The company might have had an intuitive sense of these findings. However, the intelligence from the conjoint analysis was definitive. The results have played a role in changing the company’s product line, changing what happens within distribution channels, and changing how and where the company spends its marketing dollars.
Protecting Profits at a Bank
In another recent example, a European bank picked up signals that regulators were going to force it to become more transparent about the costs of loan protection, a product the bank made available to consumers who took out loans. The bank made a considerable profit selling insurance that guaranteed payment if a borrower lost his or her job or otherwise suffered an interruption of income. What would happen to the business model if regulators insisted on changes? Would there be a way to keep making money in the business of unsecured loans and loan protection?
The bank used a conjoint analysis survey of 1,600 people who had unsecured loans to estimate price elasticity for the loans themselves and for loan protection insurance. This was a way of anticipating the options it would have in the event that the regulatory environment changed, and banks were forced to raise (or lower) prices on either loan or loan-protection products.
The conjoint analysis answered the price elasticity question in the aggregate. After the bank clustered the panelists into five segments, it was also able to answer this question in a more granular way. For instance, customers in a segment the bank called “bargain hunters” were very sensitive to pricing. This group would not pay more to take out a loan or to insure it. By contrast, customers in a segment designated “personal bankers” (those who liked the high-touch approach, were willing to hear advice, and were open to special offers) were not particularly price sensitive. Even with a regulatory change, the bank could sell this segment higher-priced unsecured loans and loan protection and profit from it.
Indeed, one of the intriguing things about this bank’s use of conjoint analysis was the broad utility of the results. Although the analysis started as a way to test price elasticity and prepare for external changes, the information the conjoint analysis generated — not only about how customers would respond in the event of a price increase, but also about more basic findings such as how people make borrowing decisions and how they think about financial providers — allowed the bank to identify tailored product strategies that would appeal to all its customer segments. The company decided its existing product would work for some segments, but that it should probably develop a no-frills product for the “bargain hunters” among its customers and a premium product for its “personal bankers.”
Segmenting for Growth
In an era of cautious consumer spending, many companies are looking for new ways to identify growth opportunities through improved customer insight. Conjoint analysis is at the forefront of this effort. The analytic rigor it brings is helping creative companies move forward with promising initiatives that they might have thought sounded good but couldn’t agree to implement without the data to back them up. Other companies find that it is generating approaches to organic growth that they might not have come up with on their own.
In this way, conjoint analysis, which has historically informed relatively narrow product decisions (enhance this feature, remove that one) is turning out to have bigger strategic implications. It can fundamentally change companies’ perceptions about where opportunity lies and how to pursue it.
Reprint No. 00092
- David Meer is a senior executive advisor with Booz & Company’s consumer, media, and digital practice, based in New York.