Since efficiency-based bidding strategies were not working for our client as expected in this case, we decided to go back to first principles.
How could we increase the likelihood that a customer interacting with our ad had a higher than average chance of passing the initial soft credit check?
Our belief was that if we could increase the number of users who passed the initial soft credit check we would be able to drastically improve the sales CPA, which would then unlock more budget and the ability to scale the account.
We had two key initiatives for finding these users:
By building a credit map of the UK, we could find users who were more likely to pass the soft credit check based on their location and adjust our activity accordingly.
Leveraging Google audiences to adjust our activity based on a user’s similarity to those that have successfully ordered a product from our client.
So, let’s break those solutions down:
#1 Building our credit map of the UK
Our first step was understanding the correlation between available UK deprivation and income metrics against our client’s internal conversion rates.
We turned to the Office for National Statistics (ONS) and downloaded publicly available data on average income and deprivation levels by postcode districts for the UK.
We then built a database with predicted acceptance-based bidding adjustments per district based on the likelihood of users within that district having a higher or lower prosperity calculation & higher or lower average income level after housing costs against the national average.
The bidding adjustments (i.e. the relative adjustment) would then be used within the Google Ads platform as bid modifiers.
Sample of the bid calculations:
Sample of the bid adjustment map:
Once the database was built and our predictions created, we wanted to validate the correlation with actual acceptance or rejection rates from our client.
Checking actual acceptance or rejection rates vs. internal data would validate our credit map calculations, and therefore allow us to move forward with an in-platform test.
We did indeed find a strong correlation between our combined deprivation index and the rejection rates observed by our client.
The next step was to move forward with a test in Google Ads with geo-location bid adjustments.
#2 Leveraging Google audiences
With our credit map built (i.e where users are who are more likely to be accepted), we wanted to find users with behavioural traits who were more likely to be accepted.
When approaching audience management we wanted to look at this from 2 sides:
Users who are like customers who have been accepted (i.e. we want to find more of these users).
Users who are like prospects who have not passed their soft credit check (i.e. we want to avoid showing ads to these users).
We already had conversion events set up for sales, so we created an additional conversion event for ‘rejections’. These conversion events were not used for bidding, but purely for reporting and sending user data back to Google Ads.
Once significant data was passed back, we started to review the data insights for each audience (and their various lookback periods), and we picked out relevant in-market audiences where a high index was achieved.
Once we had the highly indexed in-market audiences for both targeting (i.e. users who were like our converters) and exclusions (i.e. users who were like our ‘rejections’) we were ready to test them in the platform.