Mercadex Signal
Your customers already told you why they buy. So did your competitors'.
Signal reads every review on your products and your competitors' — plus Q&A, returns reasons and search-term reports — and turns them into the specific benefits, objections and language that should appear in your listings.
Why now
Amazon's search is no longer a keyword-matching engine. COSMO models shopper intent from queries and purchase patterns; Rufus answers shoppers' questions conversationally on top of it. A listing now competes on whether it visibly answers a real question — "will these earbuds stay in on a run?" — not on how many keywords it stuffs into a bullet.
That makes the voice of the customer a ranking input, not just a marketing nicety.
The reviews contain the questions. Signal extracts them.
What it produces
- Benefit ledger
- Every benefit customers actually name, ranked by frequency × sentiment × purchase influence, with verbatim evidence attached.
- Objection map
- The recurring doubts and complaints, yours and your competitors', with a suggested pre-emptive answer for each.
- Competitive gap analysis
- What competitors' buyers praise that you don't offer or don't say, and where competitors are weak enough to attack.
- Intent-phrase set
- The natural-language questions and phrasings shoppers use, mapped to where they should land (title / bullets / A+ / backend / Q&A).
- Creative brief
- A structured brief handed straight to Mercadex Studio: what this SKU's images and video must communicate, in priority order.
- Change log
- What shifted since last run: new complaints, emerging use cases, a competitor's fixed defect.
"benefit_ledger": [
{
"theme": "",
"statement": "",
"priority": 1,
"volume_pct": 0.0,
"valence": {"positive": 0, "negative": 0},
"trend": "emerging | stable | fading",
"evidence": [
{
"review_id": "", "quote": "",
"rating": 0, "date": ""
}
],
"requires_substantiation": false
}
] Every line carries its evidence. No evidence, no output line.
How it works
-
01
Ingest reviews, ratings, Q&A and search-term data for a target ASIN set (yours + a competitor set).
-
02
Cluster into themes; separate genuine product signal from noise, logistics complaints and suspicious reviews.
-
03
Score each theme by volume, sentiment, recency and correlation with rating and conversion.
-
04
Map themes to listing surfaces and produce the outputs above.
-
05
Hand the brief to Studio; measure whether the resulting listing moves CTR, conversion and rank.