Imagine that you have two customers to whom you are selling beef. One is a greasy-spoon hamburger joint and the other is an upscale French bistro that offers a pricey version of steak tartare as one of its gourmet entrees. In figuring out the terms of offer for each restaurant, would you create a profile of an “average customer” based on these two data points and sell a single variation of medium-grade beef at a mid-range price to both the burger joint and the classy bistro? Of course not – you know perfectly well that approach would please nobody and result in two lost sales. Yet that is exactly what sellers in many industries are doing every day – marketing to some notional “average” customer that in reality does not exist. It may seem less absurd when the average is based on thousands or tens of thousands of customers as opposed to two – but it is no less flawed as a marketing approach. Every single customer has a unique set of needs, preferences and priorities. It is your job as a seller to tailor your offers as close as possible to each unique demand curve. Fortunately, technology and science make that daunting challenge possible.
Segmentation – Top-down Guesses
Of course, marketers realized years ago that customers are not all the same. They created a way to incorporate this realization into their decision making, which we all know today as customer segmentation. Segmentation can make average customer profiles seem somewhat more reasonable than lumping all customers into one bucket – for example, one could reasonably assume that the hamburger joint and the French bistro would not wind up in the same segment. But ultimately, segmentation is about foisting top-down guesses about what are meaningful differentiators onto a universe of customers. In the early days of segmentation the idea was to delineate segments by zip code, assuming that the economic characteristics of a customer’s place of residence were a good indicator of his or her likely spending habits. This became popularly known as demographics. In the 1980s “psychographics” became the in-vogue approach to segmentation, for better or worse bequeathing to us “chardonnay-drinking Volvo-driving NPR-listening urban dwellers” and the like. Again, psychographic categories represent guesses about the attributes likely to correlate to specific consumer tastes and preferences.
Flaw of Averages
What demographics, psychographics and any other kind of -graphics have in common is that someone dreamed them up as a theory about what drives customer behavior and then used the theory to make determinations about how to market more effectively to their customers. But ultimately with this approach you are still dealing with flawed assumptions about the needs and preferences of your actual customers. Rather than denoting your entire customer base with one Average Customer you now have one Average Customer for each segment. Put another way, not every “Chardonnay-sipping Volvo driver who lives in zip code 06830 (Greenwich, CT)” is the same person with the same needs and preferences. These top-down assumptions cannot begin to untangle the specific multitude of factors that drive individual customers to particular categories and specific product SKUs within those categories, let alone the more nuanced considerations of what messages they are likely to respond to, when they are likely to be responsive and what other products they might be persuaded to buy in the same time frame.
Let the Data Do the Talking
A better approach than top-down guesswork is a bottom-up approach that lets the data speak for themselves. The insights are there in the transaction records that come into your data environment every evening, and increasingly from other sources as well such as social media networks, surveys and third party data providers. You do not need to embellish these with guesses about the best way to segment your customers. What you need is a rigorous methodology for formatting, analyzing and acting on the insights the data provide. What are the factors to which certain customers respond? What like tendencies may be seen between different types of customers, and why? What products are your customers buying from other suppliers that they could be buying from you, if suitably induced? Let the data reveal these patterns through applied scientific methods, rather than making top-down assumptions about which attributes go with which customer segments. You may wind up knowing your customers’ buying habits better than they themselves do.