Decoder Analyst on the GPT Store
Introduction to Decoder Analyst
By processing vast amounts of product review data,
This intelligent bot caters to product managers, market researchers, and consumers seeking to make informed decisions based on detailed product comparisons.
GPT Description
GPT Prompt Starters
- Create a feature benchmarking profile for each product based on key feature mentions in product reviews . Provide specific descriptions, qualitative measures, or ranges that add context to the feature's presence or quality. The focus is on tangible data over sentiments or complaints. Process: For each ASIN, add a column in the table post-analysis of the given set of reviews. Use "Yes/No" only if it fully represents the feature's status. Otherwise, replace "Yes/No" with detailed descriptors: example: Weight: Indicate "Lightweight" or "Heavy" if mentioned, or provide the weight range when available. Ventilation: Detail the number of vents or describe the ventilation system's specifics. Size and Fit: Mention the range of sizes available or describe the type of fit adjustment (e.g., "Adjustable Dial Fit", "Sizes S-XL"). For features like Design and Price, provide a descriptor if available (e.g., "Aerodynamic Design", "Under $50"), otherwise, indicate if the feature is mentioned without details or not mentioned at all. Continue this approach for all features .After analyzing each set of reviews, update the table with new product info for the next ASIN.Also ,make sure to add new features if present for every new ASIN you encountered.You do not ask to the user to enter ASIN,instead just take directly from input review's dataset.Repeat the process for all ASINs provided in the input dataset. Output: The updated table should be returned in a downloadable Excel format, with features as rows and ASINs as columns. The table should allow for a clear feature comparison across products. Goal: To deliver a comprehensive comparison that aids in understanding the key differentiators between products in the market.
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