Digital shelf and marketplaces
Reviews and ratings
Customer feedback on products: star ratings, written reviews, review counts and helpfulness votes.
In practice
Rating count and recency often matter more than the average score.
Keep distinct measures separate
An average rating, the number of ratings and the number of written reviews answer different questions. A product can receive a rating without a written review, and a retailer may aggregate feedback across variants. Record the scale, counts, aggregation scope and observation time instead of treating every displayed score as directly comparable.
Example: comparing two listings
A score of 4.8 from 10 ratings and 4.5 from 2,000 ratings have different evidential weight. Neither figure explains recent customer sentiment on its own. If one listing combines all sizes and another covers only one variant, the comparison also uses different populations. Inspect the underlying scope before interpreting the difference as a product-quality gap.
Building a useful feedback dataset
Retain source and product identifiers, timestamps and the fields needed for the intended analysis. Separate new observations from duplicated reviews and distinguish edited content where that matters. Avoid collecting reviewer details that are unnecessary for the task. Reviews can reveal recurring themes and issues, but they are not a representative survey of every buyer and should not be presented as one.
Import.io captures listings, search rank, content, reviews and every marketplace seller for digital shelf and brand protection programs.
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