November 10, 2022 -
The following is part 3 in this series, if you need to catch up, here is part 1 part 2 and part 3 or download the complete guide.
In this final installment we’re looking at why and how to use predictive audiences:
Now that we know what predictive audiences are, what we can do with them, and how can they be helpful?
There are two main use cases to leverage predictive audiences in marketing activities, Remarketing audiences, and re-engagement campaigns.
Remarketing audiences is a list of users grouped together who are likely to convert and more easily convinced to complete the conversion. Google Analytics 4 automatically analyses the data by applying machine learning algorithms on the data set and predicts the future behaviour of said user to find patterns of behaviour that are unique and the specific users likeliness to convert.
Re-engagement campaigns are designed for a predictive audience of users who are likely to churn. With this information, if there are a significant number of users who are likely to churn, you can consider running a re-engagement campaign to encourage these users to engage in the desired activity.
Remarketing Audiences
Users who have already visited the product details or added items to their carts, have given strong signals they will likely purchase the product.
Re-engagement Campaigns
While users who are likely to churn are signalling a waning interest in your business, they have also previously demonstrated engagement with your business. Approach them again with reminders of the value you offer in terms of product variety, quality, and price, or convenient shipping and return options. Remind them of their value to you with special offers such as a promo code.
Problem: McDonald’s Hong Kong wanted to optimise its mobile ordering journey & drive more in-app orders. They wanted to use machine learning solutions to gather customer insights and use them for their in-app ad campaigns.
Solution: The team at McDonald’s implemented GA4 & started collecting real-time ecommerce data from their app. McDonald’s turned to predictive audiences, a tool that allows marketers to predict future purchase behaviour based on those ecommerce insights.
McDonald’s tested a handful of GA4’s ready-to-use predictive audiences that met their prediction-modelling prerequisites. The “likely 7-day purchasers” audience ended up driving the most ROI and they decided to focus their ad investment on that segment.
Thanks to integration between GA4 and Google Ads, those predictive audience segments could be exported to their ad campaign. McDonald’s used App Campaigns for Engagement, a fully automated Ads campaign type that prompts app users across multiple Google platforms to take action. Google Ads automatically created optimised ads for relevant audiences, and in tandem with insights on likely purchasers, it helped them drive more in-app orders.
Results: In just two months, the team increased their conversions 550% for “likely 7-day purchasers” and decreased cost-per-acquisition 63% among that group. On top of that, McDonald’s saw 560% increased revenue within that audience.
Q. Can we use historical data when creating predictive audiences?
Q. Can any type of audience be used for either a remarketing campaign or a re-engagement campaign?
Q. Is this completely AI or is there a human aspect involved?
Q. Would this be valuable to every type of business, whether it’s an e-commerce vendor or a content site?
Q. What does the membership duration refer to?
Q. What are the reasons not to use predictive audiences?
Q. Because it’s a machine driven model, does that mean it can get smarter and smarter based on how the user uses it?
Q. Is there a way to understand how much time this is saving?
There aren’t many companies who build their own audiences and if they do it, it’s not as accurate and would likely be a year or two old. Once you connect to google ads, the audiences will automatically appear in Google Ads. GA4 machine builds the audience and constantly upgrades the audience, and the campaign in theory should then create better ROI over time because the machine is making the audiences more and more accurate. So, the clients save time, are leveraging all of their up to date data, the machines are doing all the grunt work, and in theory, companies should be getting better results from their media spend.
Q. Why should I care about this feature in GA4?
This is the last in our Predictive Audiences in GA4 series , download the complete Guide.