-research-report.pdf, 2013

Revolutionize CX with Real-Time Contextual Engagements

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.

Figure 1:

Information Value

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:

Real-Time Events

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:

Real-time events

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:

Real-time CX event

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.

Situational Context and Relevance

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.

Situational Context:

Situational Context

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

Real-time brain

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.

Figure 2:

Loyalty model

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.

Figure 3:

Broken CRM

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.

rocket surgery

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.

Figure 4:

real-time 1:1

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.

[i] https://hbr.org/2017/02/the-rise-of-ai-makes-emotional-intelligence-more-important


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



Are CX pros guilty of brain hacking?

Brain Hacking

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:

Maslows 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.


10 Commandments of Customer Experience (CX)

CX 10 Commandments

This is my shortest post by far.  I received these 10 CX thoughts last night in a dream.  When they came unto me, they seemed self-explanatory, so I saw no need to elaborate or provide examples.  Full disclosure; I did have several adult beverages before I went to sleep:


  1. Strive to know your customers as you would know yourself.


  1. Thou shalt be “Customer-Centric” and put no other products, services or stakeholders before thee.


  1. Thou shalt not make any graven image of customers, such as idol segments. Instead, thou will treat customers as individuals with personalized touch.


  1. Thou shalt not spam customers by carpet bombing with frivolity (causing them to take names in vain).


  1. Thou shalt not contact customers on Sunday…or any day for that matter, unless given permission and there is a relevant service or offer to discuss.


  1. Thou shalt be empathetic and listen to customers, and act with fairness.


  1. Thou shalt not kill off customers with WMDs – “Weapons of Math Destruction” – such as artificial intelligence (AI) algorithms with bias.


  1. Love thy customer, their loyalty, and their journey, and calculate a true LTV (Lifetime Value), not just a year’s worth.


  1. Thou shalt not steal profits from the Customer Innovation Till. A tithe of earnings will be put in said till for pursuing true innovation.


  1. Thou shalt not covet thy customer’s wallet or share of wallet. You will get yours if you obey the other commandments.

Event-based marketing (EBM) & CEP use cases for CRM

Updated: February 3, 2020

Introduction – The Key Event Types

First, check out my latest article on this topic recently published: Shush – Listen for customer signals with event-based marketing & service —- Out of that article surfaced these distinct event categories:
Event Category Example
Account status Average bank account balance trending down (by X standard deviations)
Behavior-Account Roaming charges incurred or within x% of the limit
Behavior-Device Failure of device/machine
Behavior-Person Digital browsing – showing purchase interest/intent
Calendar Major shopping holiday approaching
Contract-Account Changes in the account/contract terms & conditions
Environmental Severe weather alert – hurricane warning
Forecast Model score updated – Churn/attrition score rises above a threshold
Inactivity No activity (of a certain type) in the last 30 days – e.g., no deposits
Law/regulation Change in the overall privacy policy
Milestone Birthday – Age changes (milestones such as 18, 55, 65, etc.)
Product/Service Replenish – consumable products, such as printer ink
Product-wide/Service-wide The interest rate on all accounts of type X increases by x%
Profile-Person Investable assets increase (or decrease) by x% (or crosses a threshold)
Transaction status Order status change (disruption in availability, timing)
Below you’ll find an inventory of event-based marketing (EBM) and complex event processing (CEP) use cases for customer experience management.  In each, the system senses behavior and alerts a user or another system to the unusual activities or conditions that warrant further investigation or action.

Vertical Complex Event Processing Use Cases

Fiserv: Consumer Banking and Credit Cards

  • Unusual account activity (e.g., large deposits/withdraws)
  • Unusual account activity trend (e.g., average daily balance down by two standard deviations)
  • Inactivity pattern (e.g., no transactions in last week)
  • Missed transaction (e.g., missed direct deposit)
  • Credit card spend activity use pattern (by spending category)
  • Insufficient funds pattern
  • Web or mobile click activity indicating an interest in a product


  • Fraudulent claims activity


  • Predictive maintenance systems

Media and Communications

  • Dropped call pattern or degradation of signal/service
  • A customer has increased roaming (or other unusual account usages) behavior
  • Customer in route to a foreign country pattern
  • Popular programming based on a set-top box and social media insights
  • Prepaid consumption detection and stimulation
  • Churn detection


  • Claims fraud
  • Care interruption pattern
  • Fitness monitoring
  • Hygiene procedures pattern
  • Healthcare patient monitoring

Horizontal Complex Event Processing Use Cases

Customer Service Center / Retention Department / Loyalty

  • Customer struggling to get help pattern
  • Payment due
  • Strange returns activity
  • Customer likely wants to cancel service
  • Customer’s birthday
  • Customer’s service anniversary (e.g., been a customer for X years)

Marketing / Cross-sell & Up-sell

  • Customer online interest in a product or service
  • Customer in store interest in a product or service
  • Customer in the proximity of a store
  • Customer usage stimulation – Drop off in use of a product/service
  • Increase in use of a product/service
  • Loyalty Program – Monitoring points activity
  • Loyalty Program – Monitoring points expiration date
  • Monitoring social sentiment
  • Monitoring social influencer

Non-CX use cases

Here are some examples that are not for CX, but instead to improve business and operations efficiency:
  • Algorithmic stock trading such as if Stock A rises by X% and Stock B doesn’t automatically buy Stock B
  • Transportation security and fraud detection such as an id card used twice in a short time frame (e.g., piggybacking) or high volume transactions on a new account – and then automatically alerting the right parties
  • Detecting transportation congestion and incidents, and proactive notification of alternative routes
  • Inferred detections suggesting that a vehicle has crashed (and severity of crash), such as when an airbag has deployed
  • Communications security such as false alarms going off in a certain time window, not followed by other alarms that would be expected (false positive alarming)
  • Communications security such as network monitoring for detecting denial of service attacks, and alerting the right parties of this situation

Customer Engagement – From BI Guesswork to Prescriptive AI

Customer Engagement approaches, and the technology used to enable them, have evolved immensely over the last 25 years.  Two distinct eras define this period, as well as a major technological shift to real-time systems with AI feedback loops.

Prescriptive AI

The BI Guesswork Era

During the advent of the Business Intelligence (BI), Marketing Technology and Campaign Management era (circa 1990), marketers had limited predictive powers.  In many cases, when it came to what individuals really needed, they resorted to guesswork.  They channeled their energy to perfect efficiencies in targeting and automation.  Their main emphasis was finding an approximate audience for products so they designed promotions for large segments of the population. They fixated on finding segments that fit into certain “likelihood to respond” buckets, and then repeatedly tested timing, messages, and creative content by peppering those segments with treatments.  In other words, they identified massive groups, matched offers to these groups, and then used technology to systematize their marketing.

