The influence of the number of products on an online store page on visibility in search engines: a mini-study
Hi all! My name is Sergey Yurkov, I am the Head of SEO in SEO&ROI. Today we will discuss how the number of products on an online store page affects its visibility in search engines and, as a result, the volume of traffic and potential sales. Let's conduct a mini-study based on the analysis of one e-com site and consider whether there is a relationship between the number of products on the page and visibility indicators in search.
1. Initial data for the mini-study
One online store was selected for the study, from which data on 1,800 URLs was analyzed. This approach ensures uniformity of site-level factors, allowing you to focus on differences between individual pages.
Main parameters:
Number of products per page: number of products presented on each catalog page.
Visibility indicators: data on how many requests the page is displayed in the top search results (top 1, top 3, top 5, top 10, top 50).
Visibility: traffic forecast calculated as the sum of impressions for all queries multiplied by the CTR of positions for these queries.
2. Pre-processing of data
Before analysis, data preprocessing was performed to remove outliers and ensure reliability of the results.
Processing steps:
Calculation of the first (Q1) and third (Q3) quartiles for data on the number of products on pages.
Calculating Interquartile Range (IQR)
Defining boundaries for identifying outliers:
Data filteringbeyond the established boundaries.
As a result of processing, pages with an abnormally large or small number of products were removed, which made it possible to focus on the bulk of the data and avoid distortion of the results.
3. Conducting statistical tests to determine the presence of a relationship
Formulation of hypotheses
Null hypothesis (H0). The correlation between the number of products on the page and visibility indicators is zero (no relationship).
Alternative hypothesis (H1). The correlation is different from zero (dependence exists).
Checking the significance of the Spearman correlation coefficient
To determine the degree of dependence, Spearman's rank correlation coefficient was used. Correlation coefficients and corresponding p-values were calculated for various visibility metrics.
Results:
Visibility based on the number of products on the page:
Spearman correlation coefficient: 0.325
p-value: 3.633
conclusion: The correlation is significant at the 0.05 significance level.
Number of queries in the top 50 based on the number of products on the page:
Spearman correlation coefficient: 0.214
p-value: 2.561
conclusion: The correlation is significant at the 0.05 significance level.
Number of queries in the top 10 based on the number of products on the page:
Spearman correlation coefficient: 0.299
p-value: 8.005
conclusion: The correlation is significant at the 0.05 significance level.
Number of queries in the top 5 based on the number of products on the page:
Spearman correlation coefficient: 0.372
p-value: 7.404
conclusion: The correlation is significant at the 0.05 significance level.
Number of queries in the top 3 based on the number of products on the page:
Spearman correlation coefficient: 0.360
p-value: 1.149
conclusion: The correlation is significant at the 0.05 significance level.
Number of queries in the top 1 based on the number of products on the page:
Spearman correlation coefficient: 0.254
p-value: 4.103
conclusion: The correlation is significant at the 0.05 significance level.
Data distribution graphs show that with an increase in the number of products on the page, there is a certain tendency towards an increase in visibility indicators and the number of requests in top positions. However, a significant portion of pages have low viewability scores, regardless of the number of products.
4. Final conclusions from the mini-study
Detected dependency
Weak positive dependence between the number of products on the page and visibility indicators in search engines.
Correlation coefficient about 0.3 indicates a weak relationship, but statistically significant.
Estimating test power after the fact
Test power with a correlation coefficient of 0.3, a sample size of 1,820 and a significance level of 0.05 is 1.0.
Interpretation: This means that given the parameters, the test is almost guaranteed to detect a significant correlation if one actually exists.
Practical significance
Weak positive dependence means that as the number of products on a page increases, the number of queries for which the page is displayed in the top, increases slightly.
Correlation coefficient 0.3 suggests that changes in one factor (number of products) explain a small part of changes in another factor (number of top queries). In this case, that's about 9% of the total variation (0.3^2 = 0.09) in the number of top 10 queries that can be explained by the number of products on the page.
The remaining 91% variations are due to other factors. That is, 91% of other queries in the top are due to other factors.
Conclusions and recommendations
Statistical significance correlations confirmed, but practical significance small. In your case, although the correlation is statistically significant, it explains only a small portion of queries being in the top.
Other factorsfactors such as content quality, behavioral factors, on-page optimization and inbound links are likely to have an impact greater influence on the visibility of pages in search engines.
Recommendation. To improve visibility, a comprehensive page improvement is required, including increasing the number of products on the page, optimizing content, technical aspects and improving the user experience.
Conclusion
The mini-study showed that an increase in the number of products on an online store page has a weak but statistically significant positive relationship with visibility in search engines. However, the impact of this factor is small, and to achieve meaningful results in SEO, many other parameters must be taken into account and optimized.
Future Research may be aimed at analyzing the impact of other factors on page visibility, as well as conducting similar studies for other search engines such as Google.
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