An Empirical Test of the Conditions in a Two-Tier Situation
Virtually all firms are aware at some level that their customers differ in profitability, in particular that a minority of their customers accounts for the highest proportion of sales or profit. This has often been called the "80/20 rule"--twenty percent of customers produce eighty percent of sales or value to the company. We recently conducted an empirical study to examine this simple "80-20" scheme.
A major U.S. bank provided profitability information about retail products and customer information files with descriptive information including average account balance, average profit from account, and average age, gender, and income. Data on a random sample of 796 of these customers were merged with responses to a service quality survey from the same set of customers. Eight months later, information regarding the amount of new business, including both the incidence and volume of new business (revenue from new accounts), was added to the data file by examining behavior following the survey. In this way, service quality measures could be used to predict future behavior using a cross-sectional, time-series approach.
Demographic Differences
We examined differences in customer descriptive statistics, service quality perceptions, drivers of incidence of new business, and drivers of volume of new business across tiers using various statistical analyses.(n12) We also projected both the increase in the percentage of customers who would open a new account and the increase in the average account balance. Multiplying the projected average account balance by the average profit per account balance for each tier yielded an estimate for the projected increase in average profit per account. Multiplying that by the number of accounts yielded the total projected new profits from each tier.
We then divided the customer base into two customer tiers: the most profitable 20% (top 20%) and the least profitable 80% (lowest 20%). The results met all the conditions described above. First, customers in different profitability tiers had different customer characteristics. The top tier had a higher percentage of women than the lower tier, an average account balance about five times as big, and average profit about 18 times as much. The top 20% was also older than the lowest 20%, had more upper-income customers, and had far fewer lower-income customers. The top 20% produced more profit per volume of business, with an average profit per account balance of 2.53%, versus 0.71% for the lowest 20%. Finally, the top 20% produced 82% of the bank's retail profits, an almost perfect confirmation of the 80/20 rule in this profit setting.
Views of Service Quality
Second, customers in different tiers viewed quality differently. The top 20% viewed service quality in terms of three factors: attitude, reliability, and speed. By contrast, the lower 20% had a less sophisticated view of service quality, viewing service as only two factors, attitude and speed, with slightly different interpretations of the factors. The reliability factor was not a driver for the lowest 20%. A particularly compelling finding emerged from these data. When we combined all customers into a single group, all appear to want the same factors and the factors meant the same thing to both groups. The important insight here is that blending customer tiers resulted in an imprecise view of what service quality meant to the customer base.
Drivers of Incidence and Volume of New Business
Third, we found that different tiers had different drivers of incidence and volume of new business. Since we measured what customers did after they reported what was important to them, we captured what actually drove customers to make purchases, rather than what they thought would make them do so. For the top 20%, speed was key to driving incidence of new business whereas attitude was the key driver for the lower tier. As before, analyzing the entire customer base as a single group would have been misleading. Both the combined attitude/ reliability factor and the speed factor were key drivers for the group as a whole, but the combined analysis would not reveal the fact that different strategies should be used for different profitability levels.
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