AdTech companies are measurement businesses that often cannot measure their own growth – blurry attribution, double-counted conversions, and customer-facing reporting that does not prove incrementality. Fixing the measurement layer is what makes every other GTM decision possible, because you cannot optimize a motion you cannot read.
The Problem
Your own GTM attribution is broken in the exact way you warn customers about
AdTech companies preach clean attribution and incrementality, then run their growth on last-click platform numbers, three tools each claiming the same conversion, and a CAC nobody can reconcile across channels. Every growth decision sits on top of measurement the company itself would call unreliable if a customer used it. The result is spend allocated by gut, channels scaled on flattering misattribution, and a leadership team that cannot answer basic questions about what is actually working.
Signal loss has quietly degraded every number on the dashboard
Cookie deprecation, iOS privacy changes, walled-garden data restrictions, and shrinking attribution windows have eroded the data underneath GTM reporting, often without anyone updating how the numbers are read. Teams keep optimizing to metrics that no longer mean what they used to, scaling campaigns on signal that has decayed into noise. Without a measurement approach built for a privacy-first environment – modeled conversions, incrementality testing, first-party data design – the company is flying on instruments that have silently gone wrong.
Customer-facing reporting cannot prove the value the product delivers
For an AdTech company, the reporting you hand customers is part of the product, and weak reporting actively costs renewals. When dashboards show activity metrics instead of business outcomes, or cannot demonstrate incrementality, customers cannot justify the spend internally and churn at renewal even when the product worked. Sales also loses deals it should win because the proof-of-value story is unprovable. Reporting that fails to connect your product to the customer's revenue is a retention and acquisition liability, not just a UX problem.
Data lives in silos, so nobody can see the full GTM picture
Ad platform data, CRM, product usage, billing, and customer-facing analytics typically live in disconnected systems with no shared definitions, so simple questions take days and arrive with three conflicting answers. Marketing, sales, and finance each run their own numbers and argue about whose are right instead of making decisions. Without a unified data layer and agreed definitions, the company cannot do segmented CAC, cohort retention, or honest channel attribution, and every planning cycle restarts the same fights over whose dashboard to believe.
How We Help
We start by auditing how you actually measure your own business, which for a measurement company is the most revealing and most uncomfortable assessment we run. In the first 30 days we trace every key GTM number to its source – attribution logic, conversion definitions, data freshness, and where the silos break – and we usually find that the company's internal measurement is exactly the kind it warns customers against. We fix what you can trust before we build anything on top of it.
Strategy designs a measurement layer fit for a privacy-first world. We build attribution and incrementality methods that survive signal loss – holdouts, geo-tests, and modeled conversions rather than naive last-click – and we design the first-party data foundation that the post-cookie environment requires. We define shared metric definitions across marketing, sales, and finance so the company finally argues about decisions instead of about whose number is right. This is the same measurement rigor your product is supposed to sell, applied to your own GTM.
Execution unifies the data and builds the reporting that matters. We connect ad platform data, CRM, product usage, and billing into a layer that answers segmented CAC, cohort retention, and honest channel attribution. On the customer-facing side, we rebuild reporting to prove business outcomes and incrementality, because for an AdTech company that reporting is a retention and sales asset, not a back-office report. We tie GTM measurement and product analytics together so the same truths flow internally and to customers.
The fractional model gives you a senior analytics operator plus the data engineering and analyst capacity to build the layer, without hiring a full-time head of analytics, a data engineer, and an analyst before you know what to build. We embed in your marketing, revenue, and product orgs and stand up the measurement system on your cadence, then leave you a layer your team can run.
Measurement of our own work is, fittingly, the point. We track whether GTM decisions can finally be made on trusted numbers, whether CAC and retention are legible by segment, and whether customer-facing reporting moves renewal and win-rate conversations. The goal is a company that measures its own growth as rigorously as it asks customers to measure theirs – and a reporting product that proves value instead of just displaying activity.
What we deliver
- GTM measurement audit tracing every key number to its source and exposing broken attribution
- Privacy-first attribution and incrementality framework using holdouts, geo-tests, and modeled conversions
- Unified data layer connecting ad platform, CRM, product usage, and billing with shared definitions
- Segmented CAC, cohort retention, and honest channel-attribution reporting
- Rebuilt customer-facing reporting that proves business outcomes and incrementality
- First-party data foundation designed for the post-cookie environment
AdTech companies are measurement businesses that usually cannot measure their own growth. Fixing the internal measurement layer is what makes every other GTM decision possible, because optimizing creative, acquisition, or spend on broken attribution just means scaling confidently in the wrong direction.
