Menu-Based Conjoint Analysis
Menu-Based conjoint analysis allows researchers to correctly predict how customers’ buying behavior changes when they’re presented with a menu of products and services.
STEVE COHEN – Choice modeling is a type of conjoint analysis that allows researchers to determine which product features are most important to consumers. Over time, it has developed into a powerful tool for analyzing consumer demand for any product and its combinations, substitutes, or complements.
This article looks at the development of Menu Models in conjoint analysis.
Menu Models
Choice from a menu involves picking one unit of more than one product, brand, or item from a list of all displayed items. Go to a fast-food restaurant and buy a burger, fries, and a beverage from the menu. The example below shows the choice from a menu of services that could be added to a phone plan. This “menu” is taken from a study that I conducted on behalf of a telecom company during the early 2000s. As services and features were chosen, the survey participant displayed the total.
Any solution to estimating consumers’ interests from a menu should, at the least, deliver these results:
- How many people choose each item?
- How price-sensitive are people to each item?
- Which items are substitutes?
- Which are complements?
Old Solutions for Menu Models
In 2000, I co-authored an academic paper in the Journal of Marketing Research (JMR) that first outlined a solution to estimating the menu model. Our approach handled item choice and price sensitivity in a straightforward manner, using an advanced choice (probit) model. Substitutes and complements were estimated through the correlations of the items with one another. The paper was one of four that were nominated for Best Paper that year in JMR. Unfortunately, we did not win the award, but the paper generated lots of interest and comments.
A few years later, Sawtooth Software introduced Menu-based Conjoint Analysis (MBCA) as a solution to the menu problem, building upon their existing discrete choice technology (their early technical paper referenced the JMR work). However, their solution to complements and substitutes requires the additional estimation of effects across individual item equations. If all of those cross-effects were important in a menu of 30 items, the model would need 900 additional terms. Since it’s unlikely that all 900 are needed, the analyst must search for the best of those, which could be very time-consuming.
The New Solution
The new method demonstrates that neither my solution nor Sawtooth’s can capture the true depth of complementarity and substitution. This is mainly because both solutions are really estimating the demand for one item on the menu at a time, whereas the new method is estimating the demand for all items, combinations, substitutes, and complements simultaneously.
How good is the new approach when compared to the other two existing methods?
| Approach in JMR Paper | Sawtooth Software | New Menu Model | |
|---|---|---|---|
| Overall Hit Rate | 56% | 53% | 58% |
| Predict Choice of Single Items | 52% | 54% | 51% |
| Predict Choice of Combinations of Items | 36% | 35% | 47% |
If we compare the Overall Hit Rate and Single Item Choice, the performance is relatively the same. Compared to the two older approaches, the new approach shines when looking at correct predictions of combinations: 47% correct versus 35%-36%, an increase in predictive power by 12 percentage points, or an overall 32% increase.
Applying the New Menu Model to Your Business
The use of conjoint analysis over the years has been proven to be a valuable tool for estimating consumers’ preferences from a menu of choices. It can be used to help you gain a better understanding of product demand, combinations, substitutes, and complements. The new menu model can help you revolutionize your research and allow you to understand your customers like never before. If this sounds like an area that could help your business, please contact me today – Schedule a call or Send an email.
About Steve Cohen 
Steve Cohen is an award-winning entrepreneur, specializing in the 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
