Senior Product Manager · Education Marketplace
The challenge
Filter usage was declining despite users wanting personalised results
What I did
Led design and delivery of a conversational search chatbot
Result
80% of users completed a personalised filtered search within six months
"80% of chatbot users completed a personalised, filtered search — reversing a platform-wide decline in search personalisation."
Problem
A counter-intuitive problem: users said in research they wanted personalised results, and the product had a sophisticated filter system to deliver them — but filter usage was falling. The interface wasn't the issue. The behaviour was. Filters require users to articulate their own preferences in the product's language — a significant cognitive ask for someone uncertain about what they want.
Approach
I led the project end-to-end, working closely with the user research team and engineering to take it from concept to launch across both the website and mobile app.
The insight was simple: filters require users to already know what they want and express it in the product's language. A chatbot inverts that — it asks the right questions in plain language and does the translation automatically. Getting the logic right was the easy part.
The harder challenge was tone. After launch we collected feedback directly in the product and ran dedicated user feedback sessions with the research team. Users were clear — the chatbot felt robotic, cold, and off-putting. We iterated fast: the language changed, the tone changed, and even the product name changed based on what users told us. The principle I took from it — that copy and personality are as product-critical as functionality — is one I've applied to every conversational and AI-facing product I've worked on since.
Copy and personality are as product-critical as functionality. Users who don't trust the language won't trust the recommendations.