95% of Martech Executives say AI, Machine Learning, and Real-Time 1:1 Marketing are crucial to their long-term success, but only 5% are using these technologies successfully today
When we’re on journeys, we often encounter decisive moments, and how fast we get accurate and relevant information and the decisions we subsequently make can have a huge impact on the outcome. Take, for example, one of your vacations. Your airline cancels a flight leaving you precious seconds to react and find other options before they evaporate; Or, if you’re upset with your wireless service and decide to reach out – calling to give them one last chance. Every case is different in terms of the people involved and the situational context, but timing and the flow of relevant information always plays a crucial role.
Time Is Of the Essence
If you’ve ever managed a project or worked with a project manager, one of the first things you learn is what’s called the triple constraints: the scope (quality), the cost, and the time (schedule) for a project. Project managers always manage a project using this idiom:
“No problem, I’ll deliver as long as I can control at least one of these 3 constraints. Deliver by Jan 1 you say. Fine it will cost X and we can deliver Y scope. Change the scope and the cost will go up. You want to hold costs to $50k, then we’ll deliver X scope by Jan 1. Change the scope and the date will slip.”
It seems lately in projects and commercial transactions we’re trying to break these laws. We want everything faster, cheaper, and more of it at higher quality. Why not, it sounds great. Although the quest for instant gratification isn’t new, it certainly has upped its game.
It’s obvious why we want things faster – because time is a scarce resource and finishing something can mean the difference between opportunity and obsolescence. Since we can’t pack more minutes into a day, we squeeze more tasks into limited time usually sacrificing scope / quality, and all in an attempt to push things to faster completion. With continual pressure to increase productivity levels, technology helps accomplish more with the same resources. Clearly, time is of the essence.
However, there is another reason time is so precious. Time is the enemy of information’s value to decision making. When something happens, information about it may be extremely valuable seconds after it occurs, yet through time that value decays (see Figure 1) at rates that are different depending on its importance to a given decision.
For example, when someone has a heart attack an enzyme called creatine kinase (CK) appears in the blood. Detecting the recurrence of that enzyme and passing that information quickly to a doctor could save that person’s life. Much later, that information may still be valuable in a post analysis of that patient’s situation, and years later, it could still prove valuable to researchers performing wider studies on congestive heart failure. Nonetheless, its value diminishes considerably over time, eventually relegated to but one data drop in a vast data lake, and ultimately some information even becomes a cost and liability with no corresponding value.
In CRM, real-time data can be of corresponding value and the context of the situation determines its value and decay curve. While on buying journeys, consumers come to decisive moments. When considering what consumers expect at those moments, brands must assume they are impatient, easily distracted, and likely to act suddenly. Once again, speed becomes paramount.
When Do Customer Interactions Need To Be Real-Time?
Honestly, there’s no such thing as real-time information. When an event occurs, time immediately elapses so, at best, a device can detect an event and pass that as information in near real-time to a decision maker. In some cases to be of optimal value, this information needs to reach the decision maker and the decision made – all in less than 1 second:
Given that understanding, what’s most important is that the information makes its way into a decision management system that can factor this into making a decision and in enough time to affect some other event that hasn’t yet occurred.
In our heart attack example, the presence of the enzyme CK is the initial event followed by some device that detects it, followed by that information passing to a decision maker who takes action to prevent a subsequent heart attack:
For CRM, a customer may be on the verge of switching service providers and the event may be one last contact with the existing provider:
Depending on the problem at hand, having real-time information and using it in a dynamic way may be critical to positively affecting customer experience and satisfaction.
In general, customers are less willing to wait for service or answers than ever before. When someone decides to research a potential purchase and browses a website, if it’s not responding nearly instantaneously, it’s likely the consumer will move on. Moreover, if the customer calls into a contact center and enters a long wait queue, again it’s likely the customer will balk. In other words, you literally have seconds to make the right impression and provide them with relevant content.
As we’ve just seen, the situations the events occur within, and how events relate to each other are crucial to understanding the full story. The circumstances that form the setting for one or more events are its context, and the data that comes from them contextual data.
Feeding this data into a decision engine can be essential so decision makers understand the full context of each event before making subsequent decisions.