Our Methodology
Our data and analytics build for AdTech runs as a 90-day measurement installation. Phase one audits how the company measures its own business – tracing every key GTM number to its source, exposing broken attribution, double-counting, and silos. For a measurement company this audit is the uncomfortable but essential starting point, because nothing built on bad numbers can be trusted.
Phase two designs the measurement layer for a privacy-first world: attribution and incrementality methods that survive signal loss, a first-party data foundation, and shared metric definitions across marketing, sales, and finance so the organization stops arguing about whose number is right. This is the same rigor the product is meant to sell, applied internally.
Phase three builds the unified data layer and rebuilds customer-facing reporting. Ad platform data, CRM, product usage, and billing connect into a layer that answers segmented CAC and cohort retention, while customer-facing dashboards get rebuilt to prove outcomes and incrementality. Unlike a BI agency that ships dashboards on top of broken data, we fix the measurement first and treat customer reporting as a retention and sales asset.
How We Work
Initial engagements run 3 to 6 months because building a trustworthy measurement layer requires the audit, the attribution redesign, data unification, and rebuilding reporting both internally and for customers. The first 30 days are the GTM measurement audit with marketing, revenue, finance, and product leadership. Days 31 to 60 design the privacy-first attribution framework, shared definitions, and data architecture. Days 61 to 90 build the unified layer and rebuild internal and customer-facing reporting.
Our team includes an analytics operator who owns the measurement strategy, a data engineer who builds the unified layer, and an analyst who designs the reporting and incrementality tests. From your side we need access to ad accounts, CRM, product, and billing data, a single owner for metric definitions, and product input on customer-facing reporting. We handle the audit, architecture, attribution design, and reporting build.
Weekly working sessions track the build and validate numbers as systems connect. Monthly business reviews tie the measurement layer to GTM decision quality, segmented CAC and retention, and customer-facing reporting impact. Most AdTech companies can trust their core GTM numbers within 60 days and have rebuilt customer-facing reporting within 90, with the layer continuing to pay off every planning cycle thereafter.
- GTM decisions made on trusted, reconciled numbers instead of gut feel
- Segmented CAC and cohort retention finally legible across the business
- Customer-facing reporting that proves outcomes and supports renewals
- A privacy-first measurement foundation that survives signal loss
If your adtech company needs data, reporting & analytics leadership, we should talk.
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Frequently asked questions
How much does data and analytics work cost for AdTech companies?
Most AdTech data and analytics engagements run between $20K and $50K per month depending on data complexity, how many systems need unifying, and whether customer-facing reporting is in scope. That is less than hiring a full-time head of analytics, a data engineer, and an analyst before you know what the layer should look like.
How long before we see results from a data and analytics engagement?
The audit surfaces what you can and cannot trust within the first 30 days, which is often the most valuable early deliverable. You can usually trust your core GTM numbers within 60 days as attribution is redesigned and the data layer connects.
How does the analytics team integrate with our data and product staff?
We embed across marketing, revenue, and product because GTM measurement and customer-facing reporting both depend on connecting those systems. We need access to ad accounts, CRM, product, and billing data, plus a single owner for metric definitions. Your team keeps ownership of the data and the product while we design the measurement layer, build the unified data foundation, and rebuild the reporting.
What makes Winston Francois different from a traditional analytics or BI agency?
BI agencies ship dashboards on top of whatever data exists, including broken attribution, and they do not understand AdTech-specific signal loss. We fix the measurement read first, design attribution and incrementality that survive cookie deprecation, and treat customer-facing reporting as a retention and sales asset. We are operators applying the same measurement rigor your product sells to your own GTM and to your customers' experience of it.
How do you measure ROI from a data and analytics engagement?
We measure whether GTM decisions can finally be made on trusted numbers, whether CAC and retention are legible by segment, and whether rebuilt customer-facing reporting moves renewal and win-rate conversations. The headline ROI is better capital allocation from honest attribution plus retention lift from reporting that proves value. Most AdTech companies see decision-quality ROI within 60 days and renewal-conversation impact within a quarter of the reporting rebuild.
What type of AdTech company is the right fit for this service?
AdTech companies whose GTM runs on attribution they do not fully trust, whose data lives in silos, or whose customer-facing reporting cannot prove incrementality. Companies between $5M and $100M ARR scaling spend across multiple channels or relying on reporting as part of the product see the strongest fit. The first step is a GTM measurement audit that traces your key numbers to their source and exposes what is actually reliable.
