An old method for finding new headroom: Using conjoint analysis to shape organic growth
For 30 years, conjoint analysis has been one of the most widely used tools in market research but it turns out that it is also extremely well suited to doing customer segmentation work. Conjoint is emerging as a strategic tool, providing actionable intelligence they can use to inform organic growth strategies. This perspective offers three examples of this more strategic application of conjoint methodology.
An old method for finding new headroom Using conjoint analysis to shape organic growth
About the authors
Amsterdam Marc Hoogenberg Principal +31-62395-4065 marc.hoogenberg @strategyand.pwc.com Cleveland Steven Treppo Partner +1-216-696-1570 steven.treppo @strategyand.pwc.com
New York David Meer Partner +1-212-551-6654 david.meer @strategyand.pwc.com
Shanghai John Jullens Partner +86-21-2327-9800 john.jullens @strategyand.pwc.com
David Meer is a partner at Strategy& 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.
This report was originally published by Booz & Company in 2010.
For 30 years, conjoint analysis has been one of the most widely used tools in market research. Its methodology of getting survey respondents to say which products or product attributes they value — done as a tradeoff between two or more options and repeated in enough combinations to yield a reliable ranking of each attribute’s importance — has allowed hundreds of companies to make well-informed decisions about which product features they are going to offer, in what combinations, and at what price. But it turns out that conjoint analysis is also extremely well suited to doing customer segmentation work. And in more recent years, some companies have used conjoint to develop an aerial view of their prospective customers — the categories they fall into, the needs they have, the likelihood that they might become bigger (or smaller) sources of revenue. For some of the companies that have used it this way, conjoint is emerging as a strategic tool, providing actionable intelligence they can use to inform organic growth strategies. This report offers three examples of this more strategic application of conjoint methodology. First, it shows how a luxury goods manufacturer used conjoint analysis to reposition itself to get more business from its most desirable customer segments. It also shows how a European bank used conjoint to prepare itself for an expected regulatory change; the bank also ended up with the insights needed to reposition a whole section of its product line. Finally, it shows how one of the world’s biggest and most successful newspapers — the Wall Street Journal — used a variant of conjoint to add younger readers and get a larger share of the advertising market.
New application of a decades-old tool
When Dow Jones decided to revamp the Wall Street Journal in the mid-2000s, it had a couple of very important goals. It wanted to keep the Journal from getting caught in the circulation declines that were hurting other newspapers. And it didn’t want to achieve growth by adding just any readers — it wanted to add readers that advertisers would value, including some who were younger. To help determine what changes to make, Dow Jones employed a variant of conjoint analysis, a type of statistical analysis often used by companies to decide which product features they should change, add, or cut. Conjoint analysis and its offspring, such as Maximum Difference analysis (MaxDiff), uncover consumer preferences by asking people which attributes of a product are most important to them. In the case of employees filling out a survey about the retirement program at their place of work, the options may include a wide range of mutual funds, a guaranteed level of income in retirement, protection against inflation, easy access to an investment advisor, and so on. By presenting the same options over and over, in different combinations, the research yields a clear ranking, from most to least beneficial, of the features the respondents value. Some form of conjoint analysis is probably behind available computer configurations, new car options, and credit card loyalty-point programs. The Journal, however, was using conjoint analysis in a broader way. To be sure, Dow Jones wanted to make informed decisions about the changes it would make 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. Meeting those consumers’ needs, Dow Jones’s chief marketing officer believed, would give the Journal the best chance of increasing its share of the most desirable audience and, in doing so, bolster the paper’s finances. In other words, Dow Jones was using conjoint analysis not merely for optimization but to support a new growth strategy.
Dow Jones may have been a little early in this regard, but it is not alone. At this time of economic challenge, 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 companies move forward with promising initiatives they thought made sense but couldn’t agree to implement. It is giving other companies an even more fundamental advantage, by uncovering actionable customer insights that in turn generate concrete ideas about how they can best pursue organic growth.
Conjoint analysis: Historical uses
In the two or three decades that it has been in wide use, conjoint analysis has become an alternative to traditional analytic techniques and a widely used tool when companies want to upgrade their products in ways that customers will value. It uses a trade-off methodology to give survey respondents a choice of two product bundles — a gold necklace for $185, say, versus a solid silver necklace for $125. The respondents pick the one they prefer. Another choice is presented, and then another — as many as two dozen in all. The purpose of the repetition is to find out which underlying attributes the respondent values; for a jewelry manufacturer, the attributes might be price, quality of metal, and brand, among others. Conjoint is not limited to relatively simple product categories like jewelry; it can also be part of the methodology for determining customer preferences on highly complex products such as midsized jets (see Exhibit 1, next page). MaxDiff is similar except that the attributes are broken out and made explicit individually, as opposed to being packaged together in a product bundle. Dow Jones used MaxDiff when it was trying to determine which attributes readers and prospective readers considered most important. Once the conjoint survey has been completed, the results are fed into a software program that uses regression models to quantify preferences based on what the respondents said. In the case of a luxury manufacturer, it may become clear that the company should focus its attention on bridal registry gifts instead of holiday gifts. That insight can have an immediate impact, as adjustments to the manufacturer’s product line increase its appeal and drive higher sales.