Take another use case. Again, the event is a call coming into customer service. Here’s the setting for that event:
On a rainy day on May 22, 2017 at 4pm, while stuck in a traffic jam on 1-81 in southern VA, a certain customer (Mary) places a call to her credit card provider. Having just learned of late fees, she is not happy. She’s a loyal customer of 23 years who always pays on time and her aim is to get these fees removed.
Consider some of the contextual data in this scenario:
- Historical background of customer – e.g., family history, purchase history, engagement history – i.e., knowledge of this customer as an individual (23-year loyal customer – Mary)
- Date / Time (May 22, 2017 at 4pm Eastern)
- Location / Proximity (northbound on 1-81 at x lat / y long)
- Environmental conditions – e.g., weather / traffic (raining – stuck in traffic)
- Preferences (uses phone to call in)
- Emotional state (unhappy with fees; may be further agitated by weather & traffic conditions)
- Journey stage (just received bill and is inquiring about fees)
- Current intent / agenda (aims to get fees removed)
Think about how important each piece of information is to the scenario and how all of it, when factored together, forms a more complete picture. For instance, if the call started at 4pm and the conversation with Mary is still in progress at 4:30pm, it’s likely she’s antsy and probably tired from a long day. Now combine that with the weather, traffic conditions, journey stage, current intent, and historical background, and now it’s very likely her patience is wearing thin.
In short, situational context answers these questions about an event.
Brands that can factor in this kind of contextual information are more likely to address immediate sensitivities and handle customer interactions accordingly. Human agents, unlike artificial ones, have the potential for very high levels of emotional intelligence (EI) [i]but still need assistance compiling the information and making sense of it. So far, artificial intelligent (AI) bots aren’t that advanced in EI and humans have the edge in relating to people, applying soft skills, and making complex judgments. Thus, it’s crucial to realize the full situation when designing systems that will include both self-service options and escalation to humans, and when humans are involved, providing them with augmented information so they can serve customers better.
In this scenario, as with most others, the contextual information becomes practically useless the next day. Unless the systems involved can detect these events and the context in near real-time, and pass it on, the value of this information decays rapidly. Imagine the customer service person saying, “If you just call me back tomorrow morning Mary, my system will run an overnight batch job, and I’ll have a sentiment and value score for you so I can help you better” – said a successful customer service person never.
And systems can’t pre-calculate conditions or recommendations either, hoping that works. How would you rate a pizza delivery company if you ordered a pie, and delivery came in 15 minutes (a near real-time pizza order), but they cooked it the night before?
One Contextual Brain Making Real-Time Decisions
When businesses engage with customers – or vice versa – such as in the examples given, they’re expecting an ongoing conversation regardless of the initial channel. In some cases (such as a phone call), the exchange begins and ends on the same channel, with stimulus and response occurring dynamically and repeatedly. In other cases, such as a website visit or chat session, conversations may take longer, and experience periods of pause, delay, or even happen over multiple channels and sessions.
Unfortunately, many brands treat each interaction as an isolated transaction instead of viewing them as parts of a continuous conversation that builds toward a deeper relationship. When viewed from the latter lens, each interaction nudges the relationship in a positive or negative direction, in effect adding to a “Good Graces” balance, or subtracting from it (see Figure 2). Clearly, the goal of any customer-centric minded organization is to evaluate each interaction’s contribution, monitor this balance, and work to strengthen the relationship – improving it with each engagement.
This is more than a loyalty point balance, an NPS score, or a sentiment score. It’s more like a credibility score, measuring the overall standing your brand has with a given consumer.
Consumers reward companies with more business when they provide convenience and simplicity, can be trusted, and help improve their lives. Amazon is a prime (pun intended) example. Consider the Amazon Dash button, which really isn’t the revolution in buying experience originally promised, at least not yet. This is the internet-connected button you place, for example, on your washing machine, and you push to reorder detergent. However, many customers purchase the button (for $4.99, which comes with an equal credit upon the first purchase) but never use it. They just love Amazon, trust them (i.e., they have a high good graces balance), and go for their innovative ideas – and this idea smacks of convenience, simplicity, and real-time engagement.
Customers also keep score in terms of brands that just get it. What this means is buyers notice if they get superior, well-coordinated services, regardless of the channel, time, location, or representative involved. For a business to do this, it must have a culture that oozes of a customer-obsessed mentality and have systems that support its people in providing outstanding services at scale. If those systems are in silos (see Figure 3), each commanded by a different brain, achieving that goal becomes impossible.
