Profitability Impact

Profitability Impact

Finally, the profitability impact of improving service quality varied greatly in different customer tiers. An across-the-board service quality improvement of the key drivers (approximated by a 0.1 increase in average satisfaction with each driver in each tier) resulted in a projected 3.65% increase in incidence of new accounts in the top 20%, but only a 2.00% increase in the Lower tier. This result suggests that the Top 20% was almost twice as responsive to the changes in service quality than the lowest 20%. When examining the projected increase in average account balance, the results were even more encouraging. The projected increase in average account balance was $6.19 in the top 20%, but a meager $0.69 in the Lower tier. Here, the Top 20% appeared to be almost 10 times as responsive to changes in service quality. Finally, the projected increase in average profit per customer was 15.7 cents in the top 20%, but 0.5 cents in the lowest 20%. Again, the top 20% provided a substantially greater return on the service quality improvement. Of particular interest was that simultaneous improvement of the key drivers for both tiers produced a projected 89% of the new profits in the top 20%, while only 11% of the new profits could be attributed to the lower 20%. This was an even higher percentage than the current percentage.

The Need for More Tiers

The "80/20" two-tier scheme that many companies use assumes that consumers within each of the two tiers are similar to each other. We contend, however, that this "best" and "rest" customer division is rarely sufficient. Just as we showed above the dangers in combining data from two tiers--the results were muddied and the average did not represent either tier well--we contend that the lack of distinction among the "rest" of the levels misses important differences in consumers. 

In the two-tier analysis we conducted and in the strategies of companies that distinguish only between two groups, the customers in the large lower tier are indistinguishable from each other. This likely masks differences in demographics, perceptions and expectations of service quality, drivers of new business, and the profitability impact of improving service quality. Specifically, if we had observed the demographic differences across four tiers, rather than two, we would most likely have seen that the lower the tier, the younger the customer and the lower the average account balance. Many banks realize that their least profitable customers are students who have no income and are expensive to serve because they bounce checks and require extra handling. In the two-tier scheme above, however, this group was not explicitly articulated and we therefore cannot tell the difference between them and the rest of the 80%. 

Furthermore, in our example we cannot tell whether this lowest-tier group views quality differently. Banks are coming to understand that convenience is the critical factor drawing the youngest customers to banks. Fortunately, a bank that knows this need focus only on that element of quality with that segment, thereby reducing costs that would be spent if they were grouped into the rest of the 80% who also wanted the attitude factor. We might also have observed differences in drivers and incidence of new business, and--most importantly--in the profit impact of investing in different tiers. 

Most companies realize that their customer set is heterogeneous but possess neither the data nor the analytic capabilities to distinguish the differences. We suggest that it is highly worthwhile to do so, and to distinguish more than just the traditional two levels of customers.