A new feature increased Amazon Search by 10% & added 2s to load time. What do you do?
Amazon Product Strategy & Analytical Thinking Question: You are a Product Manager at Amazon, a new feature which you launched led to an increased in Search by 10% but added 2s to the load time.
1. Clarify the Problem (Ask Clarifying Questions)
To begin with, I’d want to fully understand the scope and context of the change. I’d ask a series of clarifying questions:
a. What does "10% increase in Amazon Search" mean?
Does it mean 10% more users using search, or 10% more searches per user?
Is this uplift in search sessions, search queries, or engagement with search results?
Is it a leading indicator (e.g., usage) or a lagging one (e.g., conversion)?
💡 Assumption: For the sake of analysis, I’ll assume it means a 10% increase in the number of searches per user session, as this is a common proxy for engagement.
b. What exactly is the “2-second increase” in load time?
Is this the Time to First Byte (TTFB), Search Results Page (SRP) load time, or end-to-end latency?
Is this delay only affecting search, or other parts of the site too?
What is the baseline load time — e.g., is this 2s on top of 2s (100% increase) or 2s on top of 5s (~40%)?
💡 Assumption: The 2-second increase refers to Search Results Page (SRP) load time, and baseline was ~2.5s — so this is now taking ~4.5s (an 80% increase).
c. Platform and Geography:
Is this happening across web + mobile, or just on one platform?
Is this limited to a region (e.g., US only) or global?
💡 Assumption: The feature was rolled out globally on both web and app platforms.
d. User impact metrics:
What has happened to key downstream metrics — e.g., CTR on results, Add to Cart, conversion rate, bounce rate?
Has there been an increase in exit rate from search due to slower load?
💡 Assumption: Early data suggests CTR has gone up, conversions stayed flat, but bounce rate on search increased slightly — indicating friction due to load time.
2. Understand the Goal of the Feature
Now I’d want to dig into what the new feature was trying to solve or improve.
a. What was the intent of this new feature?
Was it aiming to make search smarter, more personalized, or more visually rich?
Was it improving relevance, or adding new filters or content types?
💡 Assumption: The feature added AI-powered dynamic filters and semantic suggestions, aiming to improve relevance and help users discover more relevant products, especially in long-tail searches.
b. Target metric:
Was the primary goal to increase search engagement, conversion, or customer satisfaction (e.g., CSAT/NPS)?
Was this feature rolled out because of a specific business problem?
💡 Assumption: The goal was to improve relevance and product discovery, especially in categories with high catalog depth like fashion and electronics.
c. Success criteria defined beforehand?
Was the team aligned on what trade-offs were acceptable (e.g., some latency for better discovery)?
Was this tested in a controlled A/B test before full rollout?
💡 Assumption: This was tested in a controlled experiment, but performance degradation was not fully captured at scale due to limited geographic test scope.
3. Map the User Journey & Funnel
Understanding where the added friction occurs and where the benefits show up helps frame a better decision.