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Li Tan·谭李·Lin.
Essay 2 min read
· 2 min read · Geo Experiments · Marketing Analytics · Incrementality

A Few Thoughts on DMA Tests

Geo tests are one of the most powerful tools for measuring marketing lift. They are also easy to get wrong. Some notes from the field.

DMA tests — experiments at the Designated Market Area level — are one of the most powerful tools for measuring marketing incrementality. They are also easy to screw up. Here are the lessons I paid for.

Why DMA tests

User-level attribution is narrow. It misses a lot: word of mouth, cross-device conversions, brand effects that show up weeks later.

DMA tests catch more of the full picture. You treat whole markets, not cookies, so you pick up:

  • Brand lift that takes weeks to show
  • Cross-device conversions
  • Social and word-of-mouth spillover
  • The full funnel from awareness to purchase

This is why I keep going back to them, even though they are a pain.

Common ways to mess this up

Not enough markets

Power in geo tests comes from the number of geo units, not total users. With 20 DMAs you need a huge effect to see anything.

Rule of thumb: at least 50 markets. Ideally 100. I know this limits the channels you can test. Underpowered tests are worse than no test — you get a non-answer you then defend.

Spillover between markets

People travel. Digital ads ignore borders. If your “holdout” market is leaking treatment, your estimate shrinks toward zero.

What I do: buffer zones, exclude border DMAs, or model the spillover explicitly. I have learned to be paranoid.

Ignoring seasonality

A November test tells you little about February. Marketing effects move with the calendar.

Run long enough to cover a cycle. Or use methods that handle seasonality directly.

Why I prefer synthetic control

Simple treatment-vs-control comparisons work sometimes. But markets are heterogeneous and trends differ by region, so the comparison is usually noisy.

Synthetic control builds a weighted mix of untreated markets that best matches the treated market before the campaign. Then you measure the gap after.

It handles:

  • Different baselines across markets
  • Region-specific trends
  • Noisy outcomes

I get much cleaner reads from synthetic control than from naive DiD for geo tests.

What I would tell a new analyst

  1. More markets beats more users per market.
  2. Design for spillover from day one. Not as an afterthought.
  3. Pre-register the analysis. Stops you from p-hacking yourself later.
  4. Try synthetic control before DiD.
  5. Run a power calculation. Every time.

If you skip the power calc, you are setting yourself up for an inconclusive result you will then have to explain.