Hire the firm that can connect a real budget decision to a reproducible estimate of incremental impact and explain what cannot yet be identified. Ask for a named business outcome, a counterfactual design, model diagnostics, quantified uncertainty and an operating owner. An AI-generated ROAS dashboard or a polished MMM fit is not, on its own, proof that ads caused sales.
Match the method to the decision
- Incrementality experiment: isolate a media intervention against a defensible control to test a channel or campaign when power, contamination and execution permit.
- Marketing mix model (MMM): use aggregate media, outcome and control series for cross-channel budget scenarios; examine assumptions, sensitivity and calibration.
- Journey attribution: useful for tactical optimization of observable touchpoints, but assigned credit is not automatically causal lift.
- Triangulation: record why methods disagree and which evidence changes the investment choice, instead of averaging incompatible ROI numbers.
Google Meridian documents MMM workflows and model health; its GeoX component supports geographic experiments and calibration. Meta Robyn describes another open implementation with external calibration. These are examples to question a provider about methods, not endorsements or a universal technology shortlist. A measurement strategy should remain intelligible if the software changes.
Measurement Partner Proof-6: six procurement gates
- 1. Decision and estimand — the exact budget question, population, outcome, time horizon and incremental effect to estimate.
- 2. Data contract — spend, revenue or margin, promotions, seasonality, price, distribution and geography, with definitions, owners and missingness.
- 3. Causal design — precommitted hypothesis, control, geographic spillover, statistical power, test window and exclusion rules.
- 4. Model health — versioned transformations, priors, collinearity, holdout or backtest, sensitivity and experiment calibration.
- 5. Decisions under uncertainty — intervals, scenarios, extrapolation bounds and a rule for acting versus collecting more evidence.
- 6. Ownership and repeatability — access to data, code, configurations and documentation; audit rights, refresh cost and handover.
Score a gate as absent, asserted, demonstrated on a controlled sample or independently reproduced by the buyer. Do not let a favorable composite score conceal an invalid control group, inaccessible data or unsupported causal language. The Proof-6 is a MAKINAI editorial buying tool, not an external certification or empirical benchmark.
Run the same paid evidence challenge for finalists
Provide an approved, minimized aggregate dataset, hold back a validation period, and pose one decision such as shifting spend between two channels subject to a margin constraint. Require a data audit, a defensible geo-holdout design if feasible, a proposed MMM specification or a reason to defer it, and a memo showing effect ranges and limitations. Have finalists explain disagreement among platform attribution, MMM and experiment outcomes without silently selecting the highest return.
Evaluate the work required to prepare data, the decision actually supported, failure modes, elapsed time, refresh cost and your team's ability to reproduce a result. A competent firm may conclude that spend variation, sample size or outcome coverage is inadequate and recommend measurement setup before claiming precise ROI. GAO's evaluation-design guidance is a useful reminder to match evidence strength to the causal question; public-sector guidance is not an endorsement of a private vendor.
Contract for decisions and continued operation
Specify source and transformation ownership, a data dictionary, experiment registry, documented model versions, diagnostic reviews, scheduled recalibration, access controls and the threshold for revisiting a budget recommendation. Retain a right to inspect code and transfer operations. Check privacy and sector-specific requirements with counsel, particularly when matching customer-level data; aggregate MMM does not automatically eliminate every data obligation.
Related buyer decisions
Use https://makinai.co/insights/en/evaluate-ai-consulting-roi-business-case-before-hiring for return claims, https://makinai.co/insights/en/assess-data-readiness-before-hiring-ai-company for inputs, and https://makinai.co/insights/en/how-to-evaluate-ai-consulting-proposals-scorecard to compare bids. MAKINAI can help define the investment question and a comparable evidence challenge before partner selection: https://makinai.co/services/en/digital-marketing-media-performance-growth-agency.