Generic service semantics

Generic service/category semantics were expected to reach new non-game-specific audiences and generate additional transactions at acceptable ROAS/CPO/CAC.

Staged campaign testSplit: 50/501 performance snapshot
Stagedtest design
Split50/50
KPICTR / CR / CPA-CPO / ROAS-CAC
PeriodMay 22–Jun 3 vs Jun 3–9

Experiment context

Hypothesis. Generic service/category semantics were expected to reach new non-game-specific audiences and generate additional transactions at acceptable ROAS/CPO/CAC.

How the test ran. The test was run in stages because initial efficiency was weak; query cleanup and exact-match refinement were part of the process.

Traffic split. 50/50.

Performance results 1 snapshot · Click to expand

N/A means the metric cannot be calculated because the baseline is zero or the metric does not apply; — means no source value was provided.

Before vs after optimization — generic service semantics
PeriodSpendConversions (Google Ads)TransactionsCPO (GA4)First purchasersCAC (GA4)
Before optimization (22.05 – 03.06)5,438.65$44.4632169.9641,359.66
After optimization (03.06 – 09.06)321.00$101817.832160.50

Conclusions

Generic service semantics generated incremental reach beyond game-specific queries, but performance should be judged against the same ROAS, CPO and CAC thresholds as core acquisition. Keep the structure isolated so budgets, negatives and bids can be controlled without distorting game-specific campaigns.

Limitations. The before and after periods differ in duration and the campaign setup was optimized between periods. Treat the result as directional rather than as a controlled A/B estimate.