We’ve discussed why your LAL audiences aren’t working post iOS14. In this post, we’ll discuss how to improve the performance of your LAL audiences.
Let's jump right into it.
Your buyer persona is your understanding of who your customers are.
However, you have your best resource — your customers. Real people who have already bought from you.
You have their names, email addresses, details of what products they bought, how often they’ve bought from you, their total spend, etc., in your database with Shopify.
So, how do you use this first-party buyer data to improve your CAC (Customer Acquisition Cost) and target better?
In theory, this is what Lookalike audiences do.
You upload your past buyers to Facebook, Facebook ‘learns’ your buyer persona by profiling your actual buyers, and, finally, finds more people who ‘look like’ your existing customers.
To recap, Facebook makes a ‘data-driven buyer persona’ from your past buyer data and uses that persona to find other people who may buy from you.
This worked beautifully until recently.
Yes, you guessed it. Things aren’t as great after iO14.
Post iOS14, the two things that get affected are:
While there is little you can do about Facebook’s ability to track intent or its match rate, there’s a lever you can use to improve the performance of your lookalike audiences:
Customer Value: Most customer lists are exported from email programs which don’t send the value field along to Facebook. This ‘value field’ indicates how valuable this customer is for your business, relative to your other customers.
Without this, Facebook will make an audience that looks like all your customers — and not your best customers.
Sending a value field while uploading your custom audiences can mitigate some of the damage caused by iOS 14.
See https://www.facebook.com/business/help/917879191754763 for details.
This helps Facebook look for people who are more like your most valuable customers and not your least valued customers.
It’s very common to send a customer’s lifetime spend as the value for a customer. But is spend a reliable measure of a customer’s value to your business?
Let’s take the example below. Based on their lifetime spend, Jason is more valuable than Amanda whose value is higher than Bill:
However, let’s look into the transaction details of the same customers:
Once we look at the details, it’s apparent that Jason made one large purchase years ago and never came back. Whereas Bill bought recently, has made multiple purchases and also their overall monetary spend with the brand isn’t low. Clearly, Bill should be more valuable for the business than Jason.
The kind of analysis where we look at:
This is called RFM analysis. Using this value instead of the total customer spend is a much better way to tell Facebook about a customer’s relative value to your business.
RFM presents a much more well-rounded picture of who your most valuable customers are TODAY and not 2 years ago.
We know it works because Meta uses RFM-based algorithms to make lookalike audiences from pixel purchasers. See this screenshot of the Meta audience interface.
Out of the box, Socioh scores all your past buyers with an RFM score.
This is the score we upload to Facebook when making customer audiences via Facebook.
This score is refreshed daily as new buyers are added to your database and past buyers are re-purchasing, changing their scores.
Plus, with Socioh, you have an added advantage. Your audiences are kept synced with your Shopify store.
This means that every time your store gets another purchase, we automatically refresh your source or seed audience. This in turn forces Facebook to keep your lookalike refreshed.
For customers with a large number of past purchases, there’s another lever you can pull to improve your lookalike's performance.
Want to know more? Read our blog post on audience segmentation for details!