Analysis of Amazon product review rating trends and factors affecting rankings
On the Amazon platform, through analysis of product review scoring trends and regression analysis of the impact of product ratings, number of ratings, exposure price, price difference and other factors on rankings, it can help operators better understand market dynamics and formulate Effective strategy. This article will combine these two aspects and explore how to use this information to optimize product listings.
Review score trend analysis
In product portraits, “cumulative average review scores” is an important indicator. By calculating the cumulative average of the “Rating” column data in the “Product Portraits” Excel table and visualizing it through Excel’s line chart, we can observe the trend changes in review scores. Specifically, in the search results for the “dress” keyword, when the search ranking exceeds 1,000, the cumulative average review score dropped from about 4 points to about 3.4 points, which indicates that the products ranked after 1,000 have a lower review score. are generally low, meaning these items may not be specifically optimized for review scores.
Based on this finding, sellers can adopt two strategies: If their products rank after the 1,000th place in the search results for the “dress” keyword, they can improve their review scores through direct reviews, reviews, or submissions. Enhance the competitiveness of the product; if it ranks high, you should further analyze the change trend of review scores of the top 1,000 or top 500 products.
In addition, fluctuations in review scores can also reveal some patterns in head listings. For example, although the review score of the head listing remains around 4 points overall, the fluctuations in the score show that even the best-selling products have differences in quality. Therefore, operators can improve the quality of goods by improving the supply chain, thereby having the opportunity to surpass competitors.
The impact of product ratings, number of ratings, exposure price and price difference on ranking
In order to explore the specific impact of different variables on product sales rankings, the captured data needs to be cleaned first, especially the non-numeric data in the “Number of Ratings” column. After completing the data cleaning, a new variable – “commodity price difference”, which is the difference between the highest price and the lowest price within the commodity price range, is introduced to facilitate subsequent regression analysis.
Next, in a new worksheet in Excel, copy the key columns in the original data (such as ratings, number of ratings, exposure price, etc.), and take the logarithmic function value for these data. This is done to convert a change in sales into a change in ranking percentage, which is a common practice in the marketing world.
Finally, use Excel’s data analysis tool to conduct regression analysis, and set the dependent variable to be the ranking of the product (InBSR). The independent variables include the logarithmic function of the score, the number of scores, the exposure price, and the price difference. Through such analysis, we can draw conclusions about which factors have a significant impact on product ranking.
To sum up, through the analysis of Amazon product review scoring trends and the regression analysis of the impact of product ratings, number of ratings, exposure price, price difference and other factors on rankings, sellers can not only have an in-depth understanding of the current situation of the market, but also analyze the current situation of the market. This adjustment strategy optimizes product lists and improves sales performance.