Utilizing lookalike audiences to drive results on Meta for Coursology.
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Coursology is a platform providing students with tools to tackle homework, improve writing, and study more effectively. They partnered with Structured Agency to optimize their new customer strategy, creative testing, and performance tracking to hit key growth KPIs, ensuring every campaign drives measurable results.
The key KPI for this client is Net New MER, and we were looking for opportunities to unlock new learnings through testing, to continue improving that metric. We saw great performance in Q1, and not so great performance in Q2, so we decided to test running a campaign with a lookalike audience of people that had purchased in Q1. The idea was to replicate the success we saw in Q1 with the LAL audience.
30 days in, the 8% LAL audience rose to the top, becoming the top-spending ad set in the account, driving a CPA 10% lower than the account average and a ROAS 12% higher than the account average.
Recreate What Worked in Q1
Q1 performance was strong, and those purchasers represent a high-quality audience who responded well to the offer and creative. A Lookalike Audience (LAL) built from them helps Meta’s algorithm find new people who share similar behaviors, interests, and demographics, essentially scaling what worked before.
Reach New High-Value Prospects
Q2 campaigns were likely reaching broader or less qualified users. A lookalike based on Q1 buyers focused on people statistically more likely to convert, improving conversion rates, ROAS, and CPA.
Adapt to Seasonal or Behavioral Shifts
If Q2 performance dropped because of seasonality or audience fatigue, Q1 lookalikes might uncover new segments similar to best historical buyers, reduce ad fatigue among existing audiences, and bring in fresh, but relevant, prospects.
Feed Better Data into Meta’s Algorithm
The more accurate and high-quality your seed audience (Q1 purchasers), the smarter Meta’s optimization gets over time.
The Plan:
This test ultimately led to a more efficient performance in-platform and uncovered learnings for additional LAL audiences that we continued to lean into to attract new customers.
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