From 40% to 94% Event Capture: Fixing Woolworths' GA4 Migration

Working with Sean and team was refreshing.

Rebecca H., Woolworths

The situation

Woolworths is one of South Africa's most recognised retailers. The premium "Woolies" brand spans food, fashion, beauty and homeware, with a fast-growing online business and the Woolies Dash on-demand grocery operation. Decisions across merchandising, marketing and trading all lean on what the analytics say is happening in the cart and at checkout.

Then Google changed the rules on measurement. The shutdown of Universal Analytics pushed every business on the platform onto GA4, a different, event-based model. For a retailer running high transaction volumes across multiple categories, that move was not a quick checkbox. Done badly, it meant flying blind through the reports a retailer relies on most: what people view, what they add to cart, and what they actually buy.

The task

By the time Woolworths brought us in, the GA4 transition had left real holes in the data. Across the e-commerce funnel, only about 40% of events were being captured reliably. The rest were dropping silently, distorted by legacy data structures carried over from the old Universal Analytics setup and an event layer that no longer matched how GA4 expects to receive data.

That 40% was not just a technical statistic. It meant:

• Revenue and conversion reports could not be trusted, and teams knew it, so they hesitated to act on them.
• Inconsistent, legacy data structures were polluting clean events with mismatched names, types, and missing parameters.
• Attribution was unreliable, which undercut the paid-media and merchandising decisions that depend on accurate event data.

The mandate was clear. Close the gaps, clean up the mess, and get e-commerce measurement back to a number the business could stake decisions on.

The action

We were brought in specifically to solve the data gaps, and we treated it as a data-quality program rather than a one-off tag fix.

• Audited the full funnel. We mapped every e-commerce event end to end, from view_item and add_to_cart through begin_checkout and purchase, against what was actually firing, and pinned down exactly where and why events were dropping.
• Cleaned up legacy data structures. We rebuilt the old Universal Analytics scaffolding into a consistent, GA4-native schema: standardised event and parameter names, correct data types, and one source of truth for the data layer instead of a patchwork of old and new.
• Rebuilt the e-commerce event layer. We re-implemented tracking to GA4's enhanced e-commerce spec so every step of the funnel sent complete, well-formed events, with no more half-populated payloads.
• Hardened data efficiency. We tightened how and when events fired to cut loss from race conditions, navigation, and consent edge cases, the quiet places where capture rates bleed out.
• QA'd against reality. We validated every fix across the live funnel until the reported events matched actual user behaviour, not just a clean debug view.

Because the work was scoped around data quality rather than cosmetic reporting, each fix compounded. Every cleaned structure and corrected event raised the capture rate for everything downstream of it.

The result

We took Woolworths' e-commerce event capture from 40% to 94% across all e-commerce events. A measurement system the business had stopped trusting became one it could run on again.

• Capture rate climbed from 40% to 94% across the full e-commerce event set.
• Clean, GA4-native data structures replaced the legacy scaffolding, so new events stay consistent instead of decaying.
• Reporting teams could trust the numbers again, with revenue, conversion and funnel reports finally matching reality.
• Attribution became far more reliable, giving paid-media and merchandising decisions a dependable foundation.

The headline number tells the story. A 40% capture rate is a system you second-guess. A 94% capture rate is one you build a business on.

The takeaway: a GA4 migration is not "done" when the tag loads. It is done when the data is complete enough to trust. Most teams find the gaps months later, in a report that does not add up. We find them on purpose, and close them.

Mid-migration and unsure what your real capture rate is? Send us a brief and we'll come back with a plan in 48 hours.