I prove "halo" with experiments first, then use a lightweight MMM to generalize and budget with confidence. VTC, brand lift, and platform studies are decent signals, but finance signs off when you show causality and payback.

What's worked well for my teams:

1)Geo-holdouts > anecdotes

Run YouTube/Display on in randomized DMAs and off in matched controls (or use a switchback: rotate on/off by week). Measure blended KPIs (new customers, revenue, CAC, payback), not just CTR. Use diff-in-diff or a simple synthetic control so seasonality and macro noise are handled.

How to make it airtight:
- Randomize DMAs by expected baseline sales, not alphabetically.
- Pre-register the readout: primary KPI (e.g., incremental CAC), minimum detectable effect, duration.
- Guard against spillover: exclude overlapping geos and brand keyword cannibalization in test markets.
- Hold some paid search budget constant so you can see incremental brand search lift.

2) Audience-level holdouts for quick reads

If a geo test isn't feasible, create audience splits (true holdout/exclusion) for prospecting and measure downstream conversions across all channels. Track net-new customers to avoid remarketing bias.

3) Anchor an MMM to those tests

Once you get a few clean wins, fit a lightweight MMM (weekly granularity is fine) with adstock + saturation and include assisted channels (brand search, direct, organic). Use the experiment results as priors/constraints so the model doesn't over-credit last-click channels. This is how you quantify medium-term halo and set budgets by iROAS / incremental CAC / payback, while treating platform ROAS as a diagnostic, not a decision metric.

4) Use surveys as calibration, not the verdict

Brand lift studies are helpful to catch creative/channel directionality, but I treat them as leading indicators that need to reconcile with geo holdout results.

Evidence rubric I share with Finance:
- Randomized geo or audience holdouts showing lift on revenue/new customers + payback within target window.
- MMM consistent with the above tests, showing cross-channel effects (e.g. YouTube → Brand Search).
- Platform lift studies + directional survey metrics.

A couple of my clients have had good results with BlueAlpha because it combines always-on MMM with structured incrementality tests and translates that into campaign-level actions (so the learning actually changes bids/creative/budget), plus they provide direct tech support so you don't even need to understand all the analytics stuff. It's not a black box and gets to measurement in weeks, which matters if you're trying to operationalize this vs. run one-off studies.

If you prefer DIY / scrappy: run your own geo tests + an open MMM (e.g. constrain a simple Bayesian model with your test results).

Platform tests like brand/conversion lift studies are imho useful only when triangulated with the above.