The RSR Company Score

Historical Changes

August 24th 2020 - Change of Rating scale from out of 10 to 5

May 2020 - Change of detailed rating scale and historical ratings from 10 to 5

Understanding the ResellerRatings Score

The ResellerRatings Score is designed to give shoppers a clear, reliable picture of what their current purchase or website experience with a store might be like. It heavily weighs recent reviews with a store’s full review history, smoothing out short-term peaks and dips to try and provide a fair, accurate reflection of consumer sentiment if they were to experience using a brands e-commerce store.

What the ResellerRatings Score Is

A weighted rating curve based on review recency, review count, and consumer sentiment.
A measure that gives more weight to newer reviews (especially within the first 6 months to 1 year).
A fair system that captures the lifetime of reviews while highlighting what’s most relevant today.
A safeguard that prevents a single review from overwhelming overall sentiment by using minimum thresholds and “review buckets.”

❌ What the ResellerRatings Score Is Not

It is not a straight average of all reviews.
It does not over-penalize old negative feedback or artificially inflate outdated positives.
It is not static — scores shift as reviews age and new ones are collected.

The ResellerRatings score on a page is our attempt to quantify consumer sentiment into a scoring system that's easy to look at and understand quickly. It is also representative of a score in which any reasonable consumer can see and say to themselves 'this should be my current experience if I were to try and buy from this store today'.

The ResellerRatings Score is

IT IS a specific rating curve based on review count, review recency, review collection types an different methods and weighted buckets
The score takes into account all reviews across the lifetime of a store, however, a review that is within the first 6 months affects the score more than reviews that are older. The more recent a review is, the more weight it holds.

We have a couple of goals for the RSR Score.

We want it to represent consumer sentiment for their current experience with a retailer (if they were to make a purchase or try to engage with service in some capacity).
It is based on consumer sentiment
It is fair to retailers who go through various dips or increases in their business operations
Track retailer histories without punishing them for resolved issues or rewarding them for the outdated positive feedback
Eliminate troughs and peaks, ensuring a smooth and easy-to-understand reflection of all reviews

The algorithm we use at ResellerRatings is designed to give consumers a simplified view of retailer ratings based on consumer opinions. The rating aggregate is not a reflection of a straight average (i.e., number of reviews/score), but rather a snapshot that gives more weight to recent reviews. While all active reviews are considered, reviews posted within the last year influence the score more.

The Score is shown on a sellers store page

We show several scores on the store page. Within finer details of the store page, a customer can dig much, much deeper into the scoring system to understand review trends much, much better.

RSR score being the primary score.
The distribution of the scores across the lifetime of the store (you can dig deeper on detailed ratings for more information here)
Detailed benchmark ratings for particular services (shipping, returns, recommendation, would you come back, etc) which are averages on the last 6 month of scoring data
Detailed historical data on the benchmarks themselves

We believe all this data provides a cumulative and holistic view of ratings and scores over time.

How It Works

Reviews are grouped into five time buckets:
- Last 30 days
- 30–90 days
- 90–180 days
- 180–365 days
- Older than 1 year
Each bucket has a weighted point value, with the most recent bucket carrying the most influence (e.g., up to 60 points).
If a bucket has too few reviews, it merges with the next until the threshold is met, ensuring balance.
The final score = (bucket averages × weights) ÷ total possible weight.

Review Aging and Value

Reviews naturally change in influence over time. A store’s score is always evolving — even if no new reviews are collected. For example, an old negative review will gradually lose weight in the calculation as it ages, and the same applies to an old positive review. Over time, all reviews contribute to the score, but with balanced weighting that reflects recency.

To manage this, we use a bucket system:

Last 30 days
30–90 days
90–180 days
180–365 days
Older than 1 year

Each bucket has a minimum review threshold. If a bucket doesn’t meet the threshold, it merges with the next one to prevent a small number of reviews from disproportionately affecting the score. For instance, a single new review shouldn’t outweigh 20 older reviews — the bucket system ensures fairness and balance.

Once thresholds are met, the average rating for each bucket is calculated. Those averages are then combined using a point-weight system, with more recent buckets carrying more influence (e.g., reviews from the last 30 days can contribute up to 60 points). Buckets without reviews simply don’t factor into the calculation.

In formula terms:

Score = (bucket average × bucket weight) ÷ total possible weight across buckets

While all active reviews are considered, reviews older than a year have much less impact if newer reviews exist. This method smooths out sudden jumps or dips and creates a more accurate, stable reflection of consumer sentiment over time.

🔄 Updates to the Algorithm

We rarely adjust the scoring algorithm, but when we do, we provide advance notice and explain the change. This ensures transparency, fairness, and adaptability as review practices evolve.

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