Analysis of customer reviews
Insert reviews, correspondence, or survey responses. The tool will pull out recurring themes, separate product issues and expectations issues, and provide customer formulations for texts.
There are two different types of negativity in reviews: broken product and mismatched expectations. The first is repaired by the product, the second by the description and photo in the card, and it is expensive to confuse them. The tool separates them, pulls out recurring topics and provides verbatim wording from clients that are ready to be transferred to the landing page text.
AI answers are free, but limited by the overall site limit. If the limit is reached, come back later: repeated clicks will not speed up the response.
Half of the complaints are not fixed by the product, but by the description
In any set of reviews there are two fundamentally different types of negativity. First: the product is broken, the service was not provided, support did not respond. This is a product problem, and it cannot be covered with text. Second: what arrived was not what the person imagined, although what arrived was exactly what was sold. This is a problem of description, and there is no need to remake the product here.
Confusion between the two costs both ways. You can spend months refining a product where it was enough to add one photo and a line about the packaging to the card. You can, on the contrary, rewrite the description when people write about a real defect.
Feedback in positive terms is especially valuable. Clients describe benefits in their own words more accurately than a copywriter does, because they speak from their own situation. These quotes are ready-made material for the hero section and for the block with objections, and it is better to take them verbatim.
What the tool intentionally doesn't do: It doesn't count interest. Reviews are a biased sample; those who are extremely satisfied and those who are extremely dissatisfied tend to write to it. The phrase “35% of customers are dissatisfied with delivery” from thirty reviews would be falsely accurate.
What the tool intentionally doesn't do
Does not insert numbers, deadlines, benchmarks and names of companies that did not exist in your input. If the output requires a number that you did not provide, in the answer will be marked “to clarify” rather than a plausible number. This limitation is worth in the system prompt and works against the main property of language models: It’s difficult to complete what’s missing.
The request text is sent to the AI provider, and the response is returned to the browser. The site does not save these texts in the database. To limit expenses separately the number of requests and the anonymized connection identifier are taken into account. If the free limit is reached, the tool will offer to come back later.