Although some of those marketers drew on basic models (such as RFM – Recency, Frequency, Monetary), which provided rough guidance on how deep to mail into a file, most didn’t even do this.  Typical response rates were 0.5% at best.  During this period, the average adult was receiving about 50 pounds of junk mail a year – coined junk mail because the promotions were irrelevant 99.5% of the time.  Thus, the majority viewed this activity as frivolous, mocking it with nicknames and jokes.  Regardless, marketers were unrelenting as they continually carpet-bombed until consumers either responded or learned how to opt-out.

Their tools of choice were crude in nature.  They were slow, not fine-grained, and certainly not customer-centric.  Usually, the campaign flowcharts they devised utilized basic analytics where deterministic queries ran against databases returning huge customer lists called segments.  If there was any further segment refinement, they relied on business intelligence technologies like OLAP (Online Analytical Processing) and dashboards to support their intuition.  Even as some of the more sophisticated marketers attempted predictions, providing those models with feedback was nearly impossible due to the batch processing nature of the flows and platforms they employed.  As shown in Figure 1, although some crept up the analytics value chain toward being predictive and answering the question “What will happen?” most fell short.

Figure 1:

business intelligence

Source: http://www.bi-bestpractices.com/view-articles/5642

Using a backward approach, engineers pre-developed the product, and marketers wrangled the packaging, promotions, and messaging to the audience – again using more guesswork than analytics.  It was difficult to react contextually, at scale, to actual individual needs, so instead they focused on groups of customers.

And so they executed bulk outbound communications at scale. With promotional ammunition in hand, readily available data afforded them reasonable targeting coordinates, and computers and devices served as the delivery mechanisms. The marketplace and emerging technology supported a numbers game and rewarded short-term economic gains.  Longer-term loyalty and longitudinal effects took a back seat.

By the turn of the century, direct marketers were plodding ahead using ever-richer consumer profiles that enabled them to focus promotions on increasingly smaller segments.  And even though in 1995, Peppers & Rogers had coined the term “1:1 marketing,” enterprise marketers were no where near direct conversations with individual consumers.  Still constrained by scale, they were stuck communicating to segments, albeit smaller and smaller ones.  What they didn’t realize was they were about to hit a wall (Figure 2)

Figure 2:

Real-Time Evolution

By 2005, marketers had the tools to perform hyper-targeting.  They aggressively tested different incentives, creative elements, and fine tuned things based on response metrics.   Scoring models were refined, though the expense was large, and the iterations long.  The results didn’t so much alter someone’s behavior, but more provided alternatives to consider, often ones that still had borderline relevance to a current need.

Often the goal, instead of steadfast loyalty, was simply to increase immediate purchases with minimal marketing waste.  In theory, if targets responded and steadily purchased, no matter the purchase, more purchases should follow.  Supposedly then, over the long haul, the business accomplished its goal of capturing more share of wallet.

Around 2010, some leading edge marketers who realized the value of a real-time approach, began hitting that wall.  The foundation of the system they had spent 15 years building was the wrong foundation.  It was a platform built for segmentation, and it supported the wrong approach. They needed a “Real-time 1:1” platform, customer-centric prescriptions, and a more dynamic feedback loop.

Enter the Prescriptive AI Era

Good marketers have always been similar to psychologists in that they study consumer behavior. With today’s data and technology, it’s possible to take engagements one-step further – diagnosing, and treating those customers to alter their behavior methodically over time.  Stealing a page from the broadcast advertisers’ playbook – who use “subliminal seduction” – many marketers are marching toward implementing systems that use incremental and proactive drip therapy to persuade inner minds toward brand myopia.

The only piece missing from the puzzle is a real-time platform.  Traces of this began appearing in 2010, as big data systems, parallel computing, solid-state storage, and other technology advances drove computing costs radically down, and speeds up.

Today the pieces are in place, and more are climbing aboard, as real-time platforms have fully emerged and are cheaper and more reliable.  It’s now feasible to use customer-centric prescriptive tactics at scale and get huge lift over baseline approaches.  Models can predict behavior to an amazing degree of accuracy.  The artificial intelligence (AI) models both diagnose and – using Decision Management – proactively prescribe next-best-action engagement treatments.

Figure 3:


Everyone knows engagement professionals today have more channels.  They’re no longer constrained to broadcast media delivery systems (that lack dynamic feedback loops), and can now use digital response media and even physical surveillance.  And with this plethora of channels, they can administer and perfect personalized, contiguous, and hypersonic stimuli-response strategies.  Essentially, they can employ an always-on brain, powered by rich consumer data, advanced machine learning algorithms, and a 24 x 7 continuous learning loop.

What’s more, these machine learning technologies and embedded predictive algorithms can work in a very deliberate and intelligent way, dynamically creating conditional content and promotions, each time consumers reengage on a digital channel.  Incremental repeated responses (or lack thereof) allow these models to learn, tune themselves, and in essence direct and alter the future – programming individual behavior.  Customers are enticed to reveal ever-increasing amounts of personal information, in exchange for points or some privilege, trusting the exchange is amenable, and the information use one-dimensional.

All of this behavioral activity – social, purchase, demographic, and so forth – is recorded, with the aim of feeding it back into those same algorithms that iterate to find new patterns, refine predictions, and subsequently inform Decision Strategies that recommend the next series of treatments.  In some cases, these systems can even run autonomously, using advanced data science techniques such as genetic algorithms, game theory, and reinforcement learning.  System designers seed the rules of the game, configure the objective function and constraints, and then push “Go.”  The designers and their business counterparts peer in on occasion to monitor whether goals, such as higher loyalty and profit, are trending in the right direction.

Figure 4:

AI Learning Loop

Although this suggests overt manipulation, it’s not necessarily malevolent.  Provided customers have choice (and are well informed and discriminate), and businesses operate ethically (on a level playing field), the economic scales can still balance, and brands that provide products and experiences with the best value can still prevail, and consumers get a fair exchange of value.  You may have noticed, however, a few important “ifs” in this last statement.

Whether we like it or not, we now live in the Prescriptive Era, where the mission of brands is to get to know us, maybe even better than we actually know ourselves. That might sound crazy, but consider this statement from a recent article, “The Rise of the Weaponized AI Propaganda Machine” [i] where an analytics firm compiled data on Facebook likes and built millions of consumer behavior profiles, subsequently fed into an AI political campaigning machine:

“With 300 likes, Kosinski’s machine could predict a subject’s behavior better than their partner. With even more likes it could exceed what a person thinks they know about themselves.”

Whether you buy this or not, the fact remains that consumer profiles are becoming richer and consumer behavior predictions more accurate.  Data are exploding, as are the algorithms voraciously feeding on them.