Exhibit 1 How a question in a conjoint survey might look
Which jet would you prefer to own?
Brand A Price
Price, typically equipped Typical seating Max speed Max fuel range $12 million 8 cabin seats 450 knots 1,800 nautical miles 1,200 lbs with max fuel
$15 million 10 cabin seats 450 knots Intercontinental 1,200 lbs with max fuel
$15 million 10 cabin seats 525 knots Coast to coast 1,600 lbs with max fuel
$10 million 10 cabin seats 450 knots 1,800 nautical miles 1,600 lbs with max fuel
Payload with full fuel
Cabin size Flat-ﬂoor Flat-ﬂoor 12+ months Dropped-aisle 12+ months Flat-ﬂoor 12 months
None of these aircraft ﬁts my needs
Delivery lead time Proximity to company owned or authorized service center Availability of ﬁxed cost for all parts program
1 hour or less
More than 1 hour
1 hour or less
More than 1 hour
Which one would you choose?
A more strategic way to use conjoint analysis
Recently, some forward-thinking companies have started using conjoint in a broader way than in the past — applying its insights to inform their brand positions or embark on entirely new product strategies. The methodology they are using looks very similar to the one that traditional practitioners of conjoint have used — a set of trade-offs, repeated in different combinations, leading to a ranking of perceived customer benefits. However, these more creative users of conjoint are organizing the results into groups with similar preferences. For instance, there may be three customer groups — one that is clearly focused on quality and doesn’t care much about price; a second that is very cost-sensitive; and a third that cares as much about the experience of buying and owning the product as it does about the product itself. In other words, for companies that layer in this type of analysis, conjoint becomes a new source of insight into their customer segments. Of course, it would be hard to find a company that hasn’t done some kind of customer segmentation; using conjoint is certainly not the only way to do that. Companies usually have a sense of what their real and prospective customer segments are, and they usually have an idea of what each segment considers important. But purely attitudinal or need-state segmentations often produce schemes that lack economic consequence — i.e., there is no compelling reason to prioritize segments or serve them differentially. By using exercises that simulate real consumer choice behavior and forcing trade-offs, segmentations driven by conjoint usually avoid that conundrum. The importance scores, or “utilities,” that are at the heart of a comprehensive conjoint analysis provide a clear picture of what a given customer segment values. It is a form of actionable intelligence that gives executives the confidence to make changes ( for other prerequisites for using conjoint in this newer way, see “Four Essentials for Using Conjoint More Strategically,” next page). A few examples will help show how the modest divining rod that is conjoint analysis can become something more: a tool that can fundamentally change companies’ perceptions about where opportunity lies and how to go after it.
Four essentials for using conjoint more strategically
If conjoint analysis is to help a company rethink its product strategy or identify new opportunities for organic growth, four things must be in place. 1. A business case for change. Without this, the intelligence from the survey won’t have a chance of influencing the fundamental thinking of the organization. 2. A culture of — or at least an aptitude for — creative thinking. In the cases where conjoint has informed a big shift as opposed to an optimization, somebody has made an imaginative leap from the data itself to what the data might mean for a detailed product or service offering. This must be encouraged. 3. A willingness to add or deepen capabilities. Insights are one thing; executing on them is another. If the intelligence from a conjoint analysis pushes a company toward a new product offering, the company may have to add capabilities consistent with the new offering. 4. The willingness to monitor new initiatives. Even a well-informed product or service change can be imperfect the first time out. Companies must be disciplined about gathering feedback and willing to make midcourse corrections.
Case example 1: The maker of luxury goods
In recent years, a luxury goods manufacturer 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 have the necessary insights to transform its organic growth strategy. Using traditional conjoint techniques, the retailer surveyed 2,000 luxury gift–buying consumers to find out the extent to which they were price- and brand-conscious; valued precious materials such as gold, silver, and fine crystal; and wanted their gifts to elicit “oohs and ahs” from friends and family. The manufacturer combined the data from the conjoint analysis with the results of other survey questions to define five customer segments, and decided that it had “headroom” — an opportunity to pick up significant market share — in several of those segments, including a group it called “premium shoppers.” Thanks to the conjoint survey, the retailer knew that in the premium shopper segment, customers ascribed great importance to prestige, cared a lot about high-quality materials, and preferred designs that made bold statements. Meanwhile, the least important attribute among this customer segment was price — these customers didn’t mind paying a premium to get what they wanted. The company may have already had an intuitive sense of these things — after all, managers were constantly thinking about their customers and ways to hone their strategy. However, the intelligence from the conjoint analysis was more than suggestive: It was definitive, the sort of thing that gives executives the courage of their convictions. The results of that analysis seem certain to play a role in changing the company’s product line, in what happens within the company’s distribution channels, and in how and where the company spends its marketing dollars.
