When data doesn’t reflect reality
How do you make crucial product decisions when your internal research doesn’t truly reflect customer preferences? This was the challenge facing a major technology company. Their traditional discrete choice model (DCM) wasn’t accurately capturing how respondents were reacting to product changes. The data had been showing product preferences weren’t affected by variances in price or feature.
This was a problem. This disconnect between research and reality created significant business risks – teams were making crucial product decisions based on data that failed to capture meaningful differences between options.
We figured out that the core issue was poor engagement. Participants just clicked through questions without properly considering the various options. We built a more modern approach to DCM that increased engagement by improving the respondent experience to deliver accurate data, ultimately informing the product development strategy.
Using AI to get people talking
We had to get respondents more thoughtfully engaged with research without sacrificing scale. This meant fundamentally rethinking how they interact with a DCM.
Leveraging AI, we built a bespoke solution – our Guided DCM – which asks respondents why they made specific choices throughout the survey, creating a more human research experience that acknowledged their thought processes. This approach was both fluid and scaleable, maintaining the experimental rigor of traditional DCM while adding conversational elements that improved engagement to yield richer insights.
Responsive research gets better results
The Guided DCM is better because it treats respondents like humans, not data points. This approach immediately improved engagement. When asked further questions about their choices, people were now more sensitive to both price changes and feature modifications. This modern method performed where traditional research couldn’t – by capturing the nuanced trade-offs that better reflect real-world decision-making.
Verbatims also added contextual understanding and storytelling elements. Satisfaction with the research experience actually increased despite asking respondents to do more work, and the Guided DCM simultaneously helped identify and cut bad responses that were degrading data quality.
Better data drives confident decisions
Our bespoke approach transformed the client’s ability to make confident product decisions. With the right insights, they could accurately value individual product elements and set pricing strategies with certainty. It gave them data they could trust.
The improved methodology also helped identify different user segments based on their decision criteria, enabling more targeted product development and marketing. Some respondents consistently prioritized price, while others focused on specific features or configurations—insights that would have been lost in traditional approaches.
The client has since expanded their use of the Guided DCM across multiple product lines, recognizing its superior ability to inform product development decisions through both improved quantitative measures and rich qualitative context.