Brands compiling this data and wielding their algorithms do it because they say they want to know us better.  Presumably, this enables them to continuously add value, deliver insights, help automate our lives, and make attractive recommendations.

Ostensibly then, for consumers, it comes down to a few simple questions:

  • How much is our data worth to us?
  • What’s the value of the insights that brands provide when they use our data?
  • Are we getting an equitable exchange?
  • Can we trust brands to honor their commitments regarding the use of our data?
  • Do we understand the fine print in those agreements?

Consider the mission statement for Datacoup, a data company based in New York, who have gone one step further and are trying to make a marketplace where consumer’s have a more direct exchange of value for their data:

“Our mission is to help people unlock the value of their personal data. Almost every link in the economic chain has their hand in our collective data pocket. Data brokers in the US alone account for a $15bn industry, yet they have zero relationship with the consumers whose data they harvest and sell. They offer no discernible benefit back to the producers of this great data asset – you.”[ii]

So are you getting value for the data you’re giving up?  Are the “Prescriptions” you get in return an equitable exchange?  Are you aware of what happens to your data after you release it?

A Day in the Life of Your Data

We all joke about the eye-glazing 56 page “Terms and Conditions” from Apple that we always accept and never read.  We want the free software, and don’t worry about the consequences. However, if you use that approach for everything you do online, that mindset is dangerous.

Consider this for a moment.  Most firms have language that allows them to send your data to affiliates, which is a fancy word for other companies. Once floating in the ecosystem, it’s grinded, distilled, and appended to other copies, until records of your preferences, habits, and behavioral are expressed in 5,000 or more different ways.  If it’s wrong, it doesn’t matter, because you don’t own it, don’t have access to it, and can’t change it.  In many ways, it’s another version of you, right or wrong.

Is Prescriptive AI Working?

So back to the question of whether it’s helping.   It’s fair to say there are cases where it adds value.  Here are some examples:

  • You decide you aren’t satisfied with your telecommunication services. You’ve made it obvious (with various signals) you’re considering other alternatives.  Your current provider prescribes an attractive bundle that satisfies your needs. You get a better bundle of services, and your provider retains you.  The bundle is custom tailored for you, using AI.
  • You have investments with a firm. You provide additional data on your financial goals, risk tolerance, and other investments, and they provide advice (prescriptions) on how to achieve your goals over time, within the parameters you set.  They provide various alternatives and education that prove useful to your financial planning.   Presumably, some of those alternatives include additional investments with them, and turn out to be good choices.
  • Your health plan suggests meaningful diet, exercise, and other tips that promote a healthy lifestyle. They are custom tailored to you, based on your family history, age, and other personal data you provide.   They reward you with lower premiums or credits.

These are just a few examples, and many more exist across industries such as travel and leisure, automotive, insurance, and retail.  And while good exchanges do exist, there are plenty of examples where the prescription doesn’t justify the information surrendered because the value exchange is unbalanced, or the prescriptions are ineffective.

Final Thoughts

In her book, “Weapons of Math Destruction[iii],” Cathy O’Neil writes:

“Many of these models, like some of the WMDs we’ve discussed, will arrive with the best intentions.  But they must also deliver transparency, disclosing the input data they’re using as well as the results of their targeting. And they must be open to audits. These are powerful engines, after all.  We must keep our eyes on them.”

She highlights important considerations we must heed.  I’m not convinced we’re spiraling toward a dystopian society regarding the use of prescriptive AI for customer engagement, but I do believe a balance is necessary between efficacy of these systems and fairness.  As responsible marketers, we should be mindful of the ramifications of the models we use for prescriptive purposes, and as consumers, it’s our job to demand transparency, choice, and a level playing field.

[i] Anderson And Horvath, https://scout.ai/story/the-rise-of-the-weaponized-ai-propaganda-machine, January 2017

[ii] Datacoup, https://datacoup.com/docs#faq, February 2017

[iii] Cathy O’Neil, 1st edition, Weapons of Math Destruction (New York: Crown), 2016.

3 Tips to Drive Business Value with CX – Fortified with REAL AI

With a first name of “Artificial,” AI has certainly entertained us with its virtual possibilities.  Stories of wholesale disruption by robots and fully automated lives make for good movie material, but as of yet, AI hasn’t dominated the marketplace, consumer experiences, or business applications in a monumental way.  AI has the potential to change our daily lives, yet for most, its impact so far has been nominal.

Real AI

As a businessperson concerned with driving better customer engagement, you’re no doubt interested in this topic, yet probably carry some healthy skepticism about the potential for return from your AI investments, and the risk of them failing.

Congratulations!  Your suspicion is not only natural, it’s warranted.  Here are three tips for how to maximize value from your AI investments, and minimize any risk of disillusionment.

1.    Provide predictions about Customer Intent

No doubt, you have scores of business intelligence systems that compile and codify data.  They provide customer profiles, program dashboards, and other scorecard reporting of historical results.  Although informative, these systems aren’t predicting anything.  As such, they are rear view mirrors, providing a view of the past, but not anticipating and generating ideas regarding courses of action that may lead to more optimal outcomes.

Any investment in AI aimed at improving customer engagement must include capabilities to predict customer motivation.   Why are they calling?  Are they already upset?  Are they highly likely to be shopping for another provider?  What product or service best suits their true needs?  How valuable is this customer over their projected lifetime?

Answers to these questions are always guesses, yet pragmatic AI systems today use proven statistical methods to minimize errors in predictions, calibrate themselves with feedback loops, and provide confidence intervals so users understand their range of applicability.

For example, it’s feasible today to have a portal providing your marketing employees with accurate predictions such as:

  • Customer value
  • Churn likelihood
  • Loyalty to brand


For service agents, predictions like:

  • Customer sentiment
  • Reason for calling
  • Nature of problem


For sales personnel:

  • Price sensitivity
  • Available budget
  • Perception of value


Effective AI has to improve your ability to understand what impels your customers to behave the way they do, or the way they may act in the near future.  Work backward from these insights, and demand that your AI systems and vendors can prove they have experience extracting insights from available data, and in predicting and surfacing these items.

2.    Make dynamic suggestions to better serve the Customer

Consumers do business with brands that provide repeatable value.  That value comes from not just positive product use, but also from an enjoyable and smooth buying process, a friendly and efficient on-boarding experience, and stellar service.

As consumers experience a brand during those journeys, they rack up the score, keeping tally of the relevance and effectiveness of the systems and people they encounter along the way.

Any AI system worth its salt should provide ranked suggestions either directly to customers, or to customer facing employees such as:

  • Next Best Offer: The most relevant product needed, and an individualized incentive on it that will be both compelling, yet still economically affordable to the business.
  • Next Best Service Action: The best thing an agent can do next to maximize the chance of reaching an effective and efficient solution to the service problem at hand.
  • Next Best Sales Activity: The best action for a salesperson given available leads, accounts, contacts, and opportunities.

