When To Use A Conjoint Analysis

Preference Importance

A key learning you’ll get from running a conjoint analysis is something called preference importance. Preference importance lets you see how important individual features are to customers’ purchase decisions. A high importance means that feature has a very strong influence on purchase decisions. In contrast, a low importance means it has very weak influence.

In the case of our pretend financial services company, we see that Fee holds a 45% importance preference. This high importance value tells us that customers really care about the product’s fee when deciding whether or not to buy. Meanwhile, Credit Score holds a 5% importance. This tells us that it has some, but very little, impact on our customer purchase decision.

In essence, preference importance tells us that the best way to improve purchase consideration is to look at the product’s Fees. Our mocked up data also shows us that we can improve consideration with changes to Payment Period and, to a lesser extent, Rewards. But, it tells us that changing Credit Score won’t have a material impact.

Preference Share

Another learning you’ll get from a conjoint analysis is the preference share within a given feature. Preference share shows how much a given level is preferred over other levels. A high share means that a particular level is very strongly preferred. In contrast, a low share means it not at all preferred.

Let’s look at this again in the context of our financial services company. This time, we’re going to home in on the Fees feature. Specifically, we’ll see how much customers prefer paying one fee amount versus others. In this example, customers have a very strong preference for low fees. This is why a fee of .5% has a 65% share while a fee of 1.5% has a 5% share.

This outcome isn’t very surprising. Customers always want to pay less. However, when performed for other features (e.g. payment periods), we may see that customers are more balanced in their preferences.

Relative Utility

Lastly, running a conjoint analysis lets us understand the relative utility of levels within each feature. Said more simply, it lets us identify how sensitive customers are to changes in feature levels. And, it shows when changing certain levels really alienates customers.

Once again, let’s look at this for our financial services company. In this example, a .5% fee has a higher relative utility than a 1% fee. However, both of these options have a positive utility. That is, going from .5% to 1% still results in positive customer interest, albeit the fee hike does decrease interest a bit. However, when the fee goes to 1.5%, the story changes. There is now a negative utility. This means we are alienating customers.

Relative utility lets us see how elastic or inelastic customers are to feature changes. In the case of Fees, we’re seeing a lot of elasticity. This means that customer demand changes a lot when fees increase or decrease. Meanwhile, customers are less elastic when it comes to Payment Day. We can infer this because the graph shows far narrower shifts in relative utility as payment days change.

This represents a valuable insight for businesses looking for ways to improve a product’s economics while increasing customer demand. It tells them which features they can change the most and still keep customer interest high.

Build A Digital Survey To Capture Input

Using a digital survey tool, we program these different features and feature levels to capture the data needed for subsequent analysis.

When taking the survey, respondents see unique product bundles that mix and match different feature levels. For instance, one product may show a 1% fee and a 30-day payment period. Another may show a 3% fee and a 5-day payment period. Respondents select their preferred options.

The bundle options then refresh so that respondents see a new set of bundle options. Once again, they select their favorite bundle. This process repeats multiple times across all respondents.