We Didn’t Have a Product Problem. We Had a Memory Problem.

A product development team discovered that their lack of a clear memory of past decisions was hindering their ability to effectively address customer feedback and product issues. The team realized that while they had a wealth of knowledge from various sources such as customer interviews, support tickets, roadmap meetings, engineering releases and retrospectives, this information was not connected and accessible.

One example of this issue was the repeated discussion about a rejected feature. The team couldn't quickly reconstruct the context of why the decision was made, and whether the reasoning remained relevant. This is a common problem that many teams face, where they revisit the same issues over and over again because the necessary information is scattered and not connected.

The solution the team developed was PRATHIDHWANI, which stands for Customer Feedback Intelligence. This system aims to address the memory problem by connecting customer feedback, product changes, and historical context. Instead of simply analyzing today's customer feedback, PRATHIDHWANI seeks to understand what changes occurred after previous feedback was addressed.

The core idea behind PRATHIDHWANI is to connect customer feedback to the actual actions taken by the product team. For instance, if hundreds of customers report problems with checkout, the system helps the team understand that this feedback led to a product change, and whether the release of the fix led to a decrease in complaints. This historical context allows the team to see if the original problem was truly resolved or if new issues have emerged.

PRATHIDHWANI's dashboard provides a visual overview of feedback trends and historical signals, showing positive and negative sentiment changes over time. It also highlights emerging issues that require attention. The most interesting insights come from observing how individual issues move over time, allowing the team to determine if their interventions were effective.

One important feature of PRATHIDHWANI is its ability to compare data with and without historical memory. Without this memory, an AI might only provide a generic present-state observation, such as "Export performance is good." However, with historical memory, the system can provide a more comprehensive answer, including the journey, the seriousness of the original issue, the changes made, and the impact on customers after the release.

In summary, PRATHIDHWANI tackles the memory problem that many product teams face by connecting customer feedback, product changes, and historical context. This connection allows teams to understand not just what is happening with their product, but why things are happening, leading to more informed decision-making and improved customer experiences.