I was recently speaking with a friend in the industry and he asked, “Have you played around with Facebook’s new reporting features?” I am in the Facebook reports tab almost daily. There is no way that I would not notice changes or new features, right? But I had missed them, and I’m going to make sure you don’t miss them too!
Where to start?
This filter bar (pictured below) is going to become a Facebook power tool that will make optimizations quicker. It’s not necessarily that you couldn’t have located this information with some effort before, but now it’s easy and fast to check through your campaigns or ads. The breakdown filter allows us to see who is converting and how they are doing it. It is segmented into three sections: delivery, actions, and time. The delivery segments show details on the ad delivery that include age, gender, country, and placement. The actions tab gives more detail on the conversion, which includes the destination, video view type, or the device where the conversion occurred. Lastly, let’s compare data over different time spans, which range from daily to monthly. The time tab will be incredibly useful when judging changes in performance. The three breakdowns that I found most useful were Age, Conversion Device, and Placement.
How do you optimize with these features?
My first steps were to break down by age and then export the data into Excel. While you can use the Facebook interface to judge these numbers with the breakdowns, I wanted to rearrange the data in a spreadsheet. After I downloaded in Excel, I put the data into a pivot table. If you don’t use pivot tables, you definitely should! Pivot tables summarize all your data in one place and allow you to quickly change how it is displayed. I added two “calculated fields” for cost-per-conversion and conversion rate to ensure that those numbers were calculated correctly. The chart below displays all the age data for the entire account: I know that the highest percentage of my conversions come from 45-64-year-olds, and those tended to be better quality leads. The age bracket of ‘65+’ seems to have a slightly higher cost-per-conversion than our goal, which is $18 cost-per-conversion. I decided to re-examine the data on the campaign level. For the two campaigns that I used for the example, Campaign 2 has a significantly higher cost per conversion. This is something I obviously already knew. However, I now know that ‘65+’ is an age bracket in Campaign 2 that is driving up cost per conversion for the age bracket as a whole and that campaign. After drawing conclusions from the age segmentation, I repeated the process for any of the breakdowns that I found most useful. From this, I know to exclude the ‘65+’ age bracket from Campaign 2, to exclude mobile from Campaign 1, and to break out my placements into separate campaigns to better judge performance. The entire process took less than 30 minutes and gives quick and easy to implement optimizations to create more efficient performance.