Reading 500 reviews to figure out why a product gets 3.8 stars is nobody's idea of a good evening. Ooty pulls review data, breaks it down by sentiment, and extracts the patterns: what people love, what they complain about, and what they wish the product did differently. Five minutes instead of five hours.
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Sana Malik
Brand Owner, TerraCraft (sustainable homeware, launching new product line)
“I am about to launch a bamboo utensil set and I need to understand what customers hate about the existing options. I have been reading reviews for three evenings straight. There must be a faster way to do this.”
This is what it looks like in ChatGPT
Replace the {placeholders} with your details, then paste into ChatGPT, Gemini, or Claude.
Analyse the reviews for ASIN and tell me the top complaints.
Pulls review data, runs sentiment analysis, and extracts recurring negative themes.
Compare the review sentiment for these competing products: ASIN-1, ASIN-2, ASIN-3.
Side-by-side sentiment comparison across multiple ASINs.
What features do customers wish ASIN had, based on the reviews?
Extracts "wish list" patterns from review text to identify product improvement opportunities.
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Customer reviews contain more product intelligence than most market research reports. The aggregate star rating tells you whether a product is generally liked or disliked. The individual reviews tell you specifically what works, what fails, and what customers wish the product did. This information is gold for product development, listing optimisation, and competitive positioning.
Sentiment analysis at scale reveals patterns that reading individual reviews cannot. A product with 4.2 stars might have 85% positive reviews about quality and design but a concentrated cluster of negative reviews about packaging damage during shipping. That specific insight tells the seller exactly what to fix. It also tells a competing seller that they can win market share by solving a known problem that the incumbent has not addressed.
The most actionable review analysis categorises feedback into themes: quality and durability, design and aesthetics, packaging and shipping, value for money, and use-case fit. Within each theme, you want to know the frequency (how often is this mentioned), the sentiment (positive or negative), and the trend (are complaints increasing or decreasing over time). If durability complaints have doubled in the past 3 months, the manufacturer may have changed suppliers or materials. For sellers launching a competing product, the review data from existing listings is a free product specification document. Build the product that fixes the top 3 complaints in your category, mention those fixes in your listing bullet points, and you have a positioning advantage from day one.
With Ooty, your Commerce data flows straight into ChatGPT, Gemini, or Claude. Structured and filtered. Ask a question, get an answer backed by real numbers.
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31 tools. Replaces Jungle Scout ($49/mo). From $29/mo.
14-day money-back guarantee, no questions asked. Cancel anytime.