In the product portrait system, analyzing the distribution pattern of product reviews can help operators have accurate review data reference when listing new products and optimizing old models. The following will take the review score data in the “cleaned product portrait data” Excel table as an example to explain.
Select the “Score” column in the table and insert a bar chart through an Excel chart (the specific operation is similar to the operation of inserting a chart above, so I won’t repeat it here), and you can get a review score distribution chart.
According to the review score distribution chart, it can be found that the review scores have an obvious concentration trend, that is, 0~0.23.8~4, 4~4.2, 4.2~4.4, and 4.8-5. Therefore, if the operator has just listed a new product, it is recommended to do appropriate evaluation, review submission or direct review to increase sales. This is because the number of review scores in the 4.8~5 range is quite high, and there are 2568 listings that meet this review score range. At the same time, the number of review scores in the 4.6~4.8 range is not large. This means that it is not that a large number of products are of excellent quality and thus have review scores in the 4.8~5 range, but that a large number of listing operators have adopted the operation of evaluation, submission or direct review (if a large number of products are of excellent quality, the number of review scores in the 4.8~5 range and the number of review scores in the 4.6~4.8 range should not be much different).
If the operator wants to optimize the old listing, the review score in the 3.8~4.4 range can be used as a reference without forcibly raising the review to above 4.4. This is because the number of review scores in the 3.8~4.4 range occupies the mainstream of high-rated reviews, while the number of scores above 4.4 (4.4~4.6 and 4.6~4.8) is not large.
In addition to analyzing the distribution of review scores, you can also use histograms to perform Pareto analysis on review scores.
According to the review score histogram, we can clearly find a conclusion similar to the above, that is, among all the search exposure results under this category, the review scores are mainly concentrated in 0~0.2 (new products have no review scores), 4.8~5 (listings that have been reviewed and sent for review, direct reviews), and 3.8~4.4 (listings of high-quality products).