How would you launch a new product recommendation carousel for Amazon?

To launch a new product recommendation carousel for Amazon, I would focus on a phased approach, ensuring alignment with executive leadership and data-driven decision-making.

Executive Leadership Alignment:

1. Define Clear Objectives: Articulate the primary goal (e.g., increase sales, improve customer engagement, drive discovery) and quantify success metrics. Highlight how the carousel directly supports Amazon's broader strategic objectives.
2. Present a Compelling Vision: Showcase the user experience benefits, potential revenue impact, and competitive advantages. Use mockups or prototypes to illustrate the carousel's functionality and aesthetic.
3. Outline a Phased Rollout Plan: Detail the stages of the launch, including pilot testing, gradual expansion, and full release. This demonstrates a controlled and risk-mitigated strategy.
4. Address Potential Risks and Mitigation: Proactively identify challenges (e.g., cannibalization, performance impact) and present clear mitigation plans.
5. Secure Resources and Buy-in: Clearly state the resources required (engineering, marketing, operational) and gain executive approval for the launch.

Data Acquisition and A/B Testing:

1. Data Sources: Leverage a combination of existing Amazon data, including:

   • Purchase History: User's past purchases and browsing behavior.
   • Clickstream Data: Pages visited, products viewed, time spent on pages.
   • Search Queries: Terms users are searching for.
   • Product Metadata: Attributes, categories, and relationships between products.
   • Customer Reviews and Ratings: Sentiment and product quality indicators.
   • Third-Party Data (if applicable and permissible): Broader market trends or demographic information.

2. A/B Test Sample Size: The sample size for an A/B test is determined by statistical power, desired significance level, and the expected effect size of the new carousel. I would use a sample size calculator, considering:

   • Baseline Conversion Rate: The current conversion rate for product recommendations (or a relevant proxy).
   • Minimum Detectable Effect (MDE): The smallest improvement in conversion rate we aim to detect (e.g., a 1% increase).
   • Statistical Significance Level (alpha): Typically set at 0.05 (5% chance of a false positive).
   • Statistical Power (beta): Typically set at 0.80 or 0.90 (80-90% chance of detecting a true effect).
   • Traffic Volume: The daily or weekly traffic to the relevant pages where the carousel will be displayed.

   For Amazon's scale, even a small percentage improvement can be statistically significant with a relatively small percentage of overall traffic, but the absolute number of users in each test group would still be substantial to ensure robust results. The goal is to have enough users in each variant (control and treatment) to confidently determine if the new carousel performs significantly better than the existing recommendation view.