Pricing & Product · ML
Double new-product hit rate with review NLP
2×
Public reviews already describe what buyers wanted and did not get — the problem is that nobody can read fifty thousand of them. This engagement reads them at scale and turns the recurring complaints into product specifications.
The process
Collect the review corpus
Reviews scraped from public listings across your products and the category leaders, with rating, variant, date and verified-purchase status kept intact.
Clean and segment the text
Language detection, deduplication and incentivised-review filtering run first, because a corpus full of template praise teaches the model nothing.
Mine the themes
An NLP pipeline clusters sentences into complaint and praise themes, then scores each theme by frequency, rating impact and trend over time.
Write specs against the evidence
The top themes become concrete product requirements — sizing, packaging, durability, instructions — each traced back to the review lines that justify it.
Track the next release
The same pipeline re-runs after launch, so you can see whether the complaint theme actually fell rather than assuming it did.
What we need from you
- Your product URLs and the competitor set to compare against
- Category and variant structure so themes can be grouped
- A product owner who can act on the findings
What you get
- Cleaned review corpus with theme labels
- Ranked complaint and demand themes by product line
- A specification brief per priority theme, with source quotes
Timeline
Three to five weeks, depending on corpus size.