Defining Priority
Some visitors can qualify for more than one audiences created in Fibr. Fibe ensures to keep the personalization mutually exclusive but you need to define the priority.
Visitors can easily qualify for more than one audience rules, for example, you have created Variation 1 for Audience with utm-source = Fb_Ad_Copy_1, whereas you also have a Variation 2 for Audience with rule location = New York. If a person in New York, comes through utm-source = Fb_Ad_Copy_1, should they be shown Variation 2 or Variation 1?
To solve this dilemma you need to set Priority in your Personalizations.
Please take care when setting your priority order or you may end up not showing the most relevant experience to the right set of visitors.
In this page we will explore:
How prioritization works
Fibr will automatically ensure your experiences don't conflict with one another. A visitor can only see one variation on any page. You can manage the priority in the Campaign Details.
Here you will be able to define priority for every rule you create. Priorty 1 will be choosen over Priority 2.
How to think about experience priority
Audience size
In general, you should have your smallest and most targeted variations as top priority. If these Audiences are too low on your priority list, you run the risk of very little traffic seeing those experiences. For example, if you have an All Traffic audience as first priority, no one will ever see any other variations on those pages.
Highest trust
If you have more faith in accuracy of some data sources over others, you can choose to make those a higher priority. For example, maybe you have some variations targeting customers using specific data like which Ad they came from, and some from inferring data like where they are located. You would want to prioritize your specific data audience over the inference data audience.
Strategic importance
A final lens you should use when considering your variations priority is your company's strategic priorities. What are your highest priority audiences? Which are your highest performing variations?
Remember to also look at your experiment results - these might make a case for a bump in priority as well!
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