For the marketers responsible for providing next best offers, AI systems should help them recognize buying patterns, automatically perform tests, filter out offers that don’t apply, and statistically rank the best content & promotions for the right individuals.  AI should even suggest the best timing for those recommendations.

For service workers, AI should deflect routine service requests to automated or self-service channels, guide agents on complex service cases, surface potential solutions to issues, and help gauge the sentiment of the customer during the process.

For salespeople, AI should predict the best contacts to engage with in an account, the activities most likely to move an opportunity to the next sales stage, and which accounts to spend energy on to maximize close rates and quota attainment.

3.    Install a system that learns in Real-Time

Your world changes every day.  As a professional, you wake up every day to news of competitive threats, new opportunities, and market conditions that vary the effectiveness of the strategies you employed yesterday.

If you were slow to react, or simply ignored these factors, you’d fully expect your overall business performance to degrade, so you listen carefully to these environmental conditions, and you adjust accordingly.

Think about your AI systems the same way.  They must include adaptive mechanisms, where recommendations made are monitored, in real-time, and dispositions are fed back into the machine, so it can learn from its success and mistakes.   Marketing, service, and sales systems receive feedback constantly in the form of customers either ignoring your treatments, or responding to them, so ensure your AI system uses them.  Your AI system should rapidly improve its performance, as it’s fed more data, and as it tunes itself.  If it’s not, after a short trial period, start asking some hard questions to your provider.

Make sure your results (even if delayed), are monitored, measured, and understood. An accurate measurement of the real business value from AI comes when you understand the baseline, and can measure the lift you get when you employ the insights and recommendations delivered by AI.

Track response rates, conversion rates, incremental revenue, return on investment, and compare to what your vendor promised, what you expected, and what you need to achieve.

AI is a broad topic, yet to improve customer engagement and your outcomes, boil it down to these 3 things; understand customer intent, make relevant suggestions, and learn in real-time so your performance improves over time.   If you do these, you will realize REAL value from AI.

AI in CX: Real or Superficial Intelligence?

Artificial Intelligence

By all accounts, 2017 has ushered in the dawn of the newest Artificial Intelligence (AI) era. Most technology hype cycles follow typical paths, quickly shooting up, often followed predictably thereafter by a meteoric reentry to reality.  Typically, the entire flight takes place over a decade or so, as the fuel of inflated hype burns out, and the gravity of commercial application pulls down on its excitement to test its true value.

AI, however, seems different. It has appeared, drew much fanfare, and then disappeared several times already – more akin to a comet, flaring a tail of excitement with each new orbit.  As it reemerges, nearing the heat of expectation once again, it lights up with a spectacular plume, flung into space for another long dark hiatus.

AI history suggests five such orbits already – so is it destined for cold dark space soon?

Superficial AI

Regardless of the metaphor du jour, what we must inspect is the true value returned today, not the imagined expectations of tomorrow. The best test of commercial viability is not an intelligence test; it’s whether consumers are getting more value, and if the business offering the products & services are using AI technology as leverage, providing those things with higher margins.

For example, my mobile device is now my phone, my Garmin, my camera, my alarm clock, my digital assistant, my video recorder, my dictation device, my virtual reality device, and so forth.  20 years ago, it might have cost me $5,000 for these services.  Today, I get it all for $500 – $700.

We’re all under pressure to do more in the same amount of time.  To that end, these devices have become indispensable – they are essential to modern day survival – adapt to them, use them efficiently, or you’re passed by.

Therefore, by some measures and definitions, AI has delivered this time around.  Personally, I don’t care when a big company announces their sixth AI acquisition, or what their advertisements or creative animations say.  In my view, the proof is if customers are buying, are satisfied with those purchases, and are reporting their lives are easier, more productive, and more enjoyable.

Businesspeople must apply the same tests.  Can they deliver better customer experience with AI?  Are their product & services measurably smarter and more efficient?

If they aren’t passing those tests, then it’s just superficial AI.

Real AI Value in CX

AI – Automated Intelligence

As we all admire the latest bright tail of inflated expectation, let’s study what AI has really contributed to delivering better customer experience (CX) this time around.

For starters, look again at that magical device, the smartphone.  It streams location data, activity levels, browsing preferences, timing behavior, and the like.  Businesses consume this contextual data, and use decision hubs infused with AI algorithms that in less than a second calculate a next best action or insight.  That’s real!   Big banks, telecommunication / technology firms, and retailers are doing this today to improve acquisition, on-boarding, cross selling, and retention rates.

For consumers, the insights automatically delivered include recommended products, drive time estimates, calendar reminders, and service alarms. Alerts & notifications remind when bills are due, when fraud occurs, or when more exercise is required to meet goals.  Cars drive & park themselves, thermostats learn, and media services understand consumer preferences.   Customers can interact with machines by simply speaking to them.

For the marketers responsible for engagement strategies, AI now recognizes buying patterns, automatically performs A/B and multi-variate tests, which ranks the best content & promotions for the right individuals, and even suggests the best timing for those recommendations.  For salespeople, AI predicts the best contacts, opportunities, and accounts to spend energy on to maximize close rates.  For service workers, AI deflects simple service requests, and guides agents on complex service processes to improve time to resolution, ultimately improving customer satisfaction.

Simply put, there can be little argument that AI has delivered value during this orbit, much of it in the form of automation as opposed to higher-level intelligence.  Fewer marketers deliver more relevant and better-timed tactics.  AI assisted sales means higher quality pipeline with sharper close rates. Contact center managers relish shorter handle times and more efficient call resolution with less staff, and consumers enjoy shorter wait times and voice / bot-assisted service. For those using AI, NPS and customer satisfaction scores are on the rise.

All of these outcomes are commercially feasible.  Every business (not just the avant-garde) must rapidly incorporate these proven technological capabilities.  Hesitate, and the likely result will be eventual irrelevance.

What’s next – In my lifetime?

With all this said it’s back to our question.  Can AI keep delivering, or is it bound to let us down soon?

As humans, we love to dream.  That’s important.  In fact, regardless of how fast machines move forward, it’s still something that separates us from them.   We envision a fanciful future, and plot our course toward it.  Along the way, we stumble, get humbled, get up, and plot again.  This is our nature.  Each step along this evolutionary path, we create and refine machines that help us achieve our dreams.

Our vision seems unchanged.  We long to make life easier and more enjoyable for more of us.  To do this, we must continue to refine our existing tools, and invent new ones that assist us, and make up for our physical and human limitations.  No different from our first instruments, modern day smart tools take over tasks we were never very good at, or simply couldn’t do. They help feed us, optimize our resource consumption, and make our very survival possible.  We are already dependent on them, and there is no turning back.