Can Anyone (Or Any System) Do This Really Well?
The short answer is yes, companies should be able to do a decent job of this, but they aren’t. Amazingly, a DataStax commissioned Forrester survey of 206 organizations across four major countries revealed, “95% of organizations are currently unable to make sense of customer data, and struggle to gain real-time insights from this data.”
When agents serve customers, to provide great service, they need technological assistance just as a pilot needs instruments. Flight deck instruments must depict actual conditions, with little latency, or they’re useless. Finding out too late that your altitude is 2000 feet could spell disaster. When on chat or a call, consumers expect immediate responses to each question and have a threshold for the total time they’re willing to devote. In theory, whether the agent is a bot or a human makes little difference, what matters is whether the agent provides authentic empathy, good judgement, relevant responses, and resolves the situation quickly.
Today, offloading some of this to machines is possible (sensing conditions, detecting patterns, managing decisions, and triggering actions), but seemingly only with CRM rocket surgeons behind the curtains.
That shouldn’t be the case, and it’s holding back brands from providing exceptional customer experience. Some vendors, nevertheless, are working to change that and provide tooling that allows for real-time interaction management or RTIM[ii] controlled by business users. Though the right tooling alone won’t bring success, as firms must align data, organization, and processes also, choosing the wrong technology will introduce barriers and rigidity that will be difficult or impossible to overcome.
When considering the entirety of the data, insights, and technological solution needed, keep in mind that each vendor has evolved their platform from very different starting points. Some have cobbled together solutions by acquiring smaller firms and in fact don’t even have a single platform. Others have built out their solution, appending capabilities to legacy outbound and batch oriented architectures (often based on email campaign blasting), making it nearly impossible to handle enterprise real-time processing requirements. It’s likely you’ll hit a wall (see Figure 4) with these types of solutions in attempting to transform to real-time 1:1 contextual engagements.
Thus, look instead for help from partners that have extensive experience with real-time customer interactions and have built solutions from the ground up to handle the complexities of real-time data wrangling, dynamic analytics, and scaling for the instant gratification response times required by today’s customers.
If you have a smartphone, laptop, tablet, or all of the above, it’s no longer a question if you’re addicted to being online, it’s a question if you recognize the problem and are in control of it. And if you don’t have any of these devices, I’d love to meet you and even shake your hand (since you’ll actually have one free) – before you become extinct that is, since your breed is already on the brink.
Why are we so addicted to technology? Perhaps there isn’t one simple answer, but an emerging hypothesis mainstreamed recently when CBS ran a segment on 60 minutes titled “Brain Hacking.” If you haven’t seen it, it’s worth a watch. Not surprisingly, yet ironically, to get to the replay, CBS forces you to watch ads.
In the segment, Tristan Harris, a former Google employee, refers to our digital devices as slot machines, and in a recent blog claims, “…technology hijacks people’s minds.” Ramsey Brown, co-founder of Dopamine Labs, calls the developers responsible for making the apps we use everyday “brain hackers” – essentially meaning they employ techniques designed to get us hooked and to alter our behavior. Arguably, these ubiquitous methods have already succeeded in causing habitual behavior. And if you’re not convinced, unglue your eyes for a moment from your device du jour, peer up, and notice everyone else’s heads buried in digital appliances, and how antsy they’ll get when unplugged for only a few minutes.
Essentially in concept, it’s similar to a virus invading the body (hence the hacking metaphor), with its mission to reprogram us to crave constant online activity. Behind each antigen are hackers, engaged in a form of biological warfare, engineering their payloads to infect our brains to crave more activity – on their sites and apps. Though it’s debatable how much of the altered conduct can be directly attributed to just a few hackers, the pervasiveness of the behavior is indisputable.
Race to the bottom of the brain stem
Tristan refers to this battle as a “race to the bottom of the brain stem,” implying these cerebral hackers are sparring for our attention, and will do anything to get more of it by appealing to a range of our human needs, even the most primitive ones.
So, are all CX pros guilty of this practice, or just a select few in Silicon Valley? In terms of marketing techniques, is this something new or merely old methods with new names?
