Most GTM strategies die in a boardroom because people are too busy guessing what customers want. The market is already telling you how to sell to them; you just have to listen. When you start feeding the market's own language back into the engine, the sales cycle starts being a conversation.
At scale, a brand's biggest blind spot is its own customers. When a product has thousands of Amazon reviews, reading them manually is impossible, so companies rely on generic star ratings. But a 3-star average doesn't tell you why people are churning or how to rewrite your landing page. I built a sentiment engine to find the signal in the noise, turning 5,000 messy Amazon reviews into a roadmap that converts.
Product feedback isn't a satisfaction score; it's competitive intelligence. The core problem is rarely the product itself, it's the gap between what marketing promised and what the customer actually experienced. The happiest customers are explicitly writing your best ad copy for you. The angriest customers are handing you your product roadmap. You don't need to invent new marketing angles.
A repeatable loop: parse the reviews, harvest the language, ship the playbook.
Implemented an ML model to parse thousands of reviews. Instead of binary positive/negative buckets, I clustered them into GTM themes: UX friction, pricing misalignment, unmet expectations.
Used the model to isolate the exact, unpolished phrases repeat buyers used to describe value — a swipe file of customer-validated language that's 10x more effective than anything written in a vacuum.
Translated outputs into a strategic playbook, separating findings into messaging fixes (marketing) and roadmap fixes (product).
Three outcomes from the engine, plus the stack that ran underneath:
Eliminated the brainstorming bottleneck. Transformed thousands of unstructured reviews into a prioritized list of product vulnerabilities. We stopped wondering what was wrong and started fixing it.
Precision positioning. By harvesting real customer language, I enabled copy that resonates with the target ICP because it uses the words they already use.
Designed not as a one-off report but as a framework that can be deployed across any product category to monitor brand health and competitive shifts in real-time.
Python (Pandas) and Claude 3.5 Sonnet for data cleaning; LangChain for flow architecture and the OpenAI API for sentiment batching; Looker Studio and Streamlit for internal dashboards; Canva Magic Studio and Figma for the playbook layer.