Vendor reportCassandra, 2025
Non-brand search returned the highest median ROI of seven Google Ads campaign types, at 5.21x
Channel validation
| Campaign Type | Number of models | Spend (USD) | Median incremental ROI (x) | CI low (x) | CI high (x) |
|---|---|---|---|---|---|
| Search Brand | 85 | $87,000,000 | 4.14 | 1.95 | 7.78 |
| Search Non-Brand | 60 | $95,000,000 | 5.21 | 2.29 | 9.89 |
| Performance Max | 48 | $66,000,000 | 4.64 | 3.11 | 6.41 |
| Shopping | 16 | $68,000,000 | 3.09 | 1.72 | 5.25 |
| Video / YouTube | 14 | $3,600,000 | 2.7 | 1.5 | 4.2 |
| Display | 15 | $37,000,000 | 2.74 | 0.67 | 8.2 |
| Demand Gen | 15 | $27,000,000 | 2.22 | 1.44 | 4.91 |
Incremental ROI from marketing mix models by campaign type, with 95% CI of the median
- Reader Note:
- ROI here is incremental revenue per $1 of ad spend, estimated by Cassandra’s marketing mix models. Cassandra sells MMM software, and these figures are usually lower than the ROAS shown in Google Ads. Non-brand search means ads on generic queries (“running shoes”), not your brand name. It is based on 60 models and $95M in spend in 2023–2025. The confidence interval is the widest among search formats, [2.29–9.89], because results depend heavily on keywords and landing pages. The other six types: Performance Max 4.64x, Search Brand 4.14x, Shopping 3.09x, Display 2.74x, YouTube 2.70x, Demand Gen 2.22x.
- Evidence:
- Modelled / projected
- Vendor interest:
- High — This comes from a company with a commercial interest in this market, so it is not a neutral source.
- Sample:
- 253 revenue-outcome marketing mix models from 59 unique advertisers, covering $383M in media spend; data spans 2023-2025. Each record is one channel from one model run.
Good for
Planning with a rough expectation, and sanity-checking your own assumptions.
Not for
- Reporting as what happened. This is what a model expects, not what anyone counted.
- This comes from a company with a commercial interest in this market, so it is not a neutral source.
- Nobody counted this directly — it's a calculation. The publisher worked it out itself, or took the ad platform's word for what its ads achieved.
- This is an aggregated or average number that covers very different cases. Real results in a particular segment may differ.
- The source says how much it looked at, but not how any of it was chosen.