This is also true for customer experience tools.  Our expectations are high and climbing.  We expect to interact with brands that listen, understand our preferences, react accordingly, and when something goes wrong, can turn on a dime and make things right instantly.

When I enter a website, I expect the search to be intelligent, the user experience to be delightful, and the checkout process to be flawless.  If I chose to do all this while mobile, I expect the same experience on my smartphone.  If I need help, my first reaction is, “why did things go wrong in the first place…how could this have been prevented,” and then I test if resolution comes fast with low effort – and does the business learn from the mishap.

This is the new normal.  Unfortunately, many brands today are not delivering on this type of customer experience.  The bar is high, but the elevation of game is not so much a demand from technology as from organizational re-tooling and reorganization to accommodate for technologies already commercially available.

Technological advancements will continue to accelerate.  Smarts will show up in more devices. We will demand our machines become more human, especially in delivering customer service and better experiences.  As humans, we love a personal touch, a social exchange, a sense of community and belonging.  So far, machines have not been able to deliver on any of these aspects.  That’s changing.

Presently, there is very interesting research going on to bring more human-like aspects to machine interactions. Google’s DeepMind research lab has made impressive gains in speech synthesis (text-to-speech) in a project known as “WaveNet” where robotic voices are becoming a lot less robotic.  Similar advances in Chabot research is leading to smarter bots able to remember details, learn right from wrong answers, and hold basic conversations.  You can try one of the better ones at http://www.mitsuku.com/

These developments are exciting.  The possibilities are enormous.  Yet until these become commercially viable and noticeably better with true customer engagements, you should train your eyes on what is real in AI today.  For now, focus your investments and efforts on delivering real CRM value from AI tech today in the form of things like simple service request deflections, intelligent routing to the right agent, relevant product recommendations & next best offers (based on individual behavior profiles), and guiding salespeople with next best activities.

Meanwhile, keep close tabs on these other AI CX innovations as they progress, take some calculated risks on a few promising areas, and prepare for the next revolution of AI.  The AI comet will be back shortly.

Rise of Machine Marketers – Transforming CX

Machine marketers are smarter marketers, always using machines for advantage.  But this isn’t new.

Direct marketing was born out of the ability to exploit addressable media as the way to garner feedback on whether their enticements were working.  Catalogs and snail mail with reply forms, evolved to email, telemarketing, and other mechanisms – smarter marketers understood guesswork would never win over using data, technology, and the scientific method.

Machine Marketers

Database Marketing

In the 90’s, the ability to more massively codify and share customer data, and use it to steer marketing campaigns drove a revolution. It sparked a major shift of media spending away from general advertising using TV & Radio, to addressable programs.  Database Marketers, the offspring of Catalog Marketers and ancestors of Machine Marketers, scraped for individualized customer information to power personalized treatments – where direct response open rates, response rates, and conversion rates kept score.

They loved data because when they used it to drive targeting in their programs, the patient responded. Realizing their treatments were working, they wanted more data, wanted it fast, and wanted it in pure forms. Native sources worked well, but they sought alternate supplies in the forms of public, compiled, and modeled data – anything to test for a slight edge.

A new market formed with a vast array of players, arising to meet the growing demand for customer data.

The 2nd Coming of Big Data

Then, a number of things happened. Even more individualized data poured onto the market.  Consumers shopped and bought online.  Consumers went mobile.  Consumer devices of all kinds started streaming behavior data.  Consumers readily traded personal information for points and promises.

Hardware continued to plummet in price and better software meant cleaner and more accessible data.  Data compilers flourished, with data as their raw material, and database & data science technology their assembly line, and the internet their logistics network.

Database marketers had struck oil again, but this time it was BIG – and IoT data was the source of their new bubbling crude.  Data refineries appeared everywhere.

Internal IT had competition – their 90’s data warehouses rendered obsolete by a Big Data revival.  Open source databases like Hadoop, were faster and ran on commodity hardware. SaaS providers offered a variety of big data subscription services, and agencies used bigger and faster hosted databases.

There was but one small problem. Insights weren’t leaping out of these primordial big data reserves.

Data Science and Data Mining Come of Age

Meanwhile, mad “Data Scientist” marketers continued to manipulate and tune their statistical models to improve lift. Early on, they realized that algorithms devised hundreds of years prior could now be fine-tuned and fully unleashed to predict which customers were more likely to respond and buy their products.

Less sampling with faster machines and more data meant better results.  Suddenly, more people became interested in what they were doing.  People were peering over their shoulders. The press told stories of firms predicting a pregnancy before grandparents even knew.  Adding fuel, the biggest brands on the planet (Google, Facebook, Amazon, et al) got into the game, doing big reveals, seemingly weekly, on the methods to their data science madness.

It was time to give this a makeover, market it, and commercialize it.  “Geez,” said the creative marketer. “We can do that!”

AI and Machine Learning – The re-launch

Our story takes us to circa 2012.  The time was right.  Cars were beginning to drive themselves; IBM’s Watson had won Jeopardy; Google was predicting our search terms and winning at the game Go.  Our iPhone was conversing with us, and Amazon & Netflix were courting us with recommended products to buy and movies to watch.

Honestly, no new science unexpectedly sprang forth, but as happens old science around for decades (decision trees, neural nets, Bayesian learning), became an overnight – well let’s call it an over 5 year – sensation.

What happened was how technology revolutions occur.   Attention begot investment, huge investment bought more innovation, and marketable innovations caught more attention – and the virtuous loop was in motion – adequately fed by a rich venture capital environment.

Marketers assembled the pieces into cost effective working solutions. They collected and compiled consumer data sources, cleansed and filtered them, fed them into pattern recognition and self-learning systems, detected opportunities and alerted touchpoint systems, automated waved campaign schedules, and connected their outputs to fulfillment systems.  They did all this via an interconnected stack of private and public clouds, transferring data and insights in seconds.

Michelangelo meets Newton – When Content met Context


By 2016, another phenomenon unfolded.  Creative & scientific minds more closely collaborated.

Deep Learning, the science of neural networks, commoditized language and image processing, changing how we interfaced and worked with machines. Clunky interface paradigms gave way to elegant ones that were responsive and rewarding.  Design thinkers (those artsy fartsy types) were no longer an afterthought. Au contraire, they were now a strategic advantage.  Consumers dictated the definition of great customer experience: Relevance, value, simplicity, and visual beauty.

