Predictive Audiences helps you classify an unknown audience into distinct personas, in real-time, using advanced data science techniques.
This article contains product documentation meant to guide you through the setup and usage of this feature. Nothing contained herein is legal advice. Please consult your own legal counsel for legal guidance.
In a marketing context, a persona is an audience segment defined by visitors, users, or potential buyers, who share a specific set of traits, such as demographics, browsing habits, shopping history, etc.
Predictive Audiences models take this concept a step further, by enabling you to use Audience Manager’s machine learning capabilities to classify unknown audiences into distinct personas. Audience Manager helps you achieve this by calculating the propensity of your unknown first-party audience for a set of known first-party audiences.
When you create a Predictive Audiences model, the first step is choosing the baseline traits or segments that you want your target audience to be classified by. These traits or segments will define your personas.
During the evaluation phase, the model creates a new Predictive Audiences segment for each trait or segment that you defined as baseline. The next time Audience Manager sees a visitor from your target audience who is not classified for a persona (did not qualify for any of your baseline traits or segments), the Predictive Audiences model will determine which of the predictive segments the visitor should belong to, and add the visitor to that segment.
You can identify the predictive segments created by the model, in the Segments page. Each Predictive Audiences model has its own folder under the Predictive Audiences folder, and you can see each model’s segments by clicking the model folder.
To help you better understand how and when you could use Predictive Audiences, here are a few use cases that Audience Manager customers can solve by using this feature.
As a marketer in an e-commerce company, I want to classify all my web and mobile visitors into various brand affinity categories, so that I can personalize their user experience.
As a marketer in a media company, I want to classify my unauthenticated web and mobile visitors by favorite genres, so that I can suggest to them personalized content across all channels.
As an advertiser for an airline company, I want to make sure I classify my audience based on their interest in travel destinations, so that I can advertise to them in real time, within a short retargeting window.
As an advertiser, I want to classify my first-party audience in real time, so that I can react quickly to trending news.
As a marketer, I want to predict which customer journey phase my website visitors are in, such as discovery, engagement, purchase or retention, so that I can target them accordingly.
As a media company, I want to categorize my audience, so that I can sell my advertising space at premium pricing, while offering my visitors relevant ads.
When you create a Predictive Audiences model, you go through three steps:
You can choose any of your first-party traits or segments to define your personas. However, for optimal results, here’s a set of recommended best practices:
Depending on your use case, whether you want to classify users in real-time, in batch, or both, choose a target audience (trait or segment) which has a significant real-time and/or total population. Similar to persona selection, we recommend that your target audience trait or segment has users with rich profiles (rich sets of traits).
When selecting the target audience, analyze your your use case and decide which types of IDs you want to classify: device IDs or cross-device IDs. The Profile Merge Rule that you select when creating the model defines the data that will be used to place each user into the predictive segments.
As a best practice, we recommend choosing a Profile Merge Rule that has the same configuration as your target audience Profile Merge Rule, or one that includes the profile type (device profile or authenticated profile) of your target audience.
Before the algorithm can classify your first-party audience into the right personas, it needs to train itself on your data.
For each persona that you define, the algorithm analyzes its respective audience and evaluates any real time and/or onboarded trait activity for its users in the last 30 days.
This step takes place once every 24 hours, to account for changes in your first-party audience.
For real-time and batch audience classification, the model first checks whether a user belongs to the target audience. If the user qualifies for the target audience and does not belong to any of the personas, the model assigns them a persona qualification score.
While evaluating first-party audiences and assigning scores, the model uses the default Profile Merge Rule defined in your account. Finally, the visitor gets classified into the persona for which they received the highest score.
Read through this section carefully before moving on to the implementation phase.
When configuring your Predictive Audiences models, keep in mind the following considerations and limitations:
Predictive segments created by Predictive Audiences models inherit the Data Export Controls from the following first-party data sources:
The newly created predictive traits and segments will have the same privacy restrictions as the union of the first-party data sources described above.
Traits that have additional restrictions that aren’t part of the Predictive Audiences segment privacy restrictions will be excluded from the training phase, and will not become influential for the model.
All predictive segments will be assigned the Profile Merge Rule that you selected when creating the model. The Profile Merge Rule that you choose is important for the following reasons:
Selecting a Profile Merge Rule that uses both device data and cross-device data maximizes the number of traits that could be used for model training and user classification into the predictive segments.
The traits and segments that you choose for personas and audience classification are subject to Audience Manager Role-Based Access Controls.
Audience Manager users can only select traits or segments for personas and target audiences, that they have permission to view.