Correlation is cheap. Causation is expensive.
Last-click attribution feels safe until the CFO asks for proof. We give you the evidence that survives any boardroom challenge.
You shift budget based on noise
Last-click models reward the final touchpoint, not the channel that actually drove the sale. You keep funding fiction.
Experiments run without statistical rigor
Peeking at results early, tiny sample sizes, no guardrails. You declare winners that are just random luck.
MMM reports sit on a shelf
Beautiful saturation curves that never translate into real budget reallocation. Insights without action are expensive curiosity.
Causal measurement that replaces guesswork
Every test follows peer-reviewed frameworks refined across enterprise media budgets.
Geo-Lift and DiD experiments
Holdout markets or audiences. True incremental lift measured without relying on platform black boxes.
Marketing Mix Modeling
Bayesian MMM with saturation curves, iROAS by channel, and clear budget reallocation recommendations.
Rigorous A/B testing
OEC, guardrails, sequential testing. No peeking. No early calls. Statistical confidence you can defend.
CLV and RFM modeling
Predictive customer value scoring in BigQuery, ready for activation across your ad platforms.
Geo-Lift, DiD, synthetic control.
We design and run controlled experiments that isolate true causal lift from organic baseline.
What you get:
- Geo-Lift holdout markets
- Difference-in-differences analysis
- Synthetic control for single-market brands
- Statistical significance and practical significance reporting
Bayesian MMM with actionable outputs.
Channel-level contribution, saturation curves, and clear budget reallocation scenarios. Built in BigQuery for transparency.
What you get:
- Bayesian MMM with full uncertainty intervals
- Saturation curves per channel
- iROAS and marginal ROAS by spend level
- Scenario planning and optimization recommendations
Rigorous, defensible experimentation.
Platform-agnostic A/B testing methodology with proper sample size calculation, guardrails, and sequential analysis.
What you get:
- OEC definition and guardrail setup
- Sequential testing to stop early when safe
- Power analysis and sample size calculation
- Integration with AB Tasty, Kameleoon, Optimizely or custom setups
Customer lifetime value ready for activation.
RFM segmentation and predictive CLV models in BigQuery. Score every customer and feed the models back into your ad platforms.
What you get:
- RFM segmentation in dbt
- BQML predictive CLV models
- Customer scoring for lookalike audiences
- Activation playbooks for ad platforms
From hypothesis to causal proof.
Baseline assessment
Measurement maturity audit. What can you test today? What needs infrastructure? What needs data?
Experiment design
Hypothesis, sample size, duration, OEC, guardrails. Everything documented before execution begins.
Execution
Proper holdout, randomization, and monitoring. No peeking. No early calls without sequential testing.
Causal analysis
Confidence intervals, practical significance, counterfactual estimation. We answer did it cause the outcome, not was it there.
Integration
Learnings fed into media strategy, budget allocation, and product roadmap. Experiments without action are expensive curiosity.
“Geo-Lift, DiD, and MMM deployed across enterprise media budgets. Causal proof that replaced last-click guesswork.”
Evidence, not opinions
Concrete deliverables your team can actually use. No slide decks, only tested artifacts.
Experiment Design
Hypothesis, sample size, duration, and success criteria. Documented before any code is written.
Statistical Analysis
Bayesian or frequentist analysis with confidence intervals and practical significance assessment.
Incrementality Report
Causal impact measurement with counterfactual estimation and confidence intervals.
MMM Model
Channel-level iROAS with saturation curves and budget reallocation recommendations.
CLV Model
Predictive customer value scoring in BigQuery, ready for activation.
Measurement Roadmap
Quarterly experimentation calendar with prioritized hypotheses and expected business impact.
19 methodology guides. Peer-reviewed frameworks.
Kohavi for experimentation. Cunningham for causal inference. Gelman for Bayesian methods. Every methodology traces to a published source.
Is this the right engagement for you?
Best fit
- Brands spending over 100K per month on media who need causal ROI proof
- Companies with executive pressure to justify marketing spend
- Teams running experiments without statistical rigor
- Organizations considering MMM or incrementality for the first time
Not ideal for
- Businesses with very low traffic (insufficient sample sizes)
- Companies not yet collecting reliable tracking data
Common questions
What is the difference between incrementality and attribution?
Attribution assigns credit to touchpoints. Incrementality measures what would have happened without the marketing, the true causal lift. They answer different questions.
How much traffic do we need for A/B testing?
Depends on your baseline conversion rate and minimum detectable effect. We calculate exact sample size before any test launches.
Can you integrate with our existing testing platform?
Yes. AB Tasty, Kameleoon, Optimizely, and custom setups. The methodology is platform-agnostic.
Certified Analytics Engineer
DataBird 2025
GA4 Certified
GTM Server-Side Specialist
BigQuery Certified
Google Cloud
Enterprise Track Record
Carrefour. Airbus. Club Med. Ubisoft
Luxury and Premium Clients
Sezane. ByRedo. Valrhona
50 plus Client Engagements
Artefact. Sleekery. MadMetrics
You spent 100K on that campaign. Did it actually cause anything?
We answer the question attribution cannot. Book a measurement consultation.