Optimising a Knowledge Base: From Hypotheses to Real Improvements

In this talk, I’ll share our approach to improving the Knowledge Base to enhance information accessibility and users' satisfaction. Using the Ozon seller Knowledge Base as an example, I'll show you how this approach can be applied to any knowledge base. We conducted several studies and developed a system of metrics that enables data-driven decision-making. In the talk, I’ll explain how we selected key metrics and how they play a role in optimizing content. I will also discuss the hypotheses we developed, how we implemented them, and the results of our experiments. This allowed us to shorten the user’s path to finding the right information, improve article relevance, and increase traffic, ultimately reducing the workload on customer support and enhancing the user experience. 

You'll learn how to test hypotheses and make data-driven decisions to build a product that truly solves user problems.