Product teams already have the research they need: customers write it, unpaid, in reviews and in public arguments about the product. The obstacle has always been that reading all of it is somebody's job for a month.
A sample never contains the rare complaint
Manual review analysis has a fixed cost per document and a hard human ceiling, so it gets done the only way it can be: on a sample, once a quarter, by whoever was free. Rare complaints - which are the ones that predict returns and churn - fall below the sampling threshold and are never seen. And because the reading was done by a person forming impressions, what a feature mattered to and how much is a judgement nobody can re-derive later. By the time the write-up lands, the release it was about has shipped.
What the pipeline does
Three stages, and the middle one is where the argument lives.
- Extraction across sources
- Reviews from e-commerce platforms and discussion from social media are pulled in and read at full volume, not sampled. Language models pull out the product features being discussed, the pain points, and what drove satisfaction - the same reading a person does, at a scale a person cannot reach.
- Importance scoring
- Extraction alone produces a very long list of things somebody mentioned, which is not more useful than the reviews were. Scoring algorithms turn frequency and context into a quantitative ranking, so the output is an ordering that can be argued with rather than a pile.
- Expert analysis on top
- A person still reads the result and decides what it means for the roadmap. The pipeline changes what that person is looking at - a ranked, sourced summary of everything rather than an impression of a sample.
What actually changed
Analysis that took months takes days. The interesting part of that is not the speed. It is what becomes possible at that speed: the analysis can be re-run after a release instead of once a planning cycle, which turns customer research from a periodic report into something you can check a decision against.
Sentiment scores answer a question nobody asked
- Score sentiment per review
- Polarity per document is the standard output and it is nearly useless downstream. A product team cannot act on the share of reviews that came out negative; it needs to know which feature, in which shape of complaint, and how rarely — and rarity is exactly the signal a percentage flattens.
- Ask a model to summarise all the reviews
- A summary of ten thousand reviews is a description of the middle of the distribution, and the complaints worth finding live in the tail. Summarisation drops the rare item by design, which is the same failure as reading a sample, arrived at faster.
The shape of a process worth automating
The shape is worth naming. High volume, repetitive reading, a result that is currently produced from a sample because the full population is unaffordable, and a decision downstream that would be better with the full population. When the first days of a build go looking for the task to automate, this is the pattern they match against.
What it proves
An LLM pipeline took customer-research analysis from months down to days. That's exactly the kind of payoff worth building an agent around.
Category
Data Analytics
Built with
Python · LLMs · Natural Language Processing · Web Scraping · Data Visualization
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