Saving customer rapport in the age of the machine

Arguably the hottest thing in marketing right now is the use of machine learning /AI to identify primary customers and personalize the customer experience. The capacity for technology to learn and predict customer preferences and needs enables marketing leads to feel as though they are relating to customers as individuals and, by extension, building personal connection between their brand and the customer.

This would be great news for brands, if only it made sense. It does not.

Certainly, the more precisely machine learning/AI can predict customer preference, the more likely the customer experience can be enhanced. And certainly, if an organization delivers a positive experience consistently, the “brand” will be associated with a positive experience. In this way, the brand can build a reputation that draws customers to return for yet another positive experience.

But the capacity for brand building via machine learning/AI ends there. This is because “experience” and “reputation” are only a part of the equation that comprises brand loyalty, and relatively tenuous ones at that.

I have recently enjoyed my interaction or experience with both Verizon and Bank of America because their representatives have been polite, helpful, and have anticipated many of my needs. The quality of the product has been consistent and somewhat tailored and the service has been competent. However, if another entity offered me a better deal (because of its ability to predict my needs) and was also nice, polite, consistent and competent, I would switch in a heartbeat. Why? I have no connection with the Verizon or Bank of America brands. As brands, they either mean nothing to me or represent something negative with respect to how I want or need the world to be.

Brands are, and always have been, cultural symbols. They build a true loyal following if and when the values people seek to promote and perpetuate are embodied in the brand and the brand can then be used to amplify the values dear to the individual or group. Think Nike, Apple or Patagonia. As cultural symbols, brands afford customers the ability to leverage them to further their own values and, by extension, strengthen their chances for shaping the world in a way that ensures survival for themselves and their loved ones, or even their companies.

Brand loyalty is the difference between having a customer who is using you temporarily for an immediate need or desire (price, location, expediency, experience), and a lifetime customer who invests in your brand (willing to pay more, willing to excuse mistakes) because, well, because of the brand. When people invest in other people because they recognize a connection or kindred alignment of values or purpose, we call this rapport.  It is rapport that is the gateway to brand loyalty.

To understand better the limits of machine learning/AI to build brand loyalty, let’s look at one schematic of how and why humans make the decisions they do. Below is a figure that represents the scaffolding that underlies human decision-making. (You may have an alternative concept of how decisions come to be made, but this is my post, so we will use my schematic.)

Figure 1: Theoretical scaffolding depicting the way in which an inner narrative about the way the world works is the origin and ultimate explanation for human decision-making.

Let’s understand the decision-making scaffolding tracing it from the base upward:

  • At the base or origin of decision-making is the Guiding Narrative®,an inner narrative or story we tell ourselves, most often subconsciously, about how the world works. (“Guiding Narrative” is a specific phrase and approach my firm, Metro Tribal, uses to discern and describe this inner story). The Guiding Narrative is the product of our innate orientation toward the world, our physicality, education, experience, and multiple other factors that result from our constant interaction with stimuli. Ultimately, what forms at this level are our distinguishable assumptions, beliefs, and norms. These combine to create the inner story. The important thing is that this story defines reality for us and provides a blueprint for survival and success (success is a means to ensure survival). It literally tells us what to value and trust as a means for survival as we navigate life.

  • The next level, inspired by the Guiding Narrative, is our motivation. This level is where our values reside. We value certain things as a means for survival. That is, in the story we tell ourselves, our “blueprint” for how the world works, if the things we value do not exist or persist, something very bad is likely going to happen and we may be in danger of being weakened or destroyed altogether. Values serve as the central means to direct our survival, so we are motivated to amplify and perpetuate them.

  • The next level, interpretation, is our gateway, or interface with the world. Here, values also reside but perform a different function than they do on the motivation level. Here, values serve as a filter through which we process stimuli. That is, our values determine how we will interpret stimuli and whether we will allow the stimuli in or we will reject it.

  • Finally, we reach the decision level. This level is our expression of anticipation about cause and effect of our decision. The cause and effect we anticipate stems from our Guiding Narrative and is the direct product of our values, which have filtered information and motivated us to move in a particular direction for our survival.

Enter big data and machine learning. These powerful computing processes focus on the top layer of this scaffolding — the decision-making activity of individuals and groups. At this level, what is revealed is the mere product of an entire scaffolding of inner processes. Little is known at this level about why an individual or group would want to connect with and/or support a brand.

Machine learning provides a “guess” at why a person may connect with a brand as it discerns predictive relationships between variables. However, machine learning is inherently limited to various versions of the “decision” itself, or “tip” of the scaffolding as a source of inputs for its algorithms.

Machine learning inputs are pieces of information based on actions customers have taken (decisions), demographic information (life decisions or circumstances), or even estimates or opinions customers offer in response to questions about themselves (decisions of what they themselves perceive as well as what they will share about their own psychographic life on a survey, for example).

Machine learning does not have access to, nor can it discern, the origin of values that lead to decision-making. If your goal is to build rapport and trust with customers, you need to know the values that drive them and the inner story that evolves and continues to define what they should value or trust.

Rapport and trust are built upon a common connection and purpose, a link that makes customers feel understood, validated, and energized rather than simply “predicted.” This is a subtle but consequential distinction that determines whether a brand simply delivers a highly-satisfying experience or rapport, and eventually brand loyalty, is established. Once rapport is established, loyalty can develop if the brand continues to align with the individual’s or group’s need to amplify their values.

We should deploy machine learning/AI for what it does best – predicting the next thing an organism, machine or a person is likely to do or want and appropriately address that action or need.

Some may argue that brand loyalty doesn’t matter – that it so long as a company can capture customers via its predictive capacity and provide personalized and satisfying experiences, there’s no need for establishing a deeper connection to the brand. In some, highly commoditized industries where brand is unimportant or the need for the product or service is very infrequent, that may be true. But that’s rarely the case., and certainly not the case in more high-touch services and products.

Machine learning itself will become commodified by its own design and will simply form a higher bar for customer expectations. True, those who become really adept with what they do with their predictive capacity may win over those that don’t in satisfying customers and building volume. But unless there’s a foundation of rapport and loyalty, customers’ tendency to stay with a brand will likely depend entirely on the last experience they have with the brand or the first they have with a competitor’s. MT