Tyedal helps organisations design and operate sophisticated marketing measurement internally (the operating model, data, methods, technology and skills), then transfers it. The aim is a function you own, not a retainer you renew.
Outsourcing is sometimes the right model. The cost is when the organisation never builds the skill, technology or judgement to run the work itself.
| Outsourced measurement | Owned capability | |
|---|---|---|
| Data ownership | Inputs are shared; the working dataset and feature logic usually stay with the supplier. | You keep the pipelines, definitions and history. Measurement is built on data you already own. |
| Methodology | You receive results from a model you cannot inspect in detail. Challenge is slow. | Assumptions are documented and inspectable. Your team can challenge every line. |
| Internal skills | Capability sits with the people who built the model. When they rotate off, so does the memory. | Skills compound inside the organisation. Training is part of the build, not an afterthought. |
| Technology | A rented platform or a private codebase. Changing supplier means starting again. | Beacon and open methods you operate. The stack is yours whether you keep working with us or not. |
| Speed | A re-run before a budget meeting waits on someone else's sprint. | Your team runs the models on your timeline. Update assumptions in the afternoon, take results into the morning meeting. |
| Knowledge | Handover notes substitute for the people who actually did the work. | Institutional memory stays put. Every cycle makes your team stronger. |
| Long-term cost | You pay again for each refresh of insight built on your own data. | You fund a capability, not a recurring answer. External support is optional after transfer. |
| Improvement | The model updates when the contract says it does. | Continuous improvement is the operating rhythm. Experiments, calibration and documentation are part of the product. |
Tyedal builds capability with the client rather than creating permanent dependency. Pick the depth that matches where you are.
A short diagnostic and strategy engagement. We define the target capability, operating model, data requirements, technology, roadmap and implementation plan, so the organisation knows what “in-house” actually means before it hires or buys.
Best when you need a clear plan, not another model refresh.
Assess your measurement first →A larger engagement to build the capability itself: data architecture, MMM, incrementality, model design, Beacon, training, documentation and knowledge transfer. Your team is in the work from the start.
Best when you are ready to bring measurement in-house and want it done properly.
Talk to a measurement expert →Ongoing expert support for organisations that already operate an internal measurement function. Governance, model review, training, surge capacity, without taking the keys back.
Best when the team exists and needs a specialist centre of excellence around it.
Discuss CoE support →Most in-housing efforts fail because they're run like a one-off project: build it, hand it over, move on. The ones that succeed treat measurement as a living product with a roadmap, a team, and a lifecycle.
The UK team shouldn't be rebuilding what the German team already solved. A shared architecture means consistent methodology and code, while each market retains the flexibility to model their own media landscape, seasonality, and competitive dynamics.
Your MMM says social is undervalued. Your MTA says it's overvalued. Your lift test sits somewhere in between. An in-house product brings these into a single coherent view, triangulated results that tell a story your CFO can actually act on.
If refreshing a model requires a data scientist to spend two weeks cleaning spreadsheets, you don't have a product, you have a prototype. Automated pipelines, scheduled retraining, and built-in validation mean your models stay current without heroic manual effort.
We're advocates for in-housing, but we're not zealots. Both paths have real strengths. The right answer for most organisations is a deliberate combination, not a binary choice.
Our recommendation? Own the core, partner at the edges. Build your measurement engine in-house for the control and compounding benefits. Partner externally for specialist data sources, niche methodology, or surge capacity. Tyedal exists to make the "own" part achievable, even for teams starting from scratch.
We've seen the wreckage of in-housing done badly. These are the failure modes that kill programmes, and the reason you need a partner who's navigated them before.
One brilliant modeller isn't enough. You also need someone who can wrangle the data, someone who can build the front-end, and someone who can translate outputs into decisions. In-housing fails when it's treated as one hire, not a capability.
Teams underestimate this every time. Connecting Google Ads, Meta, TV logs, CRM, and transaction data into a clean, aligned, validated dataset is 70% of the work. If you skip this, everything downstream is unreliable.
A Bayesian MMM in a Jupyter notebook is a prototype, not a product. Without a principled approach to prior elicitation, model validation, and result interpretation, you've just moved the black box from your agency's office to yours.
You've secured budget and headcount, but the next budget cycle is 12 weeks away and stakeholders expect the model to inform it. The gap between "we've started building" and "here are numbers you can act on" is where political support evaporates.
The model launches to applause. Six months later the data pipeline has silently broken, the model hasn't been retrained, and results are stale. Without governance baked in from the start, in-housed models decay faster than outsourced ones.
Your data science team builds a technically excellent model. Marketing ignores it because they weren't involved in the process, don't understand the methodology, and the results challenge their existing beliefs. Adoption is a design problem, not a data problem.
Every failure mode above has a corresponding Tyedal capability. We embed the roles your team is missing, for as long as you need them, then we leave them behind.
Our data engineers build the plumbing that makes everything else possible: API connectors to ad platforms, automated QA that catches anomalies before they hit the model, and clean transformation layers that turn messy marketing data into modelling-ready inputs.
Practitioners with years of hands-on experience in Bayesian MMM, algorithmic MTA, and geo-experimentation. They don't just build models, they establish the methodology: prior specification, validation protocols, and interpretation frameworks that make your measurement credible and defensible.
Two tracks, both practical. For builders: Bayesian inference, causal modelling, and experiment design taught on your actual data. For users: how to read model outputs, challenge results intelligently, and turn measurement into budget decisions, not just presentations.
A model is only as good as its adoption. Our developers build the interactive tools that make measurement usable: scenario planners for media teams, budget simulators for finance, and self-serve dashboards that meet stakeholders where they already work.
Engineers who sit with your team, literally or virtually, during the critical first deployment. They navigate your IT security reviews, connect to your cloud infrastructure, and get a working model into production weeks faster than your team could alone.
PMs who have delivered measurement programmes at scale, and know that half the job is political, not technical. They keep marketing, finance, data engineering, and agencies aligned, manage expectations up and down, and make sure the programme outlives its initial champion.
Not every role needs to be permanent. Access our curated network of vetted measurement professionals, econometricians, data engineers, MTA specialists, on demand. Scale up for a model build, backfill a departing analyst, or bring in a specialist for a single quarter. No recruitment overhead, no long-term commitment.
Start with a maturity assessment, or talk through whether a Blueprint, a full programme, or Beacon is the right next step.