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A conversion event is a specific, tracked user action that signifies the completion of a business goal or a meaningful step toward one. In ecommerce, conversion events range from macro-conversions (a completed purchase, a subscription sign-up) to micro-conversions (an add-to-cart, a product wishlist addition, a checkout page reached). Conversion events are the primary measurement unit in A/B testing — when you run an experiment, you designate one conversion event as the primary success metric and measure how its rate differs between the control and the variant.
Macro-conversion events (primary goals):
purchase_completed — order confirmed, payment processedsubscription_started — recurring plan selectedlead_form_submitted — potential customer contact capturedMicro-conversion events (progress indicators):
add_to_cart — product added to cartcheckout_started — cart → checkout step initiatedpayment_details_entered — high-intent checkout step reachedemail_signup_completed — newsletter or waitlist joinedMost A/B tests on product or category pages use micro-conversion events (add-to-cart rate) as the primary metric because they accumulate faster than purchase events, enabling tests to reach significance more quickly.
The conversion event you choose as the primary A/B test metric determines what you are actually optimizing for. A test that shows a variant increases add_to_cart rate by 12% but decreases purchase_completed rate by 3% has mixed results — the variant may be attracting lower-intent users to add items without buying. Selecting the right conversion event for each test context, and understanding the relationship between micro and macro-conversion events, is what separates sophisticated CRO programs from those that optimize for the wrong outcome.
Boat Lifestyle ran a product page test with add_to_cart as the primary conversion event. The variant (showing a "trending now" badge) lifted add-to-cart rate by 11% at 95% confidence. But when the team checked purchase_completed as a secondary metric, the variant's purchase rate was only 4% higher — suggesting the badge was attracting lower-quality intent, with more users adding but fewer completing the purchase. This insight caused the team to redesign the test with checkout_started as the primary metric instead, to filter for stronger purchase intent from the start.
purchase_completed is ideal but slow to accumulate; add_to_cart is fast but less directly tied to revenue.In any A/B test, the conversion event is the binary signal your testing platform counts per visitor (did this visitor trigger the event, yes or no) to compute conversion rate for each group. The precision and reliability of your conversion event implementation is the single largest determinant of result accuracy. A well-defined, reliably tracked conversion event makes every test more credible; a poorly instrumented one can produce any result regardless of how good the variant is.
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