Machine Marketers, ever the opportunistic breed, seized the moment, further refining their targeting and personalizing creative treatments across available channels. Machines further assisted their agency suppliers, assisting them in turning out better, faster, and cheaper creative.  Technology further assisted marketers, auto generating optimal SEO terms, email subject lines, and even catchy tweets.  Machines advised on the optimal time to execute campaigns.  Next best recommendation rankings used statistical probability to find relevant products & services for more refined targets.

Beautiful creative no longer took months to produce.  In many cases, consumers produced content for brands – and the content bottlenecks holding back visual personalization broke lose.

Science and technology glued yet another critical piece into place.  Touchpoint systems where customers interacted could now understand natural language, and instantly fed back contextual data (location, last behavior, weather conditions, intent, mood, and so forth) straight through to systems primed with algorithms that learned in real-time, recalculating next best actions in a conversational mode.

“Computer, find me the closest coffee shop.”…”Ok, I found one 2.5 miles away, do you want directions?”

“Computer, I need a highly rated case for my X phone for under $25.”…”Ok, I found four with 5 star reviews that fit your X phone for under $25, do you want to hear about them?”

Fronted by Natural Language Processing (NLP), personalization engines married conditional & appealing content with contextual recommendations – spawning audio & visual personal assistants.  The result: off the charts lift and conversions.

These were contextual, conversational, and relevant interactions.  This was transformational.

artificial intelligence evolution

Machine Marketers Rise Up

In the end, let’s face it.  Marketers want to do one thing more than anything – sell more stuff.  Yet the smart ones know that the best means to that end is relentless focus on the customer.  Ensure each is a happy camper via an individualized relationship, and satisfaction and profits increase.

Thus, today more than ever, ALL marketers had better face one important fact.  They can’t achieve customer centricity at scale using the tools, data, or organization of yesterday.

Like any profession, winners constantly seek a new competitive edge using the latest technological advances in equipment, repeatedly testing innovations, measuring for improvement, and fine-tuning.

Artificial intelligent interfaces are changing the ways consumers interact with their devices, provide data, and interact with brands.  Data is flowing freely, and although privacy laws seem to ebb and flow, the trend has been toward more data sharing and the ability for the crafty to gain a deeper understanding of consumer behavior.

Technology – cheaper, smarter, more portable, and easier to use, continues to translate into the potential to deliver more relevant and convenient customer experience.  Those that get this, and execute on it, will win.

Machine marketers are those who master using the latest data & technology to their advantage – rising to that challenge, they rise to the top of their craft.

Note:  These views are my own, and not that of my employer

Smarter CRM: Machine Learning Episode #2

You want Smarter CRM, don’t you?

In this “Mini-Cast” on machine learning (ML) and artificial intelligence (AI) for marketing & CRM, I outline the numerous applications you can use to improve customer experience.

AI for Smarter CRM
AI for Smarter CRM

In this second episode, I delve into the various areas of AI & ML applied to Marketing, Sales Automation, and Customer Service where ML & AI play huge roles in taking those functions to new levels of insight and intelligence, enhancing productivity, effectiveness, and delivering better customer engagement.

Episode #2 on YouTube:

Mind your P’s & Q’s for Balanced Marketing

Let’s not pretend the four P’s of marketing are dead, useless, or outdated.   They aren’t.  You still have to price your product or service and promote it in the right places.

These age-old marketing corner stones are as important today as they ever were to your operations.   What they don’t do, however, is provide you a compass to help guide strategy and insure your success.   For this, you need new age P’s and Q’s that will orient you, and serve to infuse both a predisposition toward “customer centricity,” and help you take pragmatic actions with customers for balanced marketing.

In truth, customer centricity is simple to achieve.  Give customer what they want.  What’s difficult is to do this and run a growing and profitable business.  To thrive in the digital age of the consumer you need a measured approach to a variety of factors.


So think about the below P’s & Q’s, and practice them to achieve balance when you plan and perform your marketing:

Four P’s

  1. Preference
  2. Personalization
  3. Performance
  4. Pervasive & Persuasive


Instead of centering your marketing on your products and simply spouting off about all its attributes, concentrate on understanding customer preferences first.   Regardless of your business, this is a better approach.  Sure, there are times when customers can’t easily articulate a need.  Consumers weren’t breaking down Apple’s doors demanding an iPod, yet they clearly wanted portable music.  So even in these cases, if you understand their behavior and fundamental need, you’re more likely to fit an offering to an expressed or latent requirement.

If you understand the market you are in, and what the customers really want in that market, and you hear them out, then you are on the right path.  If you can’t pinpoint them, and you rarely do research and gather data, you probably aren’t actually listening to your target audience.  Get to know your customers.  Listen to them.  Live in their shoes.  Learn from them.  When you do this, you will uncover huge opportunities to serve them better.

Interface with customers on many levels.  If you sell jets, listen to and understand more than just the airline buyer, but also the infrequent & frequent passengers, pilots, flight attendants, and maintenance crews.


Make personalization self-service.   Figure out how to allow your customers to self select the marketing they receive.  Make it a dialog and give plenty of options.  Then, use that data to customize your marketing activities and the product and services you promote.  Often, the same offering will fit different customers, yet make it so because they selected it as opposed to they had no choice.


You can listen, and you can configure, but unless you can deliver, your marketing is the noise before your defeat.  Put time & effort working with your product teams, your operations, your fulfillment, your service staff, and your training folks.   Once you get a sale, make sure the customer is not subject to old school mentality where the post-sale devil appears and declares, “Oh all those promises were for when you were a prospect, now you are my customer.”


Get your message out – everywhere you can where your target audience is.   Nowadays, there is a lot of marketing clutter, and you are going to need to work hard to get reactions to your impressions.  You don’t always have to spend traditional marketing dollars or use classic methods to do this, because you can growth hack.  For example, consider using “Contextual Incremental Marketing” as a methodology to sustain your message in digestible chunks more likely to sink in over time.  Leverage your customers as a marketing channel – when they have success, they are your most credible spokespersons.   Spend a portion of your marketing efforts teasing out their accolades and distributing their stories.

In addition, leverage social influencers, blogs, crowd sourcing, partners, affiliates, analyst, review sites, and local community efforts to spread your word.  In other words, use the digital network effect to your advantage.


Four Q’s

  1. Quality
  2. Qualification
  3. Quantification
  4. Quicker


A new adage I’ve heard that I totally disagree with is “Speed trumps Quality.”  It’s a sad commentary on society in general, but specifically for Marketing and Products, it’s a problematic long-term strategy.   You will erode your customer trust if you don’t have quality marketing, sales, and products.  Strive for high quality, and take the extra time to get it right – the alternative of sloppy or mistake-ridden output is unacceptable to your customers.   Find me a brand that has stood the test of time, and I will show you a brand with quality at its core.  So measure twice and cut once.


