Make it Real with Adaptive Conjoint
In a recent survey we fielded among B2B decision-makers, respondents told us how much they liked participating in the study compared to other research studies they have done. They said it was “real” and interesting because it was confronting them with questions that reflect the kinds of decisions and trade-offs they make every day in their work.
The technique we used for that study is called Adaptive Conjoint. If you want to know how people in your target audience make decisions—how they weigh the pros and cons of your product or service versus others— adaptive conjoint can be a powerful technique that provides robust and insightful data at the same time it really engages the participants.
All conjoint techniques work by presenting people with scenarios that are more like the real-life trade-offs they always make. For example, instead of just having them rate how important price is, they are asked to make decisions based on a series of scenarios, one piece of which is price. Based on their answers, the importance of price in their decision-making can be mathematically derived.
The twist with adaptive conjoint versus other types of conjoint is that each scenario learns from the previous answers, so that the decisions confronting respondents get more real and more difficult with each answer they give. It requires online or computer-based administration (versus paper-based or telephone surveys) because an adaptive algorithm drives the learning based on previous answers. If it sees that price is not influencing decisions, for example, then the survey stops including price in the scenarios so that the respondent can focus on things that do matter.
Many respondents say they love these surveys because the tasks mimic real-life decisions. Moreover, as respondents start to realize that the survey is learning and changing based on what matters to them, they become more and more engaged. And even respondents who do not love the process appreciate how effectively it drill downs to the criteria that really matter to them:
“Interesting to take this survey because it helped me realize what my priorities are as a shopper.”
“My head was hurting near the end, but I expect this approach resulted in more accurate info from me (checks and balances).”
One of the cool things about conjoint analysis is that it builds elegant mathematical models of human decision making, and it works. The super cool thing about adaptive conjoint is that it interacts with the respondents and brings them into the modeling process itself.
If you would like to see the process at work, or think adaptive conjoint might be right for your research, we would be happy to help you think about an optimal approach.
—Joe Hopper, Ph.D.
