Analysis of Product Review Number and Rating Distribution: A Reference Guide for Operators

In product operations, analyzing the distribution patterns of review scores and quantities is of vital reference value for new product launches and optimization of old models. Using the data in the “Cleaned Product Image Data” Excel table, we will deeply explore the distribution characteristics of the number of reviews and ratings.

Review quantity distribution analysis

By selecting the “Number of Ratings” column in the Excel table and using the Excel chart function to insert a line chart, the operator can observe the fluctuation trend in the number of review ratings. The analysis results show that high-ranking products (that is, products ranked in the first few pages of search results) usually have a high number of reviews, often exceeding 5,000. This phenomenon reflects the historical sales distribution of this type of product, because there is a certain positive correlation between the number of reviews in the same category and sales.

It is worth noting that even if the number of reviews for some products is close to 5,000 (such as products ranked above 4,000, 7,000, and 10,000), their exposure ranking value may be lower. This result shows that in the “dress” category of goods, competition among goods is fierce and the life cycle is relatively short, which is consistent with common sense in the apparel industry. Therefore, the analysis of review quantity distribution can guide operators to better evaluate the life cycle of products under different keywords.

Furthermore, the analysis of review quantity distribution also helps operators understand the differences in consumer demand in different categories. Taking the search results of the “compression springs” keyword as an example, the crawler tool is used to capture the number of reviews of the listing and stored in an Excel table named “compression springs Listing Catch” for visual processing. Compared with the “dress” search results, the distribution of the number of reviews for compression springs products does not show an obvious “28 distribution” phenomenon, but shows a more even distribution.

The difference in consumer demand for these two types of commodities has become an important factor causing this distribution difference. “Dress” products mainly rely on perceptual consumer demand. Many consumers tend to buy products that are currently hot-selling and think they are suitable for them. The products related to “compression springs” are more derived from rational needs, and consumers will carefully choose them, forming a relatively even distribution of the number of reviews.

Review score distribution analysis

When analyzing the distribution of product review scores, by selecting the “Rating” column and inserting a histogram, the operator can clearly identify the central tendency of the scores. The data shows that the scores are mainly concentrated in several ranges: 0~0.2, 3.8~4, 4.0~4.2, 4.8~5. This means that if an operator has just launched a new product, proper evaluation, submission or direct review will help increase sales, because in the rating range of 4.8~5, there are 2568 listings that meet this standard.

In comparison, when considering the optimization of older listings, the rating data in the 3.8~4.4 range should be used as a reference to avoid forcing the rating to be raised above 4.4. In fact, the number of reviews in this range accounts for the majority of high-scoring reviews, indicating that operators can make strategic adjustments based on this data.

In addition, Pareto analysis using histograms for review scores also points to a similar conclusion: review scores under this category are mainly concentrated in the unscored range of new products (0~0.2), and are improved through evaluation and other methods. Products with ratings ranging from 4.8 to 5, and listings with high-quality product ratings ranging from 3.8 to 4.4.

The above analysis provides operators with a deep understanding of market trends, consumer needs and the competitive environment, helping to formulate more effective product strategies.