Case example 2: The bank
The value of conjoint analysis beyond product-feature decisions is also evident in the recent example of a European bank. The bank was picking up signals that regulators were going to demand more transparency 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 loan holders lost their jobs 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 continue making money in the business of unsecured loans and loan protection? The bank used a conjoint survey of 1,600 people with unsecured loans to estimate price elasticity for the loans themselves and for loanprotection 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 — bargain hunters, conservatives, spenders, risk avoiders, and a group it called “personal bankers” — it was also able to answer this question in a more granular way. For instance, the bargain hunter segment had the highest level of price elasticity — this group would not pay more to take out a loan or insure it. By contrast, the personal banker segment (which liked the high-touch approach and was willing to hear advice and pitches about special offers) was not particularly price-sensitive. Even in the event of a regulatory change, the bank could profitably sell higher-priced unsecured loans and loan protection to this segment. Indeed, one of the intriguing things about this bank’s use of conjoint analysis is how broadly useful the results ended up being. Although the analysis started out as a way to test price elasticity and prepare for an external change in the environment, the information it generated —
not only about how customers would respond in the event of a rate hike but also about more basic things like how people make borrowing decisions and how they think about financial providers — allowed the bank to identify a tailored product strategy that would appeal to all of its customer segments. The company decided that its existing product worked for the spender and risk avoider segments, but that it needed a no-frills product for the bargain hunter and conservative segments and a premium product for the personal banker segment.
Conjoint analysis allowed one bank to identify a tailored product strategy that would appeal to all of its customer segments.
Case example 3: The newspaper
And then there is the newspaper example. The redesign of the Wall Street Journal was at least partially about fine-tuning a well-established editorial product to make it even more compelling. But the Journal’s redesign was bigger than that: It was the cornerstone of a major strategic undertaking intended to revive the Journal’s fortunes at a time when the whole newspaper industry was ailing. At the time of the redesign, the Journal had just endured five years of flat circulation and advertising revenue. It wanted to attract a new base of readers but wasn’t sure whom to target or what changes in its editorial product would appeal to them. The newspaper did not want any changes that would alienate its core base of readers — that was one constraint. The second was that any changes should align with the company’s financial commitments to shareholders. Like the luxury goods company, Dow Jones was searching for its headroom — in this case, readers who might switch from other newspapers or information sources to the Journal. The company was especially interested in younger readers, in order to start building a next generation of customers and serve up a demographic important to advertisers. Using a variation of conjoint analysis, the newspaper identified four clusters of readers. There were those who read the Journal for “decision support” and those who read it for “reassurance.” In the decision support segment in particular, the Journal realized it already had a high market share and couldn’t do much to increase that. But in a cluster of prospective readers it named “time is money,” the Journal found what it was looking for — a younger audience of business professionals it was not yet reaching. This segment put a premium on attributes like crisp newspaper layout and availability “where and when I need it.” (In recent years, the Journal has shown that it is still pursuing that segment, and catering to the needs of younger readers, by introducing one of the most highly regarded iPad apps around.) And of great importance, in fine-tuning those aspects of its product and enhancing it in other ways — including with more color, more
arts coverage, and a sports page — the Journal determined that it would not disenfranchise its core audience. The strategy worked. In the wake of the redesign, the Wall Street Journal saw a 35 percent improvement in its efforts to add new subscribers through direct marketing — a very positive development at a newspaper that had been losing readers for five years. The Journal (now part of News Corp.) also reversed a three-year slump in ad sales and experienced a revenue improvement of $25 million from new programs and pricing initiatives.
Few companies nowadays can pursue an organic growth strategy based solely on tweaking their products or services. While small product changes are important to every business, and are part of the agenda of every R&D and product development organization, they don’t provide the fundamental lift that most companies need in an era of low consumer confidence and cautious spending. Most companies are looking for something bigger: a refurbished product lineup or strategy, at least on some level, that attracts new customers while reinvigorating old ones. In short, they are looking to discover the sources of their headroom. Interestingly, a type of analysis that has historically informed relatively narrow product decisions — enhance this feature, delete that one, and by the way we can raise the price by 2.5 percent if we combine these three things in a bundle — is turning out to have bigger strategic implications. Because it allows companies to segment their customers and identify the needs of those customer segments, conjoint analysis is giving a few creative companies actionable insights into new market opportunities. In some cases, the insights are ones the companies would not have come up with on their own. In other cases, the output of a conjoint analysis, used in this new way, is giving companies the data they need to confirm a hunch they already had. Either way, conjoint is becoming a core tool of strategy development, far more central than it used to be to executive decision making.
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This report was originally published by Booz & Company in 2010.
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