When Dow Jones decided to revamp the Wall Street Journal in the mid-2000s, the newspaper had just endured five years of flat circulation and advertising revenue, and the whole industry was ailing. Although the Journal didn’t want to alienate its core readership, it wanted to attract new readers — in particular a younger demographic that advertisers would value. Dow Jones knew it had to make changes to its then 125-year-old newspaper. But the company’s bigger purpose was to understand the needs of an emerging segment of business news consumers that the Journal was not successfully reaching.
To help develop its strategy, Dow Jones employed a variant of conjoint analysis, a technique that has been widely used in market research for 30 years. In a traditional conjoint analysis, survey respondents are asked which products or product attributes they value as a trade-off between two or more options, repeated in enough combinations to yield a reliable ranking of each attribute’s importance. Dow Jones used this type of analysis in a new way, to identify prospective readers and reveal their preferences. After its redesign to attract this new customer segment, the Journal (now part of the News Corporation) saw a 35 percent improvement in its efforts to add new subscribers through direct marketing, reversed a three-year slump in ad sales, and experienced an annual revenue improvement of US$25 million from new programs and pricing initiatives.
In fact, for companies in industries as varied as luxury goods and retail banking, conjoint analysis is emerging as a strategic tool, providing actionable intelligence businesses can use to go beyond product optimization to support organic growth. The methodology needed to use conjoint analysis in this new way is very similar to the methodology that traditional practitioners of this type of analysis have used, but has a key variation: The marketing team uses the results to organize customer groups (and prospects) with similar preferences, providing a more detailed view of the categories they fall into, the needs they have, and the likelihood that they might become bigger (or smaller) sources of revenue.
In other words, conjoint analysis has become a new source of insight into customer segments. Of course, it would be hard to find a company that hasn’t done some kind of customer segmentation, and using conjoint analysis is certainly not the only way to achieve it. Companies usually have a sense of who their real and prospective customers are, and have an idea of what each segment considers important. But by segmenting customers with the help of conjoint analysis, companies can develop a more layered form of intelligence, with implications for which segments to prioritize, which value propositions to offer them, and how to market to them.
Looking for Luxury Shoppers
In recent years, a manufacturer of luxury gifts had become dissatisfied with the pace of growth in one of its largest geographic markets. Was the company targeting the wrong customers? Using the wrong materials? Supporting a brand with an undifferentiated value proposition? Advertising ineffectively? The company thought that if it could answer these questions, it would gain some of the insights needed to transform its organic growth strategy.
Using traditional conjoint analysis techniques, the manufacturer surveyed 2,000 luxury gift–buying consumers to find out the extent to which they were price- and brand-conscious; valued materials such as fine leather, fabrics, and metals; and wanted their gifts to elicit oohs and aahs from friends and family. The manufacturer then combined the data from the conjoint analysis with the results from other survey questions to define five customer segments and decided it had headroom — an opportunity to pick up significant market share — in several of those segments, including a group it called “classic shoppers.”
Thanks to the conjoint analysis survey, the manufacturer knew that in the “classic shopper” segment, customers ascribed great importance to prestige, cared a lot about high-quality materials, and preferred designs that made bold statements. The least important attribute to this customer segment was price — these customers didn’t mind paying a premium to get what they wanted.
The company might have already had an intuitive sense of these findings. However, the intelligence from the conjoint analysis was definitive. The results of the analysis have played a role in changing the company’s product line, changing what happens within the company’s 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 was picking 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 held unsecured loans. The bank didn’t make money selling unsecured loans, but it 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 the bank designated as “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. There would be ways, even in the event of a regulatory change, of selling this segment higher-priced unsecured loans and loan protection and profiting 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 off 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 of those 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 may have thought sounded good but couldn’t agree to implement without the data to back them up. Other companies find that it is generating avenues for organic growth 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 is a powerful tool that can fundamentally change companies’ perceptions about where opportunity lies and how to pursue it.
- David Meer is a senior executive advisor with Booz & Company based in New York. He specializes in customer insight and demand analytics, with a particular focus on helping companies use statistical approaches to identify organic growth opportunities.