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Two easy methods to design surveys for complex problems
Conjoint analysis & Max-diff
The typical bootstrapped startup or product team goes about surveying users by… identifying a problem > qualifying users (who to interview) > designing categorical, ranking/rating/scaling and open-ended questions > then representing the data in the form of tables, bar/pie charts and graphs.
However, matters can get complicated when bootstrapped product teams don’t have access to enterprise-grade surveying software and/or when the the problem they’re trying to solve for is more nuanced.
Let’s take a product feature for example — your team has just introduced grocery delivery in your food delivery app but the conversion rate is underwhelming. You have some theories as to why they’re not converting, but no concrete way of proving user preferences — so, you end up throwing user incentives, conducting surveys that require more follow-ups, and tweaking or adding more features in your app.
Enter conjoint analysis…
1. Conjoint Analysis
Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced. Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than concept testing.
In the article What is Conjoint Analysis by Chad Brooks on Business News Daily, a market research firm states that conjoint analysis is based on the principle that the relative values of a product or features are measured more accurately when considered holistically rather than when considered in isolation.¹
Going back to our example, let’s say we know quite a few variables that might influence a user to order grocery delivery. They are as follows:
- Price
- Quality
- Options
- Convenience
In conjoint analysis for market research, we assign utility scores to these variables and then add them up to identify what combination has the maximum utility for our users.
