Campaign audiences in LoyaltyPlant can include both a target group and a control group. Used together, they provide a reliable way to measure campaign impact and understand how user behavior changes in response to an offer.
This article explains how each group is formed, what role it plays, and why both are essential for running effective campaigns and interpreting results with confidence.
Target Group
The target group is the main audience the campaign is designed to reach. These are the users who will see in-app content, receive push notifications, and get any rewards included in the offer.
It is defined in the campaign scenario section using a combination of segmentation filters and campaign-specific logic. Based on the configuration, this can include order history, profile fields, past activity, inactivity thresholds or selected user IDs.
📝 Note: Only users who match all selected conditions are included in the target group.
Once the campaign is active, the way the audience updates depends on the segmentation type:
Static: The target group is fixed at launch and does not update
Dynamic: New users who match the filters are added automatically and receive the offer, with no need to relaunch the campaign
This makes it possible to run a fixed campaign with a defined audience or keep it active continuously as new users qualify.
👍 Use case:
Use static segmentation to test different messages or creative options. A fixed audience removes variability, making results easier to compare and use for campaign optimization.
Use dynamic segmentation for ongoing campaigns that should respond to real-time behavior, such as birthday offers or inactivity triggers.
Control Group
A control group is a small, randomly selected portion of the campaign audience that meets all targeting conditions but does not receive any messages, rewards, or in-app content.
This group acts as a baseline for comparison. By excluding them from the offer, it becomes possible to compare campaign results against organic user behavior under the same conditions. Without that comparison, it’s easy to misinterpret outcomes and assume that any change was caused by the campaign.
In practice, user behavior is often influenced by many factors, including seasonality, competing offers, or broader trends. Control groups help separate these effects and confirm whether the campaign itself made a measurable difference.
To enable it, check the box Make 10% of selected audience a control group in the campaign scenario section, under Set up custom parameters for campaign audience. The system then automatically selects a random portion of the qualified users and includes them in the control group.
🛠️ Control groups are available in the Win-Back Lapsed Customers and Share News Offers campaigns.
How It Works?
Once a campaign with a control group is launched, the system checks users against the defined segmentation filters and campaign settings. Those who qualify are either:
included in the target group, which receives all campaign content
randomly assigned to the control group, which is excluded from the campaign
📘 Info: In dynamic campaigns, new users who meet the filters are automatically added to the target group, with a portion randomly assigned to the control group.
By comparing campaign results against a control group, it becomes possible to measure the true incremental impact of the campaign. Unlike A/B testing, which compares variations within the target audience, control group analysis reveals what would have happened without the campaign at all — accounting for natural user behavior and external factors.
This approach supports data-driven decisions and helps avoid risky assumptions, such as believing all results were driven by the campaign or that users would have acted the same way regardless.