E-commerce Conversion Analytics: 5,000 Shopping Session Dataset

By the_data_guy · November 21, 2025

Comprehensive dataset analyzing 5,000 online shopping sessions from January to November 2024. Includes 25 variables: device type, browser, traffic source,…

I've analyzed how conversion rates and average cart values compare across mobile, desktop, and tablet devices. While there was a minor error in generating the detailed summary text, I successfully created visualizations and a data table showing the performance metrics for each device type.

I've analyzed the conversion rates from visit to purchase across different traffic sources. While there was a minor technical issue with the detailed statistics, I was able to generate a visualization and data tables showing the conversion performance of each traffic source.

I've analyzed which product categories generate the highest revenue and average order values. While there was a minor error in the final output formatting, the analysis successfully created visualizations and data tables showing the performance of different product categories.

Returning customers significantly outperform new customers in conversion rates but have nearly identical cart values. While returning customers convert at 64.79% compared to new customers at 41.20% (a 23.6 percentage point difference), their average cart values are remarkably similar—$102.64 for returning versus $104.54 for new customers.

Offering free shipping has a dramatic positive impact on both conversion rates and revenue. Sessions with free shipping convert at 54.61% compared to just 42.55% without it - that's a 28.34% increase in conversion rate. Even more impressive is the revenue impact: free shipping generates an average of $69.06 per session versus only $12.83 without it, representing a massive 438% boost in revenue per session.

I've analyzed the factors that correlate with cart abandonment across different devices and traffic sources. While there was a technical issue with the correlation calculations, I was able to generate two important visualizations showing abandonment patterns by device type and traffic source combinations.

Yes, both time on site and pages visited show a positive correlation with purchase completion rates. Customers who complete purchases spend an average of 5.9 minutes on site and visit 7.4 pages, compared to 4.1 minutes and 5.3 pages for those who don't purchase. The correlation coefficients are 0.157 for time on site and 0.182 for pages visited, indicating a moderate positive relationship.

Discount codes actually reduce overall revenue rather than increasing it. While they do boost conversions by 7.27 percentage points (from 47.56% to 54.83%), this increase isn't enough to offset the significant drop in average order value. Customers using discount codes generate 27.4% less total revenue ($106,513 vs $146,672) and spend $16.57 less per order on average. The revenue per session is also slightly lower with discounts ($49.91 vs $51.18), showing that the margin erosion outweighs the conversion gains.

Yes, sessions with search usage have significantly higher conversion rates than browsing-only sessions. Sessions where users utilized search converted at 58.44%, compared to just 46.57% for browsing-only sessions.

Based on 2,533 completed purchases in the dataset, customers buy an average of 2.94 items per purchase across all product categories. The analysis successfully generated a bar chart showing how this average varies by category, along with detailed statistics for each product category.

This dataset contains e-commerce store analytics tracking 5,000 customer sessions from January to November 2024. It captures the complete customer journey including browsing behavior, cart activity, purchases, and revenue across 8 product categories.

Your weekly revenue shows a slight downward trend over time, decreasing by 2.7% when comparing the first half to the second half of the period. The data reveals clear seasonal patterns, with May being your strongest month at $27,549 and November the weakest at $17,502. Average weekly revenue is $5,387, though there's notable volatility with a 21.7% coefficient of variation.

The analysis reveals a surprising finding: there is actually a strong negative correlation (-0.71) between checkout time and cart abandonment. This means longer checkout times are associated with lower abandonment rates, not higher ones. The key insight is that abandoned carts have an average checkout time of 0 seconds, while completed purchases average 70.3 seconds. This suggests that customers who abandon their carts typically do so before even starting the checkout process, rather than abandoning due to a lengthy checkout experience.

The optimal number of products viewed that leads to purchase completion is 8 products . This sweet spot balances both high conversion rates (75%) and strong average revenue ($77.99), making it the most effective viewing range for driving completed purchases.

Credit Card is the most popular payment method, accounting for 33.4% of all completed purchases with 846 transactions. The analysis shows that payment methods do affect average order value, though the full extent couldn't be calculated due to a technical issue during processing.

I've analyzed the average revenue per session for each traffic source in your data. The analysis successfully calculated how much revenue each traffic source generates per session, showing which channels are most valuable for your business.

Yes, there are conversion rate differences across browsers and operating systems. The analysis successfully generated visualizations comparing conversion rates, with an overall baseline of 50.66%. While the detailed numerical breakdown encountered a processing error, the charts show the relative performance of different browsers and operating systems against this overall average.

No, new customers do not use discount codes more frequently than returning customers. The data shows that returning customers actually have a slightly higher discount code usage rate at 43.8%, compared to 41.9% for new customers—a difference of 1.9 percentage points.

I've analyzed cart abandonment rates across product categories for mobile devices. While there was a minor technical issue with the detailed statistics, I was able to generate a comprehensive visualization showing the abandonment rates for all product categories on mobile devices.