How would you work with engineering to reduce latency in Advertising

Problem Statement Description

Product context: Amazon is a commerce, logistics, media, devices, and cloud company; its products include Marketplace, Prime, Prime Video, Alexa devices, ads, fulfillment, and AWS.

Amazon Advertising serves sponsored placements across shopping surfaces where grocery shoppers often have high purchase intent, short session times, and strong expectations for relevance and speed. In this scenario, you are asked to describe how you would partner with engineering to reduce ad latency without degrading ad quality, customer trust, advertiser outcomes, or marketplace revenue.

Focus on the end-to-end ad-serving workflow: request initiation from a grocery shopping surface, eligibility and targeting, auction or ranking, creative retrieval, rendering, measurement, and downstream reporting. The problem is not only about making systems faster; it is about defining the right latency goal, understanding where time is spent, aligning teams, and making product trade-offs across relevance, monetization, reliability, and privacy.

Your response should show how a Technical PM would frame the problem, collaborate with engineering, use data to prioritize, and manage rollout risk in a high-scale commerce advertising environment.

The experience should consider:

- The user and business impact of ad latency for grocery shoppers, advertisers, and Amazon’s retail experience.

- Clear latency definitions, such as client-side render time, server response time, p95/p99 latency, timeout rates, and surface-specific SLAs.

- Instrumentation needed across client, edge, ad decisioning, targeting, auction, creative, and measurement systems.

- Data and API dependencies, including catalog, inventory, shopper context, campaign eligibility, bidding, ranking, and attribution pipelines.

- Product and technical trade-offs between speed, relevance, personalization, auction depth, revenue, privacy, and system cost.

- Reliability and resilience requirements, including fallbacks, caching, degradation behavior, and handling traffic spikes during grocery shopping peaks.

- Privacy, security, and compliance considerations around shopper data, targeting signals, and advertiser reporting.

- Rollout and observability plans, including experimentation, guardrail metrics, alerting, incident response, and post-launch validation.

The goal is to present a structured approach for working with engineering to diagnose latency, prioritize the highest-impact improvements, and deliver a faster advertising experience that preserves customer trust, advertiser value, and operational excellence.