Heavy buyers, (also known as “heavy users,” “high rollers,” “big spenders”) are the worst target for most marketing programs.
Why do so many companies love this target? Companies inevitably answer: Because they use more product than anyone else, dummy.
One can trace this idea to Dik Warren Twedt, a researcher who coined the term “heavy-half” in 1964 to describe the market segment that accounts for the lions share of a product's sales.
Almost always, a small segment of the population accounts for the most sales in any product category. This is the ubiquitous 20/80 Law—the 20 percent of the beer drinkers consume 80 percent of the beer—applied to marketing.
But heavy users are often price conscious, deal prone, and consequently disloyal to the brands they buy. Winning them today with a great offer is no grand accomplishment because you’ll lose them tomorrow into a competitor’s deal.
Other heavy users are psychologically locked to a competitive brand. They are perfectly happy with the brands they currently buy. They are somebody else’s best customers. They are very difficult to move.
Suppose a homemaker buys twenty jars of prepared spaghetti sauce a year and the average is five. Is that person a great target for our sauce? Not if she is buying the cheapest jar on the shelf and ours is expensive. Not if she cares about smooth texture and we feature chunky vegetables. Not if she is so loyal to the brand she’s been using she would switch stores before she would switch brands.
Heavy users may also have demographic and media-usage profiles similar to everyone else in the category.
Most target groups—18-to-49-year-old women, Heavy Users, Baby Boomers, Gray Foxes, Belongers and Achievers (to mention two well-known psychographic segments)—are far more heterogeneous than homogeneous. Significant differences hide behind a superficial veil of similarity.
When a company considers a target such as heavy buyers, it is asking in effect: “Are these people different in terms of anything other than the variable that defined them in the first place?” If heavy buyers of spaghetti sauce watch daytime television—to make up an example—and light users do not, the company then knows how to reach them.
Unfortunately, these people generally are not especially different in terms of anything other than the variable that defined them in the first place. We cannot define them by income, education, age, the television shows they watch, the magazines they read, their attitudes toward cooking, use of the internet or anything else. Heavy packaged goods buyers are rarely very similar in terms of anything other than their usage patterns and, perhaps, family size—big families do tend to buy more spaghetti sauce, toothpaste, detergent, television sets, and long-distance services than individuals living alone. But aside from that, the only thing that clearly distinguishes women who buy a lot of prepared spaghetti sauce from women who don’t buy much prepared spaghetti sauce is that they buy a lot of prepared spaghetti sauce.
But other than the heterogeneity question, a company should ask: “Are heavy users different from light users in terms of how they act in the supermarket?” It’s asking about behavior rather than about demographic, psychological, or geographic characteristics. The answer to this question is all too often, “No.”
Yes, it is true that heavy users gravitate toward cheaper, more heavily promoted brands, but then again today most brands today are heavily promoted. As a result, the brand buying behavior of heavy users is more similar to light users than many of us would like to think in categories as different as milk, light bulbs, long distance services, and, yes, spaghetti sauce.
A company can learn fairly easily that heavy users of spaghetti sauce (or whatever) buy more spaghetti sauce than light- and non-users. But that hardly helps. A marketer really wants to know: Which people—heavy users, light users, and non-users—can I induce to buy my spaghetti sauce? How much business can I generate from this group and what will it cost me to do so?
The following is an illustration of this phenomenon from an analysis of a financial services category. The client loved heavy users until we did this examination of heavy users versus more than a thousand alternative target groups in terms of three criteria:
- Sales potential (which reflects purchase rates and prices paid),
- Profit potential (which takes into account the likelihood of winning each customer and the profit margin in the channels they shop in); and
- Return on investment (which indexes the ratio of profit potential to the expected media cost of achieving that potential).
Target Group Profitability Analysis
||$ Sales Potential
||$ Profit Potential
|Middle Socio-Economic Status
We show only 8 of approximately 1000 targets in this table. Note that heavy users account for the lion’s share of the sales potential ($330 million), yet only a small share of the profit potential ($18 million). The index, which is based on all thousand-odd targets, reveals that their profitability is forecast to be only 40 percent of the average target. How come?
Because in this case the heavy users, given their profit potential, are simply too difficult to dislodge (they are reasonably happy using a competitor’s product), too sensitive to price promotion and too expensive to reach with media.
The “winning” target we might add is not shown here. Needless to say, it was even more profitable than the “young professionals.”
Does this mean that heavy users are always the worst target for marketers? No! We can think of a few times in our own consulting practice when heavy users turned out to be a fairly good target. There is, on the other hand, no reason to believe a priori that heavy users are a great target. Usually, if you do the kind of analysis that we have done here you'll find the heavy users to represent a poor return on investment.