Abandoned browse
Prerequisites
- Postman setup –> Postman installation
- Data Stores – Profile in Action –> Create Datastream
If you have not completed these labs, do so now before continuing.
Lab overview
In this video, you learn how the abandoned browse use case description reveals its decisioning elements, and what you’ll build in this lab to deliver a personalized, eligibility-aware phone offer in real time.
Business objectives
For this lab, Connection 5G wants to increase sales of the new Apple flagship phone, the iPhone 17, by targeting customers who browsed the iPhone 17 overview page but haven’t purchased. The key objectives of the campaign are as follows:
- Identify high-intent customers by detecting when a user views a flagship phone page multiple times without completing a purchase.
- Trigger a real-time personalized experience across all of Connection 5G’s digital surfaces when this behavior occurs.
- Deliver contextual offers based on key customer attributes such as the account holder’s age and their current mobile plan.
- Ensure offer eligibility is enforced so that customers only see phone offers that are compatible with their plan.
- Dynamically adjust the phone tier offered (for example, base, pro, ultra) based on the customer’s engagement or response to previous offers.
- Provide consistent personalization across channels by using centralized decisioning logic to determine the best offer in real time.
- Increase conversion likelihood by presenting the most relevant flagship phone offer to each customer at the right moment.
Lab learning objectives
To meet the above business objectives in this lab, you learn how to:
- Extend the offer data model by adding custom attributes to the offer schema so they can be used in decisioning logic.
- Create eligibility rules that determine which profiles qualify for specific offers based on profile attributes.
- Build and configure offer items, including setting priorities, defining eligibility conditions, and applying frequency capping.
- Organize offers into a collection so they can be easily referenced and evaluated during the Decisioning activity.
- Create a ranking formula that dynamically adjusts offer priority based on profile characteristics.
- Configure a selection strategy that combines offer collections, eligibility rules, and ranking logic to determine which offers are considered and how they are ordered.
- Set up a Code-Based Experience (CBE) channel to allow external systems to request decision results and receive offers in JSON format.
- Test the end-to-end decisioning workflow by sending experience events and decision requests to validate eligibility logic, ranking behavior, and frequency capping.
By completing this lab, you gain practical experience designing and validating a complete offer decisioning workflow in Adobe Journey Optimizer to meet the business use case.