In the product portrait, if you want to analyze the review score trend, you can use the indicator “Cumulative average of review score”. After calculating the cumulative average of the “Score” column data in the “Product Portrait” Excel table, you can visualize it through the Excel line chart.
By observing the cumulative average line chart, you can find that under the exposure results of the keyword “dress”, if the search ranking is higher than 1000, the cumulative average of review scores will drop from about 4 points to around 3.4 points, which means that most products after the search ranking of 1000 are not optimized for review scores. At this time, you can combine this conclusion to optimize the listing: on the one hand, if your listing is after 1000 in the “dress” search ranking, you can improve the review score through direct review/evaluation/submission of reviews and other methods to enhance the competitiveness of the listing; on the other hand, if your listing is before 1000 in the “dress” search ranking, you can analyze the changes in the cumulative average of reviews in the top 1000 or top 500.
The fluctuation of the cumulative average of review scores can reveal many patterns of top listings.
1 The review scores of top listings are not very stable. The review scores of the listings ranked first in search rankings and 200th in search rankings fluctuate significantly, which shows that there are still differences in the quality of products in the best-selling list. Operators can improve the quality of products with lower review scores through the supply chain, thereby increasing the possibility of their own listings overtaking.
2 Although the review scores of top listings have certain differences, the overall cumulative average of review scores is maintained at around 4 points. Therefore, if operators want to sprint to the top search position, they must increase the review score of their listings to around 4 points.