C-04Data & Analytics
Marketing analytics and attribution services built warehouse-first
The short answerquotable
Marketing analytics and attribution services at AiS LABZ put every source of business data into one warehouse you own, then build reporting on top of it. We install server-side tracking that survives ad blockers and privacy changes, model attribution in GA4 and the warehouse, reconcile marketing figures against the order system, and publish executive dashboards with anomaly alerts. The result is a single set of numbers that marketing, product and finance can argue from, and a governed data layer that every later AI project sits on.
Why do marketing, product and finance report different numbers?
Three tools, three definitions, three different truths
Each team reads a different source. Ad platforms count a conversion when the pixel fires, analytics tools lose blocked visitors, finance sees only paid, unrefunded orders. Fix it structurally: put events, orders and spend in one place and define every metric once.
- Ad platforms attribute to the last click they can see
- Browser tags are blocked for a meaningful share of visitors
- Refunds and cancellations rarely flow back into marketing reports
- Each tool applies its own session and conversion rules
What is warehouse-first measurement and why does it matter?
Own the data before any tool reads it
Land raw events, orders, spend and CRM records in a warehouse you own. Define metrics once, in code, and let dashboards, GA4 and ad platforms read the same definitions. Change vendors without losing history. Turn every report dispute into a query rather than a meeting.
- Raw tables for events, orders, spend and CRM
- Metrics defined once, in code, read by every tool
- A data warehouse for marketing costs less than one misallocated quarter
- Managed cloud warehouse, scheduled loads, a few hundred lines of modelling
The LABZ protocol· for data & analytics
Diagnose. Model. Build. Compound.
The same four phases run every engagement, in Waltham, London or Riyadh. Here is what each one means for this discipline.
Diagnose
Audit tags, events, ad platform data and the order system. Size the gap between reported and actual.
Model
Define the metric layer, event schema, attribution approach and dashboard questions. Agree them with finance before building.
Build
Ship server-side tracking, warehouse pipelines, reconciliation, dashboards and alerts in two-week increments, each with a named owner.
Compound
Keep pipelines healthy, tune attribution against incrementality tests and extend the layer to new sources on subscription.
How does server-side tracking survive ad blockers and privacy changes?
Collect on your domain. Blockers see a first-party request.
Browser tags depend on scripts loading and third-party cookies surviving; both fail more each year. Move collection to a server you control, on your own domain, and forward events to GA4, ad platforms and the warehouse from there. Conversion counts stop drifting downward as browsers tighten.
- First-party collection endpoint on your own domain
- One event schema shared by GA4, ad platforms and warehouse
- Consent state carried with every event, honoured not bypassed
- Server-side enrichment from your order and CRM systems
How do you model attribution in GA4 without overclaiming?
Use GA4 for paths. Trust the warehouse for money.
GA4's data-driven model only sees touchpoints it can observe; it misses offline sales, refunds and channels that run without a click. Keep GA4 for path analysis. Rebuild attribution in the warehouse where orders and margin exist, start with position-based or time-decay, and test against incrementality holdouts before moving budget.
- Write the model's assumptions down
- Match the output to finance before anyone trusts it
- Tie attribution to contribution margin, not platform-reported revenue
- Give paid media bid guardrails a margin figure finance recognises
What goes into an executive dashboard that people actually use?
Show five questions. Decide something. Delete the rest.
Dashboards die when they show everything and decide nothing. Start with the five or six questions leadership asks every week and build one view per question. Give each metric an owner, a target and a definition traced to the warehouse. Keep logic in the warehouse, not the dashboard.
- One view per recurring leadership question
- Every metric traced to a warehouse definition
- Targets and owners visible on the chart
- Contribution margin by channel, computed the way finance computes it
What you receive
Deliverables, not decks
Every item below is a contractual deliverable with a named owner and a date. Documentation is part of the work, not an extra.
Measurement audit
Where tracked figures diverge from orders, why, and what to fix first.
Server-side tracking setup
First-party collection on your domain feeding GA4, ad platforms and warehouse.
Marketing data warehouse
Raw and modelled tables for events, orders, spend and CRM, owned by you.
Attribution model
GA4 configuration plus a warehouse model reconciled to orders, checked against holdouts.
Executive dashboards
One view per leadership question, every metric traced, targets and owners shown.
Alerts and reconciliation
Scheduled anomaly detection and order reconciliation, with a runbook per alert.
How do anomaly alerts and reconciliation keep the numbers honest?
Tracking breaks quietly. Find it the same day.
A tag manager change, checkout redesign or consent banner update can drop a third of conversions unnoticed until the quarterly review. Watch each critical metric against its expected range and alert when it moves outside. Reconcile tracked conversions against the order system on a schedule, by channel and day.
- A small, stable gap is normal and can be modelled
- A growing gap is a defect
- Every alert ships with a runbook: meaning, usual cause, owner
- A red number becomes a fifteen-minute task, not an investigation
How does this measurement layer prepare us for AI?
The data layer decides whether the AI project survives
Every AI project sits on this layer. Lead scoring needs clean records of who converted and why. An LLM assistant needs a governed source with permissions and an access log. Automation acting on a wrong number is worse than a wrong number in a slide. Build the foundation first.
- Governed tables with permissions and access logs
- Reconciled outcomes for training and evaluating models
- Reusable metric definitions for assistants and copilots
- Anomaly alerts that catch automation acting on bad data
Further reading from the AiS LABZ blog
Is this for you
A good fit and a bad one
Built for
- Marketing, product and finance numbers disagree every month
- Paid media spend without reconciled contribution margin by channel
- AI projects planned that need governed, trustworthy data first
Not the right service when
- Sites with no transactions or leads to measure yet
- Teams wanting a dashboard without fixing the tracking underneath
Common questions
Questions we get asked
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.
Free 15-minute diagnostic
Find the constraint first.
Fifteen minutes with a senior on the Data & Analytics pod. We tell you what we would look at, what it would cost and whether it is worth doing — including when the answer is no.