How to Achieve Evidence-Based Advertising with Models Plus Experiments

By Dr. Ramla Jarrar, President and Co-Founder of MASS Analytics
and Rick Bruner, CEO and founder of Central Control, Inc.

Marketing Mix Models remains one of the most valuable tools in the modern marketing organization. MMM provides a holistic view of how channels work together, enables scenario planning, and gives executives a durable framework for managing investment decisions over time.

Experiments complement this strength by answering a different question: not how the mix fits together, but what is truly causal. Randomized controlled trials (RCTs) provide direct measurement of incremental lift and serve as the most rigorous form of evidence in advertising effectiveness.

Done well, MPE creates a continuous learning system: Model → Experiment → Model → Experiment

Below are practical best practices for implementing this approach successfully.

1. Treat MMM as Strategic Intelligence, Experiments as Causal Ground Truth

Marketing Mix Models provide breadth: a holistic view of the full marketing portfolio, the interactions across channels, and the ability to plan future investment with confidence.

Experiments provide depth: causal truth about incrementality, resolving the limits of correlation-based inference.

The purpose of MPE is to combine strengths of both strategic modeling with causal validation into a unified system:

2. Prioritize the Biggest Uncertainties First

Not every channel needs immediate testing.
Start where the stakes are highest:

A useful starting point is the concept of Predictive Incrementality by Experimentation (PIE): research has shown that even a small number of well-designed experiments can materially improve the accuracy of lift projections and model calibration.

But the objective of MPE is not a one-time validation or small-scale insights. The goal is a regular practice of randomized experiments, accumulating a body of results across channels, formats, markets, and time.

Over time, this experimental benchmark becomes the most reliable foundation for MMM priors, coefficient calibration, and confident investment decisions.

3. Use True Randomized Controlled Trials

Causal credibility requires random assignment. Randomized controlled trials remain the clearest way to isolate the incremental impact of advertising, because randomization controls for both observed and unobserved confounding factors.

Quasi-experimental methods, including synthetic controls, matched markets, and exposed versus unexposed comparisons, depend heavily on modeling assumptions and are more vulnerable to bias and overfitting.

These approaches can sometimes be informative when experiments are genuinely impractical, but they do not provide the same standard of causal evidence.

Randomized controlled trials should be the default standard in advertising measurement, not an occasional luxury. In most commercial settings, true experiments are operationally achievable if organizations are willing to design them carefully and commit to rigorous evidence rather than rely on weaker approximations. The discipline of experimental design is the difference between measurement and inference.

4. Favor Geographic Experiments Over User-Level Tests

Geo experiments have become the most practical experimental standard because they are:

Geo testing is operationally straightforward because most major media channels—from broadcast and outdoor to search, social, and programmatic—can target and deliver campaigns at the level of large geographic regions such as DMAs, metro areas, or clustered postal geographies. This makes geographic randomization feasible across nearly the entire media mix.

Geo experiments are also less dependent on user-level identity because many advertisers’ outcome systems already contain geographic markers. CRM platforms, retailer sales data, and third-party purchase panels frequently capture transactions with ZIP codes or similar regional identifiers. Researchers can therefore determine whether observed buyers were in test or control geographies during the measurement window, without requiring device graphs, clean rooms, or fragile user-level matching.

By contrast, user-level experimentation increasingly suffers from the same match-rate degradation and cross-device fragmentation that undermined multitouch attribution.

5. Run Experiments at Sufficient Scale

Small tests are fragile.
Whenever possible, avoid sampling only a few markets. Small-scale geo tests reduce statistical power, increase the risk of idiosyncratic bias, and limit generalizability to the true scale of the campaign.

A test that makes St. Louis look like Pittsburgh in a model may produce a plausible comparison on paper, but it does not necessarily reflect the conditions of Los Angeles, Anchorage, or the full diversity of markets where a national campaign actually runs.

Best practice is large-scale randomization:

Scale strengthens both internal validity and external relevance.

6. Design Experiments to Match MMM Needs

Experiments should be designed to produce outputs that are directly usable inside the model, rather than standalone lift studies that cannot be incorporated into ongoing measurement.

Experiments should yield:

MMM can also inform experiment design by grounding key parameters in historical context:

7. Calibrate MMM Coefficients with Experimental Evidence

The most valuable use of experiments is calibration: anchoring modeled estimates in causal evidence and reducing the uncertainty that inevitably arises from observational data alone.

Calibration allows advertisers to:

Over time, advertisers build a library of causal benchmarks across channels and conditions, making MMM not only more accurate, but more defensible to executives and finance stakeholders.

8. Build a Meta-Analytic Benchmark Over Time

The only evidence stronger than a randomized experiment is a body of many experiments. A single test is informative, but a sustained practice of experimentation produces durable knowledge that generalizes across campaigns and market conditions.

Over time, advertisers can accumulate experimental results across many dimensions:

This growing benchmark becomes the most reliable foundation for answering the question: “What really works to drive sales?”

That is the promise of evidence-based advertising.

Conclusion: Make Measurement Scientific Again

MPE is not a buzzword. It is the natural next step in the evolution of advertising ROI:

Models alone are not enough.
Experiments alone are not scalable.
But together, they form the pinnacle of modern marketing measurement.