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Running delivery time vs customer satisfaction analysis...
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Analyze another fileOLS regression of customer review scores on delivery days with order status controls to quantify the star cost per additional delivery day and identify the threshold where satisfaction drops sharply
Use when you have order timestamps and customer review scores and want to quantify the causal effect of delivery speed on satisfaction
Do not use if delivery dates are missing for most orders or if reviews are not linked to specific orders
Built for: E-commerce operations managers, customer experience analysts, logistics managers, supply chain analysts, and marketplace sellers
Typical data source: Order records with purchase timestamps, delivery dates, order status codes, and customer review scores (1-5 stars) -- typically exported from Shopify, WooCommerce, Magento, or an OMS
Dataset with 6 columns
Minimum 30 rows
Multi-dataset module using two related tables from the Olist Brazilian E-Commerce dataset (4,074 Kaggle votes, 496K downloads). Slot 'orders' has purchase and delivery timestamps; slot 'reviews' has review_score (1-5). The R module joins on order_id, computes delivery_days = delivered - purchased, then runs OLS regression of review_score on delivery_days controlling for order_status. Visualization includes delivery-time distribution, score-by-delivery-bin bar chart, and the regression coefficient with CI.
Distribution of delivery days to understand typical and tail delivery times
Average review score by delivery window bin showing monotonic decline
OLS regression coefficients with 95% CI showing star cost per delivery day
Late delivery rate and 1-star review fraction by order status
OLS residuals vs fitted values to validate linearity assumption
Full regression table with p-values, estimates, and standard errors
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Tf038 Live Ttest — When you only need to compare review scores between two delivery speed groups (fast vs slow) and do not need per-day coefficients or order-status controls
Need more power? Price Elasticity — When you need to model how multiple pricing and fulfillment variables interact to affect purchase behavior using econometric elasticity modeling
Similar: Churn Drivers, Attrition Drivers
The OLS regression quantifies the per-day star cost with confidence intervals, translating delivery speed into a measurable impact on customer satisfaction scores.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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