Choice Modeling: How to Choose the Right Approach for Your Data
Make the most out of your data with the right choice modeling tool
STEVE COHEN – At in4mation insights, choice modeling is one of our favorite ways to uncover actionable insights about how customers really feel about their experience with our client’s products and services, and what kinds of features are most important to customers when making a decision.
But what is choice modeling? How do you choose the right analytic approach when modeling choice? I thought I’d break down the basics of choice modeling to give you a better idea of how it works and why it’s so effective.
What is Choice Modeling?
Choice modeling is a statistical process used to estimate the value that customers place on various features or attributes of products/services—like price, quality, or even color. It allows brands to measure the impact of these attributes on consumer behavior and make data-driven decisions about product design, branding, marketing campaigns, and more.
How to choose the right analytic approach when modeling choice:
There are four major approaches to modeling choice outlined in the diagram below. To determine the right analytic approach for the decision process at hand, we must first answer two major questions:
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- How many separate, distinct items will be chosen? That could be just one as in standard Choice-Based Conjoint Analysis (CBCA), or it might be several distinct items or flavors that would be chosen on a trip to the supermarket to buy several containers of different flavors of yogurt.
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- How many units of each item are chosen? For example, in the case of purchasing from a fast-food menu, there are typically three to four items chosen but only one unit of each.
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To learn more about the various choice modeling approaches and how they can obtain better results for decision-makers, please contact me today – Schedule a call or Send an email.
About Steve Cohen 
Steve Cohen is an award-winning entrepreneur, specializing in design of research, analysis of marketing data, and using marketing science tools to solve business problems. In 1984, Steve was the first commercial researcher to do choice-based conjoint and subsequently, he authored the first conference presentation and paper on MaxDiff. He is a co-author of the first academic paper on Latent Class Choice-based Conjoint and Menu-based Conjoint. Steve was won numerous awards for his research including: AMA “Parlin” Award, INFORMS Society for Marketing Science “Buck Weaver” Award, NextGen Marketing Research Disruptive Innovator Award, NY Marketing Research Council’s Hall of Fame, ESOMAR Goodyear Award for the Best International Research Paper, AMA Hardin Award for the Best Paper in Marketing Research Magazine, and Best Presentation at Sawtooth Software Conference. Read More
