Marketing analytics services: do marketing, sales and finance agree on where last month's growth came from?

Almost always a tracking and attribution problem rather than a missing sale. Browser-side tags are blocked or dropped for a meaningful share of visitors, conversions get attributed to the last click that a platform can see, and refunds or cancellations never make it back into the reporting. Server-side tracking plus a reconciliation against the order system closes most of the gap, and makes the remainder explainable.

It means your data lands in one warehouse you own, and every report, dashboard and ad platform reads from that single source rather than each keeping its own version. The advantage is that finance, marketing and product stop bringing three different numbers to the same meeting, and you can change tools later without losing history. It is also the prerequisite for any AI model that has to reason over your business.

You need governed, reachable data — a full warehouse is one way to get it, not the only one. What matters is that the model can reach accurate records with permissions attached and a log of what it read. For a smaller business that can be a well-instrumented stack plus a clean export. The failure mode is the same either way: automation built on numbers nobody trusts gets switched off.

A scoped project, then an optional subscription. Diagnostic and modelling phases are fixed-price, one to two weeks each. The build runs in two-week increments against a schedule agreed up front, so the total is known before it starts. After a three-month setup, maintenance, alert handling and model tuning move to a month-to-month subscription. Warehouse and tooling costs are paid directly by you, never marked up.

The first reconciled view arrives within the first two build increments, roughly four weeks after modelling ends. Server-side tracking and core warehouse tables come first because everything depends on them. Dashboards and attribution follow once data has been checked against orders for a couple of weeks. Anomaly alerts switch on as each metric stabilises. Expect the full layer within the three-month setup.

Your team owns the result. All pipeline code, metric definitions and dashboard logic live in your accounts and repositories, documented. Where you have engineers, we pair with them and hand over as we go. Where you have an analyst without engineering support, we build the layer they will query and train them on it. The subscription should become optional, not necessary.

Spending against a model that flatters one channel. Last-click overpays brand search and retargeting because they sit closest to the sale, while prospecting channels that started the journey look unprofitable and get cut. Keep the model simple, reconcile it to real orders and margin, and test it against incrementality holdouts. A tested model may move budget. An untested one is a hypothesis.