Foundation · 2 min
What you need to know
How can ecommerce revenue be broken into drivers?
A useful starting identity is revenue = eligible traffic × conversion rate × average order value, with definitions aligned. Extend it for cancellations, refunds, shipping and product mix when explaining net results.
How do I identify which metric caused a revenue change?
Hold definitions constant and calculate how traffic, conversion and AOV changed, then quantify mix and refund effects. A bridge or sequential substitution can assign the approximate contribution of each driver.
How do I distinguish traffic problems from conversion problems?
Traffic is the main problem when eligible visits fall while like-for-like conversion remains stable; conversion is the main problem when visits hold but progression falls. Segment both because mix can move the overall rate.
How do I distinguish conversion problems from AOV problems?
Conversion is the main problem when fewer eligible visits become orders; AOV is the main problem when order count holds but value per order falls. Revenue may reflect both simultaneously.
How do I identify whether a change is store-wide or product-specific?
Segment the change by product, category, landing page, device and channel and compare each contribution to the total. Stable unaffected segments indicate a localized problem.
A quick store check
Reconcile revenue change using traffic × conversion rate × AOV, then examine mix and refunds for the remaining difference.
Foundation · 2 min
Frequently asked questions
Does traffic multiplied by conversion and AOV always equal reported revenue?
It should approximate revenue only when definitions align; taxes, shipping, refunds, duplicate orders and unit differences create gaps.
Should decomposition use overall averages?
Start there, then segment because product or channel mix can make the aggregate explanation misleading.