Perhaps guilty isn’t the fairest word (with its implication of wrongdoing), but I’ll posit the first answer is yes, most professionals tasked with generating demand are trying to do this – with the huge caveat that some think (operative word “think”) they are close to solving an ageless puzzle of how each human mind operates and how to manipulate it. Further, it’s not the impact of a few, but instead the collective efforts of many demand generators, as well as our growing dependence on technology, that’s contributing to our hyperactive online behavior.
Nevertheless, today only a few dominant firms enjoy the majority of the economic rewards, since users spend the majority of their online time in select applications such as Facebook and Google. Call this the tech titan factor, a few gargantuan companies controlling the vast majority of user interactions that attract eyeballs to digital advertisements.
As for the novelty of this approach, the core practice is actually as old as direct marketing itself. Like doctors, trained direct marketers learn early on that diagnosis, problem understanding, and treatments followed by continuous application of test & learn methodology, are time-honored principles proven to attract attention and optimize engagement. What’s changed are the tools CX pros have to administer continuous and tailored therapies (see my article on the use of Prescriptive AI in CX), and as consumers how we’ve unknowingly given up more data about ourselves and increased the quantity of our online intake. Further, this medication being administered to us comes with no warning labels or explicit documentation as to the harmful side effects.
In terms of affecting consumer consciousness and behavior, marketers have again followed long-standing hard and fast principles. Take Maslow’s hierarchy of needs:
The reward consumers get from checking their devices depends on the individual’s specific needs. For instance, one person may be in the pursuit of self-actualization, and as such may be constantly using a gadget for educational discovery. Another may be in search of esteem, and becomes hooked on social media in a constant quest for recognition. No matter the reason for being online, advertisers track, analyze, and subsequently prescribe remedies squarely aimed at selling us goods and services they’ve ascertained we need.
Moreover, take the streaks tactic that Snapchat uses. This is simply age-old marketing gamification at work. It’s true today’s games are digital, more dynamic, mobile, and played by all ages, but S&H had consumers playing very similar marketing games in the 1930’s, with the goal of creating green stamp junkies.
Whether a modern game, or a game from the 1930’s, the basics of this approach are similar. Entice someone to play but don’t let them win out (making the game incremental – in this case the increment is days), luring them back in, and make the game length seemingly infinite (e.g., collect stamps; cash them in; collect more).
The persuasive fight for our attention
Have newly minted CX pros devised new sinister methods of mind control? Has a new economy suddenly emerged centered on getting attention at all costs, hooking people into using products? I don’t think so.
As far back as the 1950’s, fears about mind control perpetrated by marketers were already spreading, and various theories, many of them hoaxes, began to crop up. And the commercialization of everything, from historical sites to holidays, can be traced back to right after the American Civil War.
In the late 1950’s, rumors abounded such as stories of theaters lacing film with stealthily implanted single frames of subliminal messages such as “Eat Popcorn” and “Drink Coke” supposedly engineered to stimulate instantaneous demand for these products. In 1957, Vance Packard wrote a groundbreaking novel titled, “The Hidden Persuaders,” making an original argument that organizations are born to manipulate, and had moved from overt tactics to clandestine ones, with hired agencies as the evil genius behind it all. Quite possibly the only difference today is that we carry around in our pockets millions of commercials, and check in constantly, making us continuous targets for impressions. Unscrupulous marketers, as well as those with shreds of decency, have existed side by side since the dawn of time. They simply have more access (by virtue of over 150 years of marketing, commercial, and technological evolution) to more minutes of our waking attention, and will always vie for a slice of that bandwidth with newfangled material engineered to break through the clutter.
When you reflect on it, marketers seek attention and puff their wares – it’s what they do. How and where they’ve sought it and how much they’ve puffed has always defined the extent to which they further commercialize our environment and how far they push ethical and legal boundaries.
This fight to own a share of our precious attention itself contributes to a further lack of focus and increased distraction. I wrote a related piece on this (Contextual Incremental Marketing), from the point of view of the marketer, at the time not fully grasping that my tips about the phenomena were in fact recursive, that is, further reinforcing and encouraging the behaviors that I suggested were simply a facet of the modern world.
In a sense, it’s a vicious circle, but not a new one: CX pros stalking consumers, contending for their attention via an ever-exploding channel continuum, employing any means to engage their reptilian brain and interrupt them, persuade them, adding to attention deficit disorder. For consumers, the antidote is the same as it ever was – common sense, education (with reliable and readily available sources of accurate information), balance and moderation, free will, and self-control.
