Facebook launched a new tool not too long ago called Facebook Audience Insights, which I got early access to while working at ROI DNA. Now that it’s been released to the public, I thought I would write up how I have been using it.
Facebook Audience Insights lets you do two key things:
- Break down and analyze your custom audience’s demographics and interests (does not cover data partners or lookalikes)
- Build and save audiences for use in ads manager
Analyzing your custom audience
You know those faceless customer emails that you used to create a custom audience? Now with Audience Insights you can actually start to understand who you are serving ads to! Dive into your target market’s income distribution, top page likes, location and much more.
This information really opens up possibilities for audience expansion by giving you a starting point for your interest and broad targeting.
Building your audiences just got a lot easier with Audience Insights. You are finally possible to see all the available targeting options in one place and how much reach they have. This makes the tool a huge time saver as you do not have to build test ad sets in the ads manager in order to explore the targeting operations.
After building your audiences, you can even save them in Audience Insights and then pull up these saved audiences in ads manager to build ads with.
While working on an airline account at ROI DNA, I ran into the problem of having very little reach in cities outside of the Bay Area, LA and NYC. I had a hunch that the reason for this was because the lookalike audience we were using in each geo, which was generated from emails of the airline’s loyalty club members, skewed heavily towards those three geos. Using the Audience Insights tool, I was able to confirm my suspicions and also solve the reach problem.
My preliminary findings using the tool revealed the following about the airline’s loyalty club members:
- The large majority of loyalty club members resided in the Bay Area, LA and NYC.
- Members skewed very liberal and democratic in their political views.
- Members were highly educated with many holding graduate degrees.
- Most members were white collar professionals.
Using the learnings from Audience Insights, I was able to build an audience that mirrored the demographics and interests of the club members, but without the geo limitations. Using this new “manual lookalike” audience, I was greatly able to greatly increase the reach of the airline’s ads in the 0ther cities.
In the end, my manual lookalike bet paid off and I was able to achieve the target 8x return on ad spend in the other cities with a greatly increased level of spend.
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