How to Use a Conjoint Study to Build Better Products

Imagine you have a new product idea.

And imagine it’s called MomME—an app for moms that helps them organize their kids’ schedules, communicate with their teachers, and streamline household chores.

You’ve done your research, and you’re convinced your concept is better than existing alternatives. You’ve got some funding, hired an app developer, and you’re ready to get going.

But as you run this idea by friends and family, you encounter a problem—everyone has their own idea about what other features the app should have. What about shopping lists? Automatic schedule reminders? Integration with Facebook and other tools?

It’s overwhelming! So many good ideas, but what do consumers really want? Which features should you include, and which should you ignore? Which combination of features is most likely to generate the most demand and ensure a successful product launch?

This is where a conjoint study can help.

What is a Conjoint Study?

A conjoint study is a market research survey that measures consumers’ reactions to different combinations of features.

We’ll explain using our MomMe example from above. As we noted, it started out as your idea to help moms, but it’s since become more complicated as you’ve identified five possible features to include in the app:

  • Calendar integration
  • Automatic reminders
  • Email integration
  • Shopping lists
  • Facebook integration

Of course, one approach would be to simply put all of these in the app. But you’re worried (and rightly so) that this will overwhelm the app’s users, and create more problems than it solves. Plus, each new feature has a cost, and in order to fund all that development work, you’d need to charge users a pretty high fee just to use the app.

You’d rather launch with just the most essential features—the ones most important to your target market (moms) and at a price that doesn’t break their bank.

So to hone in on exactly what combination of features moms want, you run a conjoint study. This entails mixing-and-matching these various possible product features together, and asking about respondents reactions to each of the combinations. Each respondent sees a few sets of possible “products” (combinations of features) side-by-side, and is asked to pick the one they like best. They do this a few times (usually 4 to 5, but it depends on how many features you’re testing and how many people will be taking your survey).

How Do You Run a Conjoint Study?

The short answer is that most survey platforms have a “conjoint” question type. These will ask you to input your different features into one big list, ask you to define how many features should be included in each combination, and ask you to define how many sets should be shown to each respondent.

As you can see, it can be complicated. Even with the guidance of a robust survey platform. So at PeopleFish, we do it for you.

We first decide on the attributes. These are high-level categories like price, color, size, etc. In the case of the MomME example, this would probably be price, integrations, and features.

Then, beneath each attribute category, we include up to six different levels. Say, three possible prices, four possible integrations, and six possible features.

Once these are uploaded, respondents are then shown three products side-by-side, each with different levels inside each attribute. They are asked to choose which one they most prefer. Example picture below:

What’s the Outcome of a Conjoint Study?

Once respondents have gone through a few iterations of this exercise, each time choosing between a different set of three products/combinations, we generate a report that tells us which attributes were most impactful to their choice of preferred product, and then which level inside those attributes was the most popular.

So it’s two-layered analysis.

Not only are we able to see that, for example, consumers prefer a Facebook-integrated MomME to a Google-integrated MomME, but we’re able to see whether the integration itself made a big difference toward which product respondents favored. For example, we might learn that while a Facebook-integration was more popular, price was almost always what drove respondents’ decision—as price lowered, respondents were more likely to choose the cheaper product than they were if the integration switched from Facebook (more popular) to Google (less popular).

This can get complicated, but the value here is unparalleled. When running a conjoint study, it’s important to work with a professional to maximize the value of your report. With a conjoint, you should be able to determine the optimal set of features to include in your new product, without any doubt.

Got more questions about conjoint studies? Click here to get in touch with one of our analysts for a free consult.