E-commerce Returns Analytics: Return Rates, Reasons, Reverse Logistics Cost

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Count lines and returns per category and per size with COUNTIF and COUNTIFS, compare the return rate by units with the rate by value, and name the problem category.
• Turn the reason codes from the returns portal into shares and a cumulative share, measure the fit problem and build a Pareto chart.
• Build the full cost of one return per category, the monthly return cost, the weighted average cost per return and its share of net sales.
• Split the returns into refunds, exchanges and store credit, calculate the margin each one keeps, and measure what a shift towards exchanges is worth per month.
• Follow the returned units through grading: written-off units, the restock rate, the value recovered and the recovery rate per category and in total.
• Count orders and returns per customer with COUNTIF and COUNTIFS, calculate each return rate and flag the customers above both thresholds for review.
• Measure how long customers take to request a return, count the requests outside the return window, average the time to refund and the share refunded within the promise.
• Model what a 4.95 USD return fee does to orders, returns, margin, return costs and fee income, find the change in contribution and chart both policies side by side.