Methodology & data.

How you can tell the numbers are sound

Scoutvo works exclusively with signals that can be verified:

• Public Amazon product pages — titles, bullets, descriptions, prices, reviews, variants and Buy Box state of the analysed category.
• Seller profiles and legal information — imprint data, company names, addresses and contact details of sellers.
• Our own monitoring — price and Buy Box histories of monitored products, built from our daily checks (90-day history).
• Our own test data — our own image A/B tests and 111 analysed variant families from our own listings, continuously growing. This data comes from our own day-to-day selling, not from third-party accounts.

What we do not use: Amazon-internal data, data from other sellers’ accounts, or purchased clickstream profiles.

How fresh the data is

Monitored products are checked daily for price and Buy Box changes; alert rules run 1×, 2× or 4× daily depending on plan, your own ASINs hourly. A report uses the data available at the time of analysis and carries a timestamp — you always see when a statement was made.

Origin classification: the procedure

For every seller in a category, Scoutvo combines several signals into a classification (Germany/DACH, EU, China, disguised, unclear):

• Imprint and register information (registered office, legal form, VAT details)
• Anomalies such as foreign dialling codes behind a German company name, or bulk addresses hosting dozens of “brands”
• Buy Box behaviour and revenue signals within the category

Every classification carries a confidence value and shows the source it is based on. If confidence falls below our threshold, the result is “unclear” — we don’t guess.

How we measure quality: We regularly compare the automatic classification against manually researched samples (imprint, registers, manufacturer websites). The match rate is around 87–90% (≈88% on average). That is a solid indicator — but not a certainty, and that is exactly how we label it in the report.

Purchase criteria, keywords and images

Purchase criteria: Scoutvo extracts product features from the listings and review texts of a category and relates them to sellers’ revenue signals. Important: the result is a correlation, not proven causation — it shows which features cluster among successful sellers and separates them from box-ticking attributes with no measurable effect.

Keywords: Your listing is compared against the category’s top competitors using term weighting (TF-IDF), cleaned of brand names and filler words. This surfaces terms that are present among successful competitors but missing from your listing.

Images: Main-image recommendations draw on the evaluation of our own image A/B tests and on comparing image building blocks (badges, cut-outs, usage scenes) within the category. Image recommendations are hypotheses with a test recommendation — not a promise.

Listing suggestions

Every copy suggestion is traceable to its source — a review quote, a competitor listing or your own product data. What cannot be substantiated, we do not suggest. Scoutvo additionally checks variants for consistency (description, images, specs) and warns when a variant setup needlessly splits the review pool.

Where we promise you nothing

Honesty is part of the methodology. You should know these limits:

• Origin classification is an indicator with a confidence value, not a legally binding finding.
• Revenue signals are based on publicly observable metrics (including rank and review dynamics) and are estimates.
• Feature-impact analyses show relationships, not proof — real certainty only comes from your own A/B test.
• Data density varies by category: in small niches with few sellers, conclusions are phrased more cautiously.

This page reflects the state of July 2026. If our procedures change, we update this description.