Service
Marketing data analytics
We build the tracking, data pipelines and reporting that agencies and brands use to make marketing decisions. AI agents pull the data and flag changes, and people decide what the numbers mean.
The work behind the service.
Most marketing data problems start with definitions. A qualified lead has to mean the same thing in the report, in the CRM and in the conversation with sales. We agree on those definitions first, because a cleaner dashboard won't settle an argument about what a lead is.
Then we check that the numbers reconcile. We compare platform conversions against CRM records, spend against invoices, and each metric against its definition. If a figure can't be regenerated from its inputs, it stays out of the main report until we can explain it.
Agents are useful for the repetitive part of this work. They can pull data from each platform, flag changes against the prior period and draft a first summary. The person who owns the account still decides what matters and what to do next.
What's included.
- tracking and measurement audit
- GA4 and GTM setup
- attribution
- data pipelines
- automated reporting
- live dashboards
- audience modeling from your own customer data
When to bring in a marketing analytics agency.
Outside help makes sense when your teams argue about the numbers, when leadership stops trusting the reports or when someone spends days each month copying data into slides. It also helps when you're spending across several platforms and each one claims credit for the same sale.
You may not need a big project. If the issue is one broken conversion or a report nobody reads, we'd fix that first. Pipelines and dashboards only become worthwhile once the underlying numbers can be trusted.
Attribution: which channels actually drive sales.
Every ad platform reports results from its own point of view, so adding them up usually gives you more conversions than you actually had. Attribution is the work of getting closer to what each channel really contributed.
No single model gets it completely right. Platform reports, GA4, CRM records and tests can each answer different questions. We compare them, explain why they differ and decide which one to use for each decision. Where the budget and volume allow, tests that pause or hold out a channel can give a stronger answer than any model.
Data pipelines and one source of truth.
Most marketing teams have data spread across ad platforms, analytics, a CRM and spreadsheets. A data pipeline pulls it into one place on a schedule, using the same names and definitions, so people stop rebuilding the same numbers by hand.
We keep the design as simple as the job allows. A small team may only need a well-built connected sheet, while a larger one may need a data warehouse. We choose based on your volume, your existing tools and who'll maintain it.
Dashboards and automated reporting.
A dashboard should help someone make a decision. We build one main view around a short list of questions, then keep the detail a click away. Automated reports can then build and send themselves on a daily, weekly or monthly schedule, without manual exports.
Automation can't tell you why something happened, though. That's why we pair the numbers with a short written note from the person who owns the account. If the reader can't restate the decision after thirty seconds, the report still needs work.
Lead quality and customer data.
The most useful marketing data often sits in your CRM. It shows which leads became customers, which channels brought them and what they were worth. Feeding that information back to your reports, and to ad platforms where consent allows, moves the focus from form fills to real business.
Your own customer data can also help find similar people and build better audiences. We agree on consent, data handling and access before connecting anything.
How it works.
- 01
Agree on the decisions
We start with the decisions the data has to support. We list the people who will use it and the questions they need answered. We then agree on what counts as a lead, a sale and a qualified result.
- 02
Audit what you have
We review your tracking, platforms, reports and available CRM records. We look for duplicates, gaps and metrics that have no clear definition. You get a short list of what to fix first.
- 03
Fix the signal
We correct the events and connections that affect your decisions. When tracking needs a larger rebuild, we scope that separately. We test real actions and check that they arrive where they should.
- 04
Connect the data
We build the pipelines needed to bring your platforms into one source. Each number can be traced back to where it came from. Your team can see what the pipeline pulled and when.
- 05
Build one operating view
We build one main view with the questions that matter most: what you spent against plan, what it produced, what each qualified result cost and what is being tested. The detail stays one layer down for anyone who wants it. A short written note says what happened and what we will watch next.
Proof from the work.
For the Journées de la persévérance scolaire, a draft media plan overstated delivery by 400,000 impressions because a CPM changed and the impression row never followed. We audited the plan line by line in code, so the whole estimate now regenerates from a single script.
For Dans la rue, a draft results model had digital delivering between 170% and 219% of the entire $100,000 campaign goal. We rebuilt it from scratch and found four independent defects.
- Dans la rue: a results model rebuilt from scratch, four defects found
- Read how we rebuild a number from its inputs
How we price it.
We bill hourly and set the scope in hours. Strategy and advisory are CAD $175/h. Media management and execution are CAD $110/h. We confirm the applicable engineering rate in the scope for technical builds. Hosting, software subscriptions and advertising budgets are identified separately where relevant.
You can engage one discipline without buying the others. A reporting fix doesn't require ongoing media work, and we recommend a sequence only when the evidence shows a dependency.
Questions before we start.
What does marketing data analytics include?
It covers how marketing results are tracked, connected and reported so a team can make decisions from them. That can include GA4, conversion tracking, attribution, data pipelines and dashboards. We scope only the parts your decisions depend on.
Do you only work on accounts where you also run the media?
No. You can hire us for the data work alone, whether another team runs your media or you run it yourselves.
Can you work with the dashboard we already have?
We start by checking whether its numbers reconcile and whether people use it to make decisions. If it does the job, we keep it. If it doesn't, we recommend what to change and why.
What do the AI agents do with our data?
Agents handle bounded jobs such as pulling platform data, flagging changes against the prior period and drafting a first summary. People review the results and make the decisions. Access and permissions are agreed before we automate anything.
How much do marketing analytics services cost?
It depends on how many platforms are involved, the state of your tracking and whether you need a one-time audit or ongoing work. We bill by the hour and show you the scope before we start.
Do we need a data warehouse?
Not always. It depends on how much data you have, how many sources you use and how you plan to use it. We'll recommend the simplest setup that does the job.
Which attribution model should we use?
There isn't one right answer for every decision. We compare what your platforms, analytics and CRM show, then choose what fits the question you're trying to answer. Testing can help where the budget allows.
Who owns the ad accounts?
Your ad accounts stay with you or your agency, and we work through delegated access. Removing our access doesn't remove your campaign history.
Related services.
Notes from the work.
Start a conversation.
Tell us what you are trying to move. We will tell you which disciplines are worth engaging, and which are not.