Entering the Personalized Shopping Market

Personalized shopping apps all have distinct features, but which do users consider table stakes and which are bonuses? Our client came to us to help determine which of their 11 value propositions were more popular among financial app users and how interests differ across varying user groups.

In a 12-minute online quantitative survey, we utilized a MaxDiff with accompanying TURF (Total Unduplicated Reach Frequency) to evaluate reactions to 11 branded and unbranded value propositions. By using this method, we were able to rank and scale interest scores for all value props, revealing detailed insights into the reasons why people gravitate toward specific propositions.

Our analysis uncovered the most effective value props for our client to showcase in messaging, and feature integration, driving engagement of their personalized shopping platform for current and prospective users.