Don’t be afraid to qualify with whom you do business.  You have a business to run, and if you did a strategic business plan (I hope you did – if not – go directly to jail and do it – do not pass Go – do not collect $200) – you have a specific market and customers you are focusing on.  If you stray outside that, using the same plan, it’s a recipe for failure.

Consider customers that aren’t properly qualified for your existing products as a potential future opportunity for another adjacent market with another product.   Then, study it and make a deliberate decision of whether to enter that market segment.


If you aren’t measuring it, you aren’t managing it.  Moreover, if you aren’t managing it, you can’t improve it.  You need to commit to sustained measurements so that you can adjust dynamically.   Consider this – if a police officer pulls you over and asks you why you were doing 60 mph in a 35 zone, and your answer is that you didn’t think you were speeding because when you checked a week ago, you were going 35 mph – that argument won’t hold weight, and it doesn’t hold weight in your business.


You need quality, but you also have to be quicker than you were before, and certainly faster than your competition.  This means speed in detecting changing market conditions and agility in adjusting your messages and product to that new dynamic.  You must be real-time in your ability to use customer context, and factor it into your decision-making, using a next best action approach.

5 tips to avoid becoming “A Distracted Marketer”


In June of 2015, I wrote about the Distracted Consumer with their heads buried in smartphones and attention spans decreasing from 12 to 8 seconds in just 15 years. It occurred to me that marketers, subject to the same rush of options, information and environmental forces, are highly distracted also, and may not be focusing enough to get real value out of the solutions they buy and attempt to implement.

As a consultant for years, my DNA is wired to help solve problems and find ways for clients to streamline and improve their marketing efforts, ultimately improving customer experience for their clients.

Stick with me (proving you can outlast the attention span of a goldfish) as we explore 5 areas of distraction that can derail you from sustained improvement, and consider ways to combat those:

  1. The explosion of options and the sea of information regarding technologies & practical applications, much of it contradictory, hyped up, biased, and confusing, makes it difficult to find and focus on the right technology platform.
  2. The amount of data available can be overwhelming and lead you down countless dead ends. Big data, little data, slow data, fast data.  Its data galore and finding the right data and putting it to use in a timely fashion before it decays into worthless bits can be challenging, costly, and elusive.
  3. The disruptive organizational environment. Matrix management and average tenure in jobs less than 3 years means instability in teams, long term planning, and accountability.  Disruptive isn’t always an adjective to be proud of.
  4. The death of critical thinking trumped by speed over quality.
  5. Agile as a crutch to why people drop everything to work on the new thing when often the newest thing isn’t always the most important thing, or the thing leading to highest impact & value.

So many choices


Let’s face it.  It’s great to have choices.   It allows us to discriminate as consumers on features, quality and price, and push our suppliers to compete and innovate.   Marketing technology is certainly no exception.   Chiefmartec.com catalogs nearly 4000 companies supplying a variety of technology to the marketing function.   Yet picking solutions is very different than walking down a cereal aisle and deciding on your next breakfast.  More akin to a pharmacist that needs to understand the interactions when mixing medications, a marketer needs to understand how these technologies will (or won’t) interact to produce a productive overall solution.

Before you pick a technology stack, explore examples where other firms have successfully deployed a solution with various vendors “stacked” together.   Ask yourself if they received the value and within the budget and timing they expected.  Once you pick one, be patient (within reason) and give your user community a chance to adjust to it, and adopt its full potential before you jettison it for shiny new parts.   Find a core linchpin vendor, and build around that firm and its technology, making sure that company is stable, innovative, and invests reasonably in its products versus its exit strategy or next acquisition.

I’m drowning in a big data lake

Have you ever started searching the internet for something and 30 minutes later find yourself reading an article that has absolutely nothing to do with what your initial pursuit?   Sales & marketing data trails are no different.   With a remarkable amount of data, often linked together with drill downs, report hyperlinks, summary and details tabs – you can quickly get lost in a maze of data.

Marketers need new discipline and training to navigate data and discern bad data from good data.   Before embarking on data exploration, it helps to have a hypothesis in mind, and then set out on a path to prove or disprove it. Use caution and judgement to verify data resources, tossing out dubious sources.

Stick with some core tools that enable you to navigate, slice & dice, fuse, and distill data.  Learn some deep features and exercise your proficiently with them, just as you regularly exercise different muscles to stay in shape.   Spreadsheets, like tools in a plumber’s toolbox, haven’t changed that much in 20 years and in many cases work just fine.

Disruption corruption

These days, we’ve been disrupted by much more than travel and leisure start-ups.   The workplace culture is one that breeds and seems to reward attention deficit disorder and interruptions.   There is actually a premium put on impulsive behavior, hatched by a technology crazed society that fears they may become outdated and obsolete thinkers because they have been offline for more than 15 minutes.

Interruptions abound in our lives.  It’s what’s killing our attention span.  But to be a happy and productive marketer, you need to find your quite place and time.   I find it early in the morning.  My brain is fresh, my associative thinking runs freely, and I have some time to plan ahead and some control over the interruptions, which later in the day become harder to ignore.  I use this time to plot out longer term efforts, take stock in what I’ve accomplished and what’s in flight, and then factor that all together to adjust priorities in tune with my long term objectives.

Find your happy place and time, and use it to get deeper into a subject, a skill, or to perform some research that otherwise invariably falls victim to death by fragmentation.  Avoid forcing something out prematurely if it hasn’t been subject to testing or vetting commensurate with the expectations of its audience.  Use your practical judgement to tradeoff the benefits of a rush job with the consequences of shoddy work.  Justify the time needed to question, fact check and sustain deep analysis.  The world still needs a healthy dose of this from those willing to buck the new world disorder.

The need for speed – the death of the deliberator

I’m not suggesting speed is always bad.   If you can crank something out with “good enough” quality, and you beat your competition to market, you gain a first mover advantage.   That said, it’s easy to see the deterioration of product, service and content depth and quality, as massive quantities are pushed out at breakneck speeds by dubious producers and publishers – or simply regurgitated into the echo chamber of the digital world.


Yes, it’s the golden age of the individual, where anyone gets a platform to be an author and self-proclaimed subject matter expert.  Yet alone, that doesn’t make the ideas worthwhile or the end product immune to scrutiny. Take the necessary time to question things, double check them, and dig deeper to find something unusual or interesting as you probe into that next layer of analysis that can lead to real insights.

Hand me my agile crutch please

Agility is critical during a performance, when things don’t go according to plan, or as a marketer when you are:

  • Responding to seismic market forces
  • Using it to quickly test and learn and refine new ideas.

Don’t let it, however, becomes a crutch for failing to plan, concentrate, and see tasks through.   Form a hypothesis or formulate a project plan, plot a path, and stick with it unless you encounter an actual example of a major market force that should be factored in.   I’m sorry, but these disruptions don’t happen every day, although rhetoric would suggest otherwise.

