After Cleartrip launched Price Trends on flight search, search-to-booking conversion fell 6%. Why?

Introduction

In early 2026 Cleartrip promoted Price Trends, a feature on flight search that shows how fares for a route have moved and helps travellers decide whether to book now or wait. Cleartrip positioned it as a fix for "booking anxiety", the fear of paying more than necessary. In this scenario, after Price Trends rolled out to all users, flight search-to-booking conversion fell 6%.

This is a tricky RCA because the feature may be working as designed. A tool that tells some people to wait will, by definition, reduce bookings in the session where that advice is given. The question is whether Cleartrip is losing bookings, or just recording them later, and whether the advice is good. I'll clarify the metric, rule out external factors, break down the funnel, and test hypotheses.

Step 1

Clarifying Questions (3 minutes)

• How is conversion defined: per session, per search, or per user over a time window?

• Why it matters: A "wait" recommendation moves bookings to a later session. Session conversion falls even if user conversion doesn't.
• Hypothetical answer: The dashboard metric is bookings per search session.
• Impact on approach: We must look at user-level conversion over 7–14 days as well.

• Was Price Trends launched with an A/B test or rolled out to everyone?

• Why it matters: Without a control group, we're comparing before and after, which mixes in seasonality.
• Hypothetical answer: It was tested for two weeks on 10% of traffic, then rolled out to all.
• Impact on approach: We can revisit test data and, if needed, restore a holdout.

• Does Price Trends show a recommendation ("book now" or "prices may drop"), or only a chart?

• Why it matters: An explicit "wait" message changes behaviour much more than a neutral chart.
• Hypothetical answer: It shows a chart and a short line such as "Fares are usually lower 3 weeks before travel."
• Impact on approach: Recommendation accuracy is a key suspect.

• Is the drop uniform across routes and users?

• Why it matters: Uniform drops suggest a measurement or global UX issue; concentrated drops suggest a model issue.
• Hypothetical answer: Not yet checked.
• Impact on approach: Segmentation is the first analysis step.