- AdTech / MarketingIndustry
- Ad & marketing teamsClient
- Machine LearningCategory
- Phased rolloutTime to impact
The challenge
- Deciding which ads and placements to back is mostly guesswork until the spend is already out the door.
- By the time CTR comes in, the budget is gone.
Predicts which creatives and placements will actually get clicked, before you spend on them.
A machine-learning model that predicts click-through rate for ad creatives and placements, so budget goes to what will actually perform instead of being decided by gut feel.
What we built
- 01Learns from historical creative, placement and audience data.
- 02Predicts CTR for new creatives and placements before spend.
- 03Ranks the options so budget goes to the strongest.
- 04Sharpens as more performance data comes in.
How it works
What it can do
CTR prediction
Estimates click-through before you spend.
Creative ranking
Ranks creatives and placements by likely performance.
Budget guidance
Points spend at what will actually perform.
Learns over time
Improves as new performance data arrives.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
A machine-learning model that predicts click-through rate for ad creatives and placements
The media buyer
Decideswhere the budget actually goes
Seesthe ranked creatives with their predicted CTR, and the history the prediction was learned from
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
Spend goes to the creatives most likely to be clicked, not the ones that just looked good in the meeting.
Budget aimed at creatives that will perform.
Less spend wasted on weak placements.
Decisions backed by data, not gut feel.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Data in
- Feature engineering
- Model layer
- ML models
- Forecasting
Common questions
The same answers as above, written out.
A machine-learning model that predicts click-through rate for ad creatives and placements, so budget goes to what will actually perform instead of being decided by gut feel.
Deciding which ads and placements to back is mostly guesswork until the spend is already out the door. By the time CTR comes in, the budget is gone.
Feed in historical creative and performance data. The model predicts CTR for new options. Budget follows the highest-ranked creatives.
The media buyer decides where the budget actually goes. They see the ranked creatives with their predicted CTR, and the history the prediction was learned from.
Data in: Feature engineering. Model layer: ML models, Forecasting.
Budget aimed at creatives that will perform. Less spend wasted on weak placements. Decisions backed by data, not gut feel.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
- Machine learning
- AdTech
- Marketing
- Forecasting
- AdTech / Marketing
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