Agility works best when it’s backed up with a thoughtful game plan.  The game plan forms the blueprint and guardrails you operate within.  The tactics you employ to achieve your goals should be flexible as you tactically maneuver once the action begins.   If you alter your course in ways that have no connection to your overall strategic plan, the clamor from these random adjustments will be the noise prior to failure.

Instead, use the agile approach as a methodology to execute on a grand plan & mission – with the difference being that you are simply releasing, communicating, and adding value to your constituents on a more frequent basis during the full length of your overall master effort.

Improve Customer Experience with 5 (Almost) No-Brainers

As a consumer, I’m often disappointed when companies don’t get the little things in Customer Experience (CX) right.  For example, I frequently stay at hotels, and I always ask for a room away from the elevators, but they never remember it, and I can’t note my preference.

customer experience duh

CX professionals and Marketers can benefit by reaching the first level of being “Customer Friendly”, and doing this with consistency and meaning.  Small things matter, such as knowing someone has children, what products they already own, and promoting what is relevant to them.

Sounds really simple?  Yet to do that – if you aren’t a small business – you need coordinated mechanisms to record customer preferences, common history, a standard set of rules, and then a way to surface key information and recommended actions to all customer facing staff and systems.  Functionally it’s not that intricate, still today many companies don’t do this well.

Why is that?  I think it’s because they aren’t seeing the cumulative value, and as such can’t envision or measure the overall benefits derived by connecting these dots.  Simple improvements to customer experience – applied one step at a time – build upon each other.

Everyone agrees that having institutional client memory (a 360 view) is valuable – and great entrepreneurs relish it, but large organizations struggle to build and provide it, for a variety of reasons. Constant acquisitions, distributed staff, legacy technologies, and silo systems – or worse, simply losing sight of customer centricity.  As the excuses build up, competitors swoop in with new technology and agility, and fill the customer experience vacuum.  Although these players may be disruptive, usually they aren’t employing earth shattering methods or technology.

As pressure builds, large entrenched players are forced to take stock.  They reflect on expensive efforts that didn’t meet ROI promises, such as failed Data Warehouse and CRM efforts, and it taints them with an overly jaded mindset.   If this describes your corporate culture, pull an alarm bell, ringing for an attitude adjustment.  The message is clear-cut.  We have to fix this – pronto, and it shouldn’t take millions and years.  Find the CX “No-Brainers” and execute on them.


All the rage these days is about Personalization.  And personally, I love it.  And it’s so easy.  Just make sure to remember all the little things about customers, particularly the things they share, and make them feel special.   And that, my friends, in a nut shell, is Personalization.

Here are 5 examples of No-Brainers and likely outcomes.  Literally hundreds more can spring from these:

  • Getting little things like names right is really important. My name is Vince Jeffs – not Jeff Vincent (in fact, where I work at Pegasystems, there is a Jeff Vincent, and you can only imagine the fun that creates).  If you call me Jeff repeatedly, my sense is you don’t know me and don’t really care.  Record children and/or pet information also.  If they tell you something about them (like their names), record that.   Make sure you use it wisely, when it counts and with class.  If you do, it matters in a massive way.  For instance, if you sell Pet Supplies, you think this might be important?
  • Document product interest and purchase activity, whether based on search, surfing behavior, or traditional conversations and transactions. Incorporate that knowledge into your near term marketing efforts.  Customers will love when you send relevant offers.
  • A customer shares when it’s their birthday or anniversary. You remember it, and on that special day, wish them well.  I don’t think I’ve ever heard anyone complain about being wished a Happy Birthday.
  • Demonstrate you remember their business, and show appreciation for it. Keep RFM statistics (How Long/Recent, How Frequent, How Much), and use it astutely.  A staff member saying, “For 10 years we have had the pleasure of having you as a customer.  Thanks so much!”  Again, you think that might make a difference?
  • Remember something really unique about each customer, use it judiciously, and make them smile. Don’t try to infer this, by appending 3rd party data.   You aren’t clever trying to deduce I’m a cat lover because I subscribe to Modern Cat.   However, if I mention I love Blues Guitar, and you cater to that later by recommending shows in the area, there is no way that won’t tickle me, even if the shows aren’t that interesting.   At times, you will have no choice but to put prospective customers into segments, because you otherwise can’t reach that audience.  Yet once I’m a proclaimed customer, ditch the mass segmenting and assumptions, and go instead with my stated preferences.

Your goal is to do this at scale, while executing like the corner bakery.  There is no reason why you can’t put an initial Customer Decision Hub – An Always on Customer Brain into place in 90 to 180 days; regardless of the size of your firm. This is why I entitled this post “… (Almost) No-Brainers” because you need this brain, but what it initially thinks about isn’t that sophisticated.

customer decision hub

True, it will take time to connect to more data sources and customer channels, but in less than 6 months, your foundation will be in place.  In mega firms, there will be 100’s of people telling you it can’t be done.   In spite of this, I’ve seen it done.   Start with one or two channels, a single business purpose, and build decision strategies that are driven by a customer profile.   Assemble a library of actions associated with the business purpose.   Then rank these actions and execute on them.

And here is the really amazing thing.  Implementing subsequent no-brainers entails very little incremental cost.   I’m not a fan of fuzzy math, but this is a case where 1+1 does equal 3.

Note:  These views are my own, and not that of my employer

Top 10 Things for Marketers to Deliver on Great Customer Experience


A slight re-vamp of my last post taking out the holiday twist…

10.  Really get to know your customers. Make sure every customer facing person and system has access to your knowledge.

9.  When you have the privilege of interacting with your customers, listen to them (that’s context) and adjust your message, actions and offers accordingly. Provide them value, and get to the point fast.

8.  When they need help with their buying decisions, make it easy and convenient for them. Use your knowledge of them wisely (e.g., location, which buying stage they are in) and they will love you for it.

7.  Work closely with your product and engineers to ensure your product is indeed awesome. Bring them your innovative ideas and ideas from your customers.

6.  Tell them stories about your fantastic products, but more importantly have your best customers or someone they trust tell these stories.  That’s right – like it or not – they trust them more.

5.  Make sure you entertain them with these stories. Nothing is better than some humor and excitement to get attention.

4.  Treat each and every interaction with your customer as a “Moment of Truth.” It may be your best and it may be your last.

3.  When they get to the point where they are really interested, don’t sell to them – help them buy.

2.  Always respect them. Respect their privacy.  Listen to their concerns.  Try to understand their needs.

Microwave at 10 sec

1.  Evaluate every action you want to take as a marketer, and ask yourself one simple question. Will this improve Customer Experience?