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Implement and select ad bidding strategies from manual CPC to automated target-CPA and target-ROAS.
Passed 21 of 22 test cases with the skill, and 13 of 22 without it.
and 14 more in the full report
9 fixed12 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our retail account gets 90 conversions/month with current CPA of $50. We want to adopt Target CPA. Output the strategy analysis in JSON format adhering strictly to recommendation (strategy, target, currency, confidence), expected_performance (cpa_range, volume_change), and metadata (monthly_conversions, current_cpa, learning_period_days).
| Checked | Without skill | With skill |
|---|---|---|
| The cpa_range is represented as a JSON array of two numbers. | Missed | Passed |
| The cpa_range is not formatted as a single string. | Missed | Passed |
The instructions your model reads when the skill loads.
Bidding strategies determine how much an advertiser pays per auction. Range from manual CPC (full control) to automated strategies (Target CPA, Target ROAS, Maximize Conversions) that use ML to optimize bids in real-time based on contextual signals.
Trigger conditions:
When NOT to use:
IRON LAW: Automated Bidding Requires SUFFICIENT Conversion Data
Below ~30 conversions/month, the algorithm lacks signal and performs
WORSE than manual bidding. Strategy selection depends on data volume:
- < 30 conv/month: Manual CPC or Maximize Clicks
- 30-50 conv/month: Maximize Conversions
- 50+ conv/month: Target CPA
- 50+ conv/month + revenue data: Target ROAS
Assess: monthly conversion volume, conversion tracking accuracy, campaign budget, business goal (volume vs efficiency vs revenue). Gate: Conversion tracking verified, sufficient data for chosen strategy.
Manual CPC: Set bid per keyword. Adjust based on: device, time, location, audience performance data.
Target CPA: 1. Set target cost-per-acquisition. 2. Algorithm predicts conversion probability per auction using contextual signals. 3. Bids up for high-probability conversions, down for low. 4. Aims to average at target CPA over time.
Target ROAS: Same as CPA but optimizes for return on ad spend = conversion_value / cost.
test cases passed, vs 13 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.