How to Automate E-commerce Reporting

Recorded in Polish. English subtitles are available on the recording via YouTube's auto-translation.

Picture your Monday stand-up. Mateusz says Google Ads reported 100 conversions. Hania says Meta reported 120. Iwona from accounting says there were 90 transactions — 20 of which were returned. Instead of discussing decisions, the whole company is debating the data.

This is the first webinar in the Witbee series. It shows how to reach a state where nobody asks for data, waits for it, or requests it — because everyone already has it in one report, and AI has already run the analysis.

Four problems worth an honest audit

"We have Google Analytics, so we have analytics" — the most common lie I hear, especially from management. Across dozens of analytics audits run for Google's clients in Poland and Europe, I can count on one hand the companies whose data I would actually trust. Disappearing transactions, missing events, incorrect revenue across currencies, broken consent. And even a correctly implemented GA4 is not enough — it holds no CRM, affiliate, or marketplace data.

Data silos. With one market you can just ask Frank across the desk. With 15, 17, or 24 markets, every market means separate Google and Meta accounts and a stack of other systems — and suddenly "Kacper, prepare the report for Monday" eats an eighth, sometimes a quarter, of a specialist's month. Someone who should be analysing data, not collecting it. And who will eventually take a holiday.

Algorithms have no intuition. You may have 30 years of experience and sound instincts — Google Ads and Meta do not. With ROAS at 500 in the platform and 250 in reality, the algorithm aggressively buys ever more expensive traffic. With understated ROAS, it fails to invest where it should. Fed with your data, it markets to your customers rather than to internet users at large.

AI without data. Garbage in, garbage out. An agent with no access to your data, no understanding of it, and no knowledge of your business context will not support your organisation.

The six steps

1. A place for your data in the cloud. Google Cloud (BigQuery), or Azure or AWS. A new account comes with 300 USD in credit.
2. Data pipelines. Start by connecting GA4 to BigQuery — until you do, the historical data simply is not there, and it cannot be backfilled. Then add CRM, ad platforms, the store, and marketplaces.
3. Organising the data. A single system can produce a dozen or more tables; larger implementations reach 800. Raw, heavy tables incur processing costs on every query. They need to be transformed into a handful of light, purpose-built tables.
4. Visualisation. Looker Studio connects well to BigQuery and lets you tailor views to specific people in the organisation, with control over who sees what.
5. Feeding data back to ad platforms. Joined, recalculated, with returns deducted — back into Google Ads, Meta, and marketing automation: supplementary product feeds, lists of one-time buyers, champions, and lost customers.
6. Documenting the data for AI. Only then does AI know what the tables mean, understand your schema and business context, and stop answering generically.

The hard truth, and a case study

Steps 1 and 2 are easy. Steps 3–6 demand cloud, SQL, and analytics experience that most e-commerce businesses do not have in-house. Doing it by hand means long, expensive projects that collapse the moment someone leaves the company.

Unitrailer operates across 25 markets with roughly 100 data sources. It has one coherent report covering every market, refreshed hourly, available to all decision-makers — implemented with almost no involvement from IT.

The session closes with a live walkthrough of the All-in-One report in Looker Studio on real data: marketing data from every platform, product data, analytics joined with ad investment, recalculated attribution paths, e-commerce customer data (new, returning, LTV), and marketplaces — all in one place.

Who it is for

E-commerce managers, especially in multi-market businesses, and agencies serving them. Everything covered here works from both sides of that relationship.