The surveillance economy
Like crime scene investigators (for more on this, read my blog: The CSI Guy – Customer Success Investigator), CX pros seek clues to solve the mysteries of making best guesses about the likely behavior, needs, and actions of customers.
Those involved in pure acquisition have little to go by, and as such, stretch for data and surveillance methods, test data privacy, ethical, and permission boundaries, and often still miss the relevance mark. In many respects, they are like matchmakers, casting a wide net, and hoping to bring in a few choice prospects. Those tasked with building on relationships, often called relationship or loyalty marketers, have it easier, with a treasure trove of owned media behavior data at their fingertips collected by modern digital tracking sensors. In either case, it should come as no surprise that gathering evidence is a top priority.
In 1992, Eric Lawson wrote a book called “The Naked Consumer.” It was an excellent account of the growing problem at the time of personal data sold as a commodity on the open market, and its lessons and conclusions are as germane as ever.
So what should we do?
Like any history, there always seems to be the appearance of it repeating itself, but invariably with evolving twists. In this case, some of the twists are:
- We can take devices with games and reinforcements anywhere, and often do. Mobility means more chances to be online. In contrast, when TVs first appeared, they were stationary. And radios were too bulky to carry, until transistors transformed them into the iPod of the 50’s.
- Because this digital drug is available constantly, and there are no official regulators, many of us are unconsciously overindulging. Like any addiction, step one is problem recognition, and for most of us, we haven’t admitted there’s a problem, let alone embarked on a recovery journey.
- For digital natives (those who have grown up with smartphones and social media), there are new pressures and social dynamics many of us that are older can’t fully appreciate. This has resulted in massive numbers of teens afflicted with anxiety and depression (see this Time Magazine article for an in-depth look). That’s sad. There’s no easy answers, as these issues are rooted not only in technological realities, but interwoven with deep seeded tribal sociological phenomena.
- Impatience thresholds are down to seconds, partly due to the availability of technology itself and our dependence on it, and on industrial productivity pressures.
What should CX pros do?
There’s no disputing that businesses need customers and have to make money to survive. How they play the game, the rules they follow, and the cultural approach they use defines both their character and destiny. When plotting how to engage customers with artificial intelligence and automation technology, consider the following:
- Those who play the long game win the long game. If the ultimate goal is improving customer experience, then factor customer quality of life into the long-term value equation. Depending on the definition and time horizon for winning, chances are good consumers will recognize (and reward) you for considering their best interests.
- Regulate, or be regulated. Incidentally, industry in general doesn’t have a great track record for self-regulation, so prepare eventually for some regulations in this area to emerge.
What should consumers do?
Throughout recorded history, hucksters have been selling unsuspecting consumers products they really didn’t need. That doesn’t mean every modern day CX pro inherits the label of huckster.
Quite the contrary, those who exchange value with consumers, and provide them with solid recommendations of products well suited to their requirements are effectively service providers. Those taking the easy path and simply pushing and deceiving others toward a clever sale, will rightfully earn the dubious timeworn label.
Consumers need to:
- Shop around. Although it can be a hassle, weigh the pros & cons of moving to another provider, versus amassing more points or transactions with a single provider. Be sure, nonetheless, to factor in all switching costs, including your time.
- When you shop, think outside the box to get a list of alternatives. The path of least resistance these days is to search on Google, but that list of both the paid results as well as the first page of organic ones is a limited (and often highly biased) set.
- Take occasional breaks from technology – Simply put, you don’t need to be online every minute. Don’t expect to completely kick the habit, same as you can’t stop eating food altogether. Research already shows, however, you should use technology in moderation or your long-term health may be at stake. A recent survey of 3500 adults shows stress levels likely rise when alerts go off, such as new emails or text messages. Like getting adequate sleep is necessary for good health, you’ll probably be more productive (and live a longer, heathier life) if you’re offline periodically.
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:
- Strive to know your customers as you would know yourself.
- Thou shalt be “Customer-Centric” and put no other products, services or stakeholders before thee.
- Thou shalt not make any graven image of customers, such as idol segments. Instead, thou will treat customers as individuals with personalized touch.
- Thou shalt not spam customers by carpet bombing with frivolity (causing them to take names in vain).
