A large private insurer wanted to test a novel and innovative concept. They planned to launch a new investment app based on machine learning algorithm. The objective was to understand the relevance of the concept in their target audience and also discover the significant drivers which would help them drive the concept to success
Given the focus was to determine the relevance of a novel concept, we chose a monadic design approach. In this approach, we split the target audience into multiple groups and then presented the concept to each group. Each group was asked specific questions on the concept attributes to test the concept overall and features they like or dislike, independent of any external stimuli. The participants were not informed if they were in control groups or the name of the brand to ensure unbiased responses.
To ensure unbiased responses and get a comprehensive response the concept tests were conducted blind using computer aided in person interviews (CAPI). This also helped streamline the data collection process, reducing time and effort compared to paper-based methods.