AI Analytics & Attribution

Most reporting problems are definition problems

When two systems disagree about how many leads you generated, the data is usually fine and the definitions are not. One counts a form view, another a submission, a third a qualified enquiry; attribution windows differ; test traffic was never excluded. The result is meetings spent arguing about numbers instead of deciding anything.

So we begin with an audit and a decision: what is a conversion, in one sentence, agreed by the people who use the report. Then we implement that definition once — GA4 events, Tag Manager, CRM stages — and report against it consistently. It is unglamorous work with an outsized effect on decision quality.

Attribution, honestly framed

Attribution is a model, not a measurement, and any agency presenting it as truth is either careless or selling something. Last-click over-credits the final touch and starves demand creation. Multi-touch models are better and still make assumptions. Offline and broadcast channels cannot be tracked to the individual at all.

We combine methods and label them: platform and GA4 data for digital paths, call tracking for phone-led sectors, branded search demand and direct traffic as proxies for brand and broadcast effect, and where budgets justify it, geographic holdout testing — the only approach that answers incrementality with real confidence. Where a number is an estimate, the report says so.

Dashboards executives actually read

We build Power BI reporting that combines marketing, commerce and operational data into a view a board can absorb in two minutes without an analyst present. That means a small number of decision-relevant measures, trend over time, comparison against target, and plain-language commentary — not a wall of charts that flatters the effort involved in building it.

For clients like Wafex, Tile Empire and our rates and collections work, this layer is where scattered systems finally reconcile: one place where inventory, sales, campaign performance and operational throughput can be read together.

AI-assisted forecasting and its limits

We use AI to help with pattern detection, anomaly alerting, forecasting and drafting the narrative around a dataset. It is genuinely useful for spotting that something changed before a monthly meeting reveals it.

We do not let it invent explanations. Correlation surfaced by a model is a hypothesis for a human to test, not a finding to put in front of a board. The value of a measurement partner is judgement about what a number means and what to do next — that part remains ours, and we would not pretend otherwise.