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Years ago, Michael Kami was the chief strategic planner for both IBM and Xerox. He worked for the two organizations when each set an enviable standard for innovation, marketing, growth and profitability.
As a strategist, Kami appreciated the difficulties of segmentation. And while he embraced systems and processes, he knew their limits. He was skeptical of efforts to confidently assign consumers to neat and tidy segments.
Kami reminded us that marketing leaders who overlook and underestimate the quirks, foibles and follies of irrational consumers are perhaps themselves somewhat delusional…
“Keep in mind that you can’t control your own future. Your destiny is not in your hands; it is in the hands of the irrational consumer and society. The changes in their needs, desires and demands will tell you where you must go. All this means that managers must themselves feel the pulse of change on a daily, continuous basis. They should have intense curiosity, observe events, analyze trends, seek the clues of change and translate those clues into opportunities.”
The Confusing Clues Irrational Consumers Toss Out To Marketers
Behavioral data, lifecycle segmentation, analytics and their big data brethren ultimately dance to an uncomfortable tune. Irrational consumers call the shots while smart marketers try to aim their shots.
It takes a confident marketer to hear the music of their customers, to sense when it’s about to change key and to have a good idea as to where it’s about to lead.
It also takes a confident marketer to acknowledge that organizations don’t know nearly as much as they would like about the actual reasons why consumers do business with them.
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“Know your customer” has been a mantra for as long as business has been conducted. Similarly, knowing why your customer does certain things remains as elusive as ever.
Irrational Consumers Can Sink Marketing Strategy
So much of marketing strategy is wrapped around a knowledge of the consumer that failing to take their inexplicable behaviors into account can easily derail a campaign. Or even bankrupt a business that fails to achieve relevance.
The process of creating an accurate customer lifetime value bumps up against this same challenge. Purchasing patterns have a way of unraveling, often unraveling more quickly than marketers would like. Anticipating the future of purchase patterns, acknowledging unknowns and assigning percentages to the likelihood of these patterns continuing is one way to approach uncertainties.
There is so much we don’t know. We often have no idea why someone who has been brand loyal to a product for years, even decades, suddenly makes the switch to a competitor. If the brand management team knew what sent these once loyal customers scurrying, pre-emptive moves could conceivably take place.
The precise nature of this triage can be difficult to pin down. Just as shoppers in the brick-and-mortar world are conditioned to tell an inquisitive clerk they’re “just looking,” a similar unaccommodating response is likely when they’re asked why they moved on and left a brand behind.
Putting Stock In Predictive Analytics
The data that makes predictive analytics possible may be dated, incomplete, or otherwise flawed. Even when pristine there are limits to the illumination data can provide.
Research is saddled with constraints. As Ernest Dichter explained in his classic book, “The Strategy of Desire,” “We generally have a choice of two types of research; descriptive research, which tells how many did what; and diagnostic research, which tells why what happened did happen. While there is an interesting relationship between the two, it is diagnostic research that provides the tools for creative predictions of human actions in the future and make possible the correct strategy.”
When marketers mine data with an assist from AI to try and identify patterns presumed to be hidden in shadows humans can’t discern, there is a glimmer of hope that useful insights will emerge.
These hopes should be tempered. Marketing remains a blend of art and science, no matter how much accurate data is at hand. Our occupancy of a segment in a marketer’s database is subject to unknown whims. We can move out instantly. Our departure may not be noticed let alone analyzed for quite some time.
Human nature is confounding. Our motivations are often murky, our intentions often contradictory. In the years ahead, it will be intriguing to watch how well AI and predictive analytics turn these ambiguities and unknowns into something useful for marketers.