- 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.
- Thou shalt be empathetic and listen to customers, and act with fairness.
- Thou shalt not kill off customers with WMDs – “Weapons of Math Destruction” – such as artificial intelligence (AI) algorithms with bias.
- Love thy customer, their loyalty, and their journey, and calculate a true LTV (Lifetime Value), not just a year’s worth.
- Thou shalt not steal profits from the Customer Innovation Till. A tithe of earnings will be put in said till for pursuing true innovation.
- Thou shalt not covet thy customer’s wallet or share of wallet. You will get yours if you obey the other commandments.
There are many historic examples of using Complex Event Processing (CEP) and pattern recognition to sense and alert unusual activities or conditions. Some are not related to CX or CRM ( Customer Relationship Management) such as:
- 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
…and many others. What follows are some additional complex event processing & pattern detection use cases that represent interesting, somewhat complex and unique opportunities for how this technology might be applied to CRM in industries, and horizontally.
Vertical Complex Event Processing Use Cases
Fiserv: Consumer Banking and Credit Cards
- Unusual account activity or inactivity pattern
- Credit card spend activity use pattern
- 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
- Customer has increased roaming (or other unusual account usage) behavior
- Customer in route to a foreign country pattern
- Popular programming based on 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
- Customer struggling to get help pattern
- Payment due
- Strange returns activity
- Customer likely wants to cancel service
Marketing / Cross-sell & Up-sell
- Customer online interest in a product or service
- Customer in store interest in a product or service
- Customer in proximity of 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
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.
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.
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)
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.
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.
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.
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
[iii] Cathy O’Neil, 1st edition, Weapons of Math Destruction (New York: Crown), 2016.
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.
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.
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?
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
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.
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.
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.
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
In this 7th short video in my Machine Marketing Series, I give my views on the “Adtech Martech Convergence” specifically as it relates to using machine learning.
I cover four main layers of technology to consider as this conversion takes place:
- Customer Behavior Data – Why the Adtech Martech convergence may force a better coordination of this data as its compiled along the customer decision life cycle.
- Basic Analytics & Insights – I give some examples and why this area isn’t a huge concern or risk area.
- Advanced Analytics (Machine Learning) – I explain why integration here is key, and give some marketing use case examples.
- Programmatic Real-Time Automation – I outline key aspects of automation & workflow, and why these areas are essential to combine for a coherent Adtech Martech solution stack.
In this 6th short video in my Machine Marketing Series, I explain the Machine Learning Ecosystem for Marketing and review about 50 major players.
I cover the 6 categories of Machine Learning vendors in the landscape I created with Tier 1 & Tier 2 players:
- Category 1 – Paid Media / Digital Recommendation Vendors
- Category 2 – Big Data Cloud Platforms with Machine Learning Services
- Category 3 – Open Source Machine Learning Tools
- Category 4 – Business Intelligence Vendors with Predictive Analytics
- Category 5 – B2B Marketing / Account-Based-Marketing (ABM) / Lead Management
- Category 6 – Real-Time (Multi-Channel) Interaction Management (RTIM)
In each category I discuss the Tier 1 (largest players) and the Tier 2 (contenders / firms to watch).
Here are 5 things you will undoubtedly underestimate about Artificial Intelligence (AI) in 2017
“We are Now Controlling the Transmission”
If you aren’t familiar with the 60’s TV series “The Outer Limits” you need to watch this intro (its 58 seconds long).
Artificial Intelligence is controlling more than you realize, and in 2017, it’s going to accelerate. AI algorithms are already affecting which products & services you see when you perform a search – regardless of the device or interface. AI is pushing messages, offers, & advice to you that you may never have asked for. Artificial Intelligence is deciding what shows up in your news feeds, on the sites you frequent, and in the apps you use.
Sometimes those results will be relevant and useful. Yet often, they won’t be that relevant and will have bias. So beware, be careful, and try to understand better what is showing up and why. Be an activist. Consumerism is empowering. Use that right to force companies into making AI better, in essence taking back some control.
“We know who you are…and where you are…Well kind of”
A little over 2 years ago, I wrote a blog post entitled “Customer Data & Decisions – Reflections of Me.”
The forces I cover in this piece related to the collection of consumer data will continue to accelerate in 2017.
