Powered by Journey AI, Adobe Campaign can analyze and predict open rates, optimal send times, and probable churn based on historical engagement metrics.
This capability is not available out of the box as part of the product. The implementation requires Adobe Consulting to be engaged. To find out more, please reach out to your Adobe representative.
Predictive engagement scoring predicts the probability of a recipient engaging with a message and the probability of opting out (unsubscribing) within the next seven days after the next email send. The probabilities are further divided into buckets according to the specific risk of disengagement, medium, or low. The model also provides the risk percentile rank for the customers to understand where the rank of a certain customer in relation to others.
Learn how to view engagement scores at the individual profile level, use scores for targeting engaged users and suppressing fatigued users and how to create typology rules to manage customer fatigue.
This is a demonstration of Predictive engagement - scoring powered by Journey AI in Adobe Campaign Standard.
In this video, we will - review engagement scores at the individual profile level, using scores for targeting engaged users and suppressing fatigued users, in creating typology rules - to manage customer fatigue.
Scores will be stored in - the Adobe Campaign Data Mart and joined to the customer profile.
A marketer can evaluate - the engagement model scores by accessing the custom data resources under client data.
Scores can also be viewed at - the individual profile level.
Let’s take a look at the - scores for a specific profile in this environment.
Drive into the profile record, and then let’s click onto - the detail view screen of the profile.
Here we see the standard attributes that exist on the profile table, but we’ve also included a link to view the Predictive engagement scores.
We’ve separated the view to show scores by engagement and retention.
The product documentation - contains a definition and description of each score and how those scores were calculated using the Journey AI model.
Next let’s focus on using these scores in our marketing campaigns.
In this first example, we’ll focus on using the engagement scores in particular click engagement to target the highest engage users and send a special offer - in a weekly newsletter, for example.
Engagement probability scores - represent the probability that a subscriber will engage - with a specific message.
So a marketer can determine - any threshold to use based on the distribution - of their customer scores, as well as their own business use cases.
So here we’re choosing to target profiles with a click engagement higher than 5%.
First, we target all of - our newsletter subscribers, followed by a segmentation activity.
The segmentation activity will - isolate only the most likely to click into the newsletter.
The condition we’ve set - here is quite simple. We use our click engagement score in operator of greater than, and we set our value to 0.05 or 5%. Well this is a very simple example, understand that a marketer - can layer these scores on top of already defined - business logic and data logic to work with their campaigns.
Next, we want to detail a few examples of creating typology rules to help govern delivery execution.
Engagement scores can also be used to reduce customer fatigue by enforcing exclusion - or suppression rules.
So Adobe Campaign typology - rules can be configured to automate these exclusions across a certain set of deliveries.
First, let’s look at - creating a filtering rule to remove customer profiles that have a high - unsubscription probability.
First create a new rule, set the type to filtering and bring in the engagement score link let’s join to your profile.
Choose the Unsubscription - Probability score and set the filter accurate criteria. We’ll choose to suppress - all customer profiles with an unsubscribed probability, again, greater than 5%.
This effectively will - remove any customer profile that’s indicating a higher - Unsubscription Probability than what we’ve included here.
We have the capability to - apply this typology rule to a certain set of deliveries, or to allow the rule to apply - to all deliveries globally. We set that under - Application criteria here.
Let’s take a look at another example. Here we’ll review a fatigue rule where a marketer can - create a frequency cap for the number of messages - a profile can receive in a given time period.
First, we set our rule type to fatigue.
And in this case, we’re - going to want to set a rule that’s going to restrict any profile with a retention level of low to receive at maximum two messages a week.
This will allow retention - scores of medium or high to receive the regular marketing cadence just reserving anyone with low retention for maybe the most important - content or campaigns that you’re sending from your system. First, we choose a - threshold type of constant, and we set that threshold to two for the maximum number of communications within this given time period.
Next let’s choose a calendar period, set for us seven day period or one week.
Now the important part here is we need to select the checkbox for refined threshold on - profile and deliveries. This is going to allow us - to use our engagement scores to refine who this rule is applied to.
Going to click on the - edit profiles to count, and I can create my filter condition here, going to bring over the - engagement score link.
For this rule, I’m selecting - my retention level, and setting that value to low.
Here you can see my rules been complete. I now have a specific fatigue rule targeting only low retention users.
These are just a few examples, how to leverage these engagement scores in Adobe Campaign Standard. -