E-commerce events: the funnel before purchase
E-commerce events are a recommended set that model the shopping funnel before and around purchase: view_item, add_to_cart, begin_checkout, add_payment_info, and purchase. Each shares a common items array, so the same product schema flows through the journey. Implemented consistently, they let you see where buyers drop off — and they carry product data, never buyer identity.
What this means
GA4 defines a recommended e-commerce event set that mirrors the buying journey: view_item_list, view_item, add_to_cart, begin_checkout, add_payment_info, add_shipping_info, and purchase. Each takes an items array with item_id, item_name, price, and quantity — the same product schema reused at every step so a product can be traced through the funnel.
Building a funnel
Because the events share an item schema and fire in journey order, you can build a funnel: how many viewed an item, added it, began checkout, and bought. The drop-off between steps is where the money leaks. Consistency is everything — if item_id differs between add_to_cart and purchase, the steps will not join, and the funnel will look broken when the tracking is the only thing broken.
- view_item -> add_to_cart -> begin_checkout -> purchase
- Shared items[] schema joins the steps
- Inconsistent item_id breaks funnel joins
How it appears in analytics and logs
Missing or inconsistent e-commerce events break the funnel: if add_to_cart lacks the same item schema as purchase, product-level drop-off cannot be computed across steps.
Diagnostic use case
Model the shopping funnel with a consistent set of e-commerce events so you can find the step where buyers drop off, using product data only.
What WebmasterID can help detect
WebmasterID records funnel events first-party with product and value parameters and no shopper PII, so checkout drop-off analysis stays accurate and privacy-safe.
Common mistakes
- Using different item_id formats across steps.
- Skipping begin_checkout so the funnel has a gap.
- Adding personal data to item or event parameters.
Privacy and accuracy notes
E-commerce events describe products and cart actions, not the shopper. Keep item and value data; keep names, emails, and addresses out. This is educational, not legal advice.
Related pages
- The purchase event and e-commerce
The purchase event records a completed transaction and anchors all e-commerce reporting: revenue, items, and conversion value. It carries a transaction id, a value and currency, and an items array describing what was bought. The discipline is to record the order, not the customer — product and revenue data belong in the event, personal identity does not.
- Funnel analysis: finding the leak
Funnel analysis follows visitors through an ordered set of steps (view → add to cart → checkout → purchase) and shows where they fall out. It turns a single conversion rate into a map of where the loss happens. The pitfalls are step definition, small-sample noise, and assuming a strict order where users actually skip around.
- Conversion rate: definition and denominators
Conversion rate is the share of some base that converted. The trap is the denominator: conversions per session, per user, and per unique visitor give different numbers and mean different things. Without stating the base, a conversion rate is ambiguous — and comparing rates with different bases is meaningless.
- Event Explorer
Trace e-commerce events across the funnel.
Sources and verification notes
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.