My advice is work to get your own data house in order next year. Go beyond the obvious, like pulling your credit report, to being prudent about how you safeguard and when you share any of your data. Its valuable, but its also vulnerable.
When you install apps, be thoughtful about which ones you grant permission to track your location. Did you know that Uber now tracks your location from the time you open the app to 5 minutes after you arrive at your destination? Uber values that data, and can leverage it in a variety of ways – but do you want them doing that? What have you received in return? How will they share that data?
Think about these factors when you decide how much data to share, and spend time to understand how firms are using and compiling YOUR data. In some cases, you may not have direct control, but nonetheless it’s important to understand what’s happening since after all, it is your data.
I’m not opposed to companies collecting relevant data to feed into Artificial Intelligence systems to use in a responsible way to add value to my consumer experience. Yet I expect value in return, and I expect a firm to respect my privacy.
You have a vote in Artificial Intelligence evolution. Don’t let it run amuck
You vote with your wallet & purse everyday. Intelligent devices are popping up everywhere and dropping in price, and firms are dropping Artificial Intelligence into almost everything we buy. From voice activated appliances, to connected cars, we now live in a world where having a conversation with a computer is commonplace.
Again, what you buy helps decide which AI infused products thrive and which ones die. Buy wisely. Perform some reviews. Render your opinion. Your opinion on a frying pan may mean a little less to society then your review of how an interaction went with a Chat Bot.
More money than you can imagine will be spent on it
How much will be spent on AI? No one really knows. This post has some interesting estimates. Let’s just say that all of us will underestimate the amount of energy that will be put into AI related research & technologies in 2017. It’s going to be HUGE – and it’s not just in 2017. This is a revolution happening.
AI will have bias because of its creators & the data it feeds on
Artificial Intelligence actions & outcomes must be monitored & governed by its carbon-based creators. Yet ironically, its creators are the ones that introduce bias into the AI brain.
From biased data (think about how a hiring bot might make decisions about which candidates are most likely to succeed in a job….it will use past employee performance data…which is biased by the hiring practices of the past), to the bias rules & code from the inventors.
Its incumbent on us all to be mindful of these tendencies, and advocate aggressively against bias in the machine.
Note: These views are my own, and not that of my employer
In this 5th short video in my Machine Marketing Series, I explain Machine Learning Measurement and monitoring techniques.
I cover the concept of a Model Factory and Dashboard where you can:
- Measure Machine Learning Lift employed in Marketing and CRM programs
- Monitor Model Features and Model Data use
- Pit models built for Customer Experience (CX) against each other (Champion / Challenger)
- View KPIs (Key Performance Indicators) on a business Dashboard
Use these tips & best practices to benchmark against your efforts, and compare how your platform stacks up in using machine learning in your marketing and Customer Experience programs.
Getting positive marketing results for your customer engagement efforts is what it’s all about.
In this episode, I explore what to expect in terms of outcomes when you effectively employ machine learning (ML) and artificial intelligence (AI) in your marketing efforts.
I cover performance ranges I’ve seen actual companies achieve including:
- Churn & Attrition reduction
- Response Rates for your marketing execution & tactics
- Sales Lift increases which means revenue to the bottom line
- ROMI – Return on Marketing Investment
- NPS – Net Promoter Score improvements
- AHT – Average Handle Time reduction
You can use these figures to benchmark against your efforts, or even help you build a business case for embarking on using machine learning in your marketing and Customer Experience programs.
I created this graphic so you can map out your own partner & vendor Martech Ecosystem. The “Major Players” shown are examples and are not intended to be comprehensive.
Its a qualitative assessment anyway, but I have used my experience in dealing with some of the worlds largest companies and thus which vendors have emerged as the 800 lb gorillas in their respective categories.
If you are not already familiar with the extensive work done in this are by Scott Brinker, please check out his website http://chiefmartec.com. Scott has done a fantastic job cataloging the thousands of MarTech companies in a variety of different areas of specialization. His latest super graphic stuffs an amazing 3,874 companies onto one page. You can view it here: http://chiefmartec.com/2016/03/marketing-technology-landscape-supergraphic-2016/
By contrast, my picture is intended to filter that list down to some of the major players in key areas, and is especially useful to larger corporations, or software vendors that seek to operate & partner for selling to enterprise class organizations.
If you would like a copy of this .ppt, click here.