Predictive Audiences: Smarter Targeting with Marketo

[Ruchi_Lapran1]

Marketers constantly seek smarter ways to build segmentations or profiles for targeting the right audience through the right channels at the right time. While traditional segmentation is helpful to gauge the intent based on behavior, demographics, or engagement activities, it often falls short in predicting future behavior. That’s where Marketo’s Predictive Audiences come in useful.

I have recently started to explore this feature and its capabilities so I aim to highlight potential practical use cases and share how Marketo users can start thinking about integrating Predictive Audiences to upscale their marketing automation strategy.

1. What Are Predictive Audiences in Marketo?

Predictive Audiences utilizes AI and Machine learning models to score and segment leads based on their likelihood to engage with your campaigns. It helps marketers shift from manual segmentation to intelligent, data-driven targeting.

Predictive Audiences in Marketo Engage provides the following features:

• Registration and Attendance Likelihood values for every lead in an event program
• Predictive Filters
• Models and influencing factors
• AI/ML-based Insights
• Goal Tracking and Projected Registrations (Goal Tracking is only available to those who have the modern UX toggle enabled for Event Programs)

2. Availability

Predictive Audiences is automatically included and activated for instances utilizing Prime or Ultimate pricing bundles. For getting access to this feature, please contact your Adobe Account Manager.

a) Once the feature has been added to your instance, you should see the Predictive Audiences page in the Admin area of Marketo.

b) Check Enable Predictive Audiences to activate the feature. Note that it could take up to 24 hours for all processes to complete set-up.

c)  All of your models and their statuses can be seen in Models and Data Health section under Predictive Audiences in the Admin area.

3. How Predictive Audiences Work?

The system builds behavioral models using historical engagement and conversion data to identify patterns among those who engaged with your programs. Each person in your database is scored according to their predicted likelihood to engage or convert.

You can then use predictive filters to segment based on those scores, such as: "Predictive Engagement is High", "Predictive Goal Conversion Likelihood is Medium or Higher", etc....

Note 1 # The “Likely to Attend” and “Likely to Register” filters can only be used in Event Programs.

Note 2 # “Likelihood to Unsubscribe,” “Lookalike of Program Members,” and “Lookalike of Smart List Members” can be used in all program types.

Note 3 # The “Likelihood to Attend/Register/Unsubscribe” filters must be used in conjunction with other standard filters.

Note 4 # “Lookalike of Program Members/Lookalike of Smart List Members” helps you expand your current audience by targeting people who share key characteristics with your ideal customer profiles based on factors like lead attributes, email activity, web activity, and engagement.

These filters dynamically adjust as the model learns and updates in real-time, making them highly valuable for agile campaign targeting.

4. Potential Practical Use Cases (with Industry Examples)

Here are several ways marketers could apply Predictive Audiences to elevate their campaigns:

➡️ Precision Email Targeting

Example: A SaaS company launching a new feature targets only the top 30% of users most likely to engage based on predictive scores. This reduces campaign cost while boosting open rates and conversions.

Method: For a product launch, target only the top 30% of leads based on predictive engagement. Then, analyze performance before expanding to a broader segment. Reduce email fatigue and increase engagement by sending promotional or content emails only to those leads with a high likelihood of engagement.

➡️ Nurture Streams

Example: A B2B Manufacturing company focused on leads showing high predictive engagement are fast-tracked to receive product offers and demo invites, while lower-scoring leads are nurtured with thought leadership content and case studies.

Method: Leads with high predicted engagement can enter a fast-track nurture stream, while low-likelihood leads can be placed into longer, education-focused journeys. Use engagement likelihood to define lead stages and personalize nurture content accordingly.

➡️ Web Personalization Based on Intent

Example: Prospective students at a University with high goal conversion scores are shown scholarship offers and application CTAs, while casual visitors are guided to explore programs or take a virtual campus tour.

Method: Display CTAs or offers to visitors with high predicted goal conversion likelihood. Show dynamic content to website visitors based on their predicted likelihood to register, convert, or attend an event.

➡️ Campaign Goal Tracking & Forecasting

Example: An event marketing team for a large tech conference uses predictive modeling to estimate how many registrations their segmented email list will generate, allowing for smarter budget allocation and follow-up planning.

Method: Before launching a webinar campaign, view the projected registrations using Predictive Audiences and adjust your strategy accordingly. It enables you to forecast the performance of a campaign based on the targeted audience.

You can also easily apply Predictive Audiences to the following areas:

👉 Email Sends (if there is an error in the evaluation of predictive filters, the campaign will automatically abort)

👉 Campaign Filters (not available for trigger campaigns)

👉 Smart Lists (you can use upto 5 predictive filters in smartlists)

👉 Data segmentation (Predictive filters currently have an input limit of 1 million qualified people)

👉 Web Personalization (This will require Adobe’s Web Personalization Add-On and the Predictive Audiences feature)

 

Final Thoughts 💡

Those who haven’t yet used the feature, I believe these potential use cases outlined above could offer a strong starting point for any Marketo user exploring Predictive Audiences.

Have you tried Predictive Audiences in Marketo? I’d love to hear your experiences or questions—let’s start the conversation!