The method
Use this prompt in ChatGPT to refine your e-commerce product recommendation engine. Input data on user behavior (purchase history, browsing data, demographics) and product attributes (category, price, features). Analyze the output to identify areas for personalization improvements.
The prompts
Prompt 1
Analyze the following customer data and product catalog to generate 5 actionable strategies to improve our product recommendation engine. Focus on increasing conversion rates and average order value. Customer Data (Provide anonymized examples of purchase history, browsing behavior, demographics). Product Catalog (Provide a representative sample of your product catalog with key attributes like category, price, features, and average rating). Consider collaborative filtering, content-based filtering, and hybrid approaches. Prioritize strategies that can be implemented with our current tech stack: [Specify your tech stack].
Prompt 2
Given the following user segment (describe a specific user segment, e.g., 'first-time buyers aged 25-34 interested in sustainable products'), and the recent sales data (provide anonymized data showcasing product popularity and trends within that segment), suggest three novel product recommendation strategies that leverage psychological triggers like scarcity, social proof, or authority to enhance their likelihood of purchase. Explain the rationale behind each strategy and provide specific examples of how they would be implemented on our website. Further consider our current inventory and margin to make the recommendations most beneficial.