How to pull customer pains out of reviews: why quotes matter more than ratings
A five-star rating tells you a customer is happy. It does not tell you what problem they had, and that problem is what your next buyer is searching for.
Why the text matters more than the rating
Ratings are useful for reputation, but they all look alike: most products that survive have a high average. The text is where people explain themselves. A sentence such as I used to spend Sunday evenings on invoices tells you the pain, the context and the words a similar person would recognise in an ad. No rating carries that.
What to look for in each review
- The situation before: what was wrong, annoying or risky.
- The trigger: what made the person start looking for a solution now.
- What they tried before and why it did not work.
- The result: what changed after buying, ideally in concrete terms.
- Doubts they had before buying, which often become objections in ads.
Not every review contains all of these. Many say only great service. Skip them without regret; a dozen detailed reviews are worth more than a hundred short ones.
How to record what you find
- Give each pain a short label of three to six words, for example fear of missing a deadline.
- Copy the exact quote next to it, without correcting grammar or style.
- Merge labels that mean the same thing, but keep all the quotes.
- Count how many reviews mention each pain and note where the reviews came from.
A small example (illustrative numbers)
A furniture repair workshop reads 30 reviews. The owner expected price to be the main topic. In the text, 11 reviews describe the fear that an old family piece would be ruined, nine praise that the work was done on the promised date, and only three mention price. The ads that follow speak about careful handling of things that matter to people, quoting a customer who wrote that the chair from her grandmother came back looking like itself, only stronger.
How OrgLines handles it
In the Market research module, which our team runs inside the client's cabinet, reviews are added as text, audio or video, and the AI picks out pains together with verbatim quotes. When new reviews arrive, the AI processes only the new ones and keeps the manual edits made earlier. The pains then go to the survey, which ranks them.