Decision policies
Learning objective
By the end of this lesson, you will be able to:
- Explain what a decision policy configures and where it’s applied
- Define a decision package and what it comprises
- Differentiate the individual and grouped methods of combining multiple selection strategies
- Explain how frequency capping interacts with the number of decision items a policy returns
Materials needed
-
12 playing cards (Jack, Queen, King from each suit)
-
13 sticky notes
- 12 filled with both attribute name and values from previous lessons
- One new sticky note to track the requests
Lecture
This is the longest and most involved simulate in the course. You’ll simulate live decision policy behavior — making repeated “requests,” tracking impressions against frequency caps, and watching cards drop out and get replaced — then apply everything to a real business scenario comparing individual vs. grouped selection strategy combination.
Key takeaways
- A decision policy applies selection strategies to an actual AJO delivery channel, configured on a channel node in a journey or a campaign’s channel section
- A policy can use none, one, or many selection strategies; with none, it returns items by original priority score, filtered by item-level eligibility
- A decision policy plus its delivery channel together are called a decision package — the configuration that lives on the hub or edge
- With individual combination, each strategy’s collection is ordered separately, then the lists are stacked; with grouped, all items are ordered together into one list and duplicates use the higher of their two scores
- The same inputs can produce dramatically different final orders depending on individual vs. grouped
- Frequency capping directly limits how many items are available to return, so plan enough uncapped fallback items to fill every slot
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