Editor's note: Craig Lutz is senior manager, professional services, at Qualtrics, Provo, Utah. Devon McBee is senior product marketing manager at Qualtrics. Ben Rogers is head of content at Qualtrics.

In the early 2000s, two new car models had very divergent fortunes: One was highly successful and the other was a flop. In their May 2016 Harvard Business Review article, “In product development, let your customers define perfection,” Madhavan Ramanujam and Georg Tacke shared the intriguing story of two automotive industry giants, from which we’ve pulled a valuable product research lesson shared below.

After avoiding bankruptcy in the early 1990s, Porsche executives set out to turn around the storied car brand. By the early 2000s, the burgeoning SUV market became a promising target. Porsche set out on a mission to understand the market and its customers’ appetite for a luxury SUV.

Confirming that customers would welcome a Porsche SUV, the product team gathered feedback from consumers on every aspect, finding that they were willing to trade-off on a manual transmission (a staple in all Porsche cars up to that point) in favor of other iconic features that Porsche had become known for, including sportiness, power and handling. 

The authors wrote that, “The customer-listening process continued with every proposed feature. If customers valued and were willing to pay for them, they were in. If not, no amount of convincing from Porsche engineers could overrule the end user.” The Porsche Cayenne soon hit the market and was an instant success. And it later became the most profitable vehicle in the industry. 

Around the same time Porsche was launching the Cayenne SUV, the CEO of Fiat Chrysler declared, “Of all the cars I can get wrong, it ain’t this one.” The car he was referencing was the Dodge Dart, a vehicle for which the company shut down production several years after its launch, amassing a huge financial loss. 

One key takeaway is that Porsche bucked conventional thinking using good product research: It showed that a legendary sports car manufacturer with a loyal fan base could succeed in the SUV market. Yet, what went wrong with Dodge building another sedan? Harvard Business Review shared this valuable lesson: “The underlying cause ... was that Fiat Chrysler did not try hard enough to find out what the American compact car customer wanted, valued and was willing to pay for, before turning the Dart over to engineers and designers to build it.”1

Product managers and market researchers are frequently faced with critical product development and trade-off decisions. Which features should we build into the product? What features should we include today? How much should we charge for the product? These and other product development questions are difficult to answer without data. 

Yet many product managers and researchers don’t utilize proven research methods for product development. In fact, in a recent study we ran with product managers and researchers, we found that few were aware of one of the most important product development analysis techniques: conjoint analysis. In our interviews with over 80 product managers and researchers, only about 25 percent were aware of conjoint analysis. And of those who were aware of conjoint, only 30 percent ran a project last year, yet had proven results. 

To give researchers a competitive advantage while developing products and to help close the awareness gap on conjoint analysis, we want to discuss what conjoint is, how it works and what business questions it can answer. 

Optimizes your chances for success 

Conjoint analysis is a special class of research tool that provides the insights necessary to confidently determine what attributes and features of a product or service are important to your target consumer and what optimizes your chances for success in the market.  

As different package configurations are shown to respondents, they simply select the option that they would be most likely to purchase. This is an exercise that all of us face every day and why the data acquired through conjoint analysis is so actionable. Conjoint analysis will accurately prioritize product development, predict price sensitivity and uncover competitive advantages. Conjoint analysis delivers accurate data because of how questions are structured and presented to respondents. Finally, conjoint analysis will model and predict the choice share that can be won when introducing specific product or service combinations within a current or new market. 

Conjoint analysis is a market research technique for measuring the preference and importance that respondents (customers) place on the various attributes of a product or service. Conjoint can play a critical role in understanding the trade-offs that consumers are willing to make when given multiple product configurations.

Conjoint analysis is typically conducted via online questionnaires where respondents are shown different product bundles and are then instructed to evaluate and select those bundles based on which one they would be most likely to purchase. In analyzing conjoint studies, respondents’ selections shed light on the features and feature combinations that show up more frequently in favorable bundles. The essence of conjoint analysis is predicting and modelling the results of the online questionnaire into what product configuration consumers are most likely to purchase – in other words, it models the real-life trade-off decisions people make every day when purchasing products. 

Up to this point we’ve discussed what conjoint is and how it works but what business questions can you actually answer with conjoint? Conjoint is an excellent tool for product and pricing and it can also be used for employee benefits research, brand awareness and more. Below we share some of the key business research objectives that can be answered using conjoint: 

  • What feature or functionality of a product is most important and influential to the market? What do customers focus on when making purchase decisions? What has the greatest impact on whether they will purchase?
  • What trade-offs can we expect our customer to make? What are they willing to give up in order to get what they want?
  • What role does price play in decision-making and what is the optimal price point? How sensitive will customers be to shifts in pricing? What is the monetary value of the different product attributes?
  • How can we create a benefits package that our employees want and is still cost-effective for the business?
  • What does optimal pricing and packaging look like for a software platform to address different types of buyers and maximize revenue?

Avoid costly mistakes

Conjoint analysis can help companies not only build products that customers want but also avoid costly product mistakes and the fallout when a product fails. Relying solely on instincts and internal teams is a recipe for disaster. By using product research and development techniques such as conjoint, you can speed up your insights to action and have confidence that the product you’ve built is the product your customers want. 

References

¹Reprinted by permission of Harvard Business Review. From “In product development, let your customers define perfection” by Madhavan Ramanujam and Georg Tacke, May 2016. Copyright ©2016 by Harvard Business Publishing; all rights reserved.