Intermediate level · Module 3: Paid Campaigns on Meta and Google Ads · Lesson 4 of 4
Testing and Optimising Campaigns
Use simple experiments and cost metrics to improve campaigns over time.
In this lesson you will
- Design a fair A/B test
- Optimise using cost per result and frequency
Key concepts
A/B testcost per resultCTRad fatigue
The lesson
Optimisation means using results to improve: moving budget to the ad or audience with the lower cost per result, pausing poor performers and testing a new idea.
Judge results on the metric that matches the goal — cost per lead or cost per message, not likes. Wait for enough data before deciding; a few clicks can be luck.
Write down every change and its reason in a change log, so you can explain the campaign to the client and learn for next time.
Step by step
- Calculate cost per result for each ad and audience.
- Identify the clear winner and loser.
- Decide what to scale, pause and test.
- Record decisions in a change log.
Practice activity
Analyse a provided campaign dataset, decide which ad and audience to scale or stop, and justify the decision.
Expected result: A justified optimisation decision.
Check your understanding
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Why change only one thing at a time in an A/B test?
Show answer
So any difference in results can be attributed to that change.
Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.