Introduction to Predictive Goals

# In this article

# Predictive Goals overview

Predictive Goals analyzes your historical data to predict which users are likely to convert on your business goals in the future. Predictive Goals also provides insight into the events and properties that contribute most to a prediction, identifying those that may increase or decrease the likelihood of a certain outcome. These insights can help you connect customers with experiences that match their interests and encourage conversion, while promoting the outcomes you seek.

Predictive Goals bridges the gap between insights and actions.

Let’s say your business wants to connect customers with products they love; Predictive Goals can help you identify who’s likely to purchase luxury brands, so you can alert them to new arrivals. Or, if your business wants to mitigate churn, you can identify users who might disengage from your messaging, so you can reconnect with them before they do.

Use Predictive Goals to:

• Identify segments of users who are likely to convert on business goals that you want to optimize (like purchases and promotional clicks), or minimize (returns or unsubscribes), so you can send them personalized messaging. By sending a segment to a dynamic list, you can ensure that campaigns go to all of the contacts who match the segmentation query at send time.

• Identify which of your project's relevant user properties and first-party data (like purchase, system, and custom events) you want to increase or decrease in frequency to promote desired outcomes. For example, let's consider that you create a predictive goal to identify users who are likely to make multiple purchases a month. Predictive Goals uncovers that it's likely that these users will also complete OfferRedeemed events. To increase the likelihood that users will convert, you might increase the number of special offers you send to your customers.

• Generate conversion probability scores for use in journeys to direct how users interact with your brand. For example, users with strong probability scores might be assigned to a journey that recognizes brand loyalty, and those with low scores to one that incentivizes conversion with promotional offers.

# Requirements for valuable predictions

At a minimum, your Iterable project should have:

• Six months of historical data
• 100,000 unique users

The most valuable predictions are defined with 100,000 event and user profile field occurrences across users, with at least 20—30 active events that have been frequently updated in the past month. They should include custom events or properties that represent unique business goals, not generic conversion metrics like email opens or clicks, and goal criteria that's neither be too broad, nor too narrow. Predictive goals that apply to fewer than 1,000 users, or to almost all of the users in a project don’t provide a meaningful forecast.

# Keeping predictions current

Predictive Goals accounts for your ever-changing data landscape by refreshing predictions on a regular basis.

• User conversion probability scores (which relay how likely a user is to convert on your goal) are updated weekly to reflect the most recent user data. These updates are reflected in the Prediction chart where you create user segments.

• Predictive strength scores (which reflect how effective your goal might be) are regenerated and refreshed monthly to reflect changes to relevant project-related data.

Predictions don't have finite start and end dates—they "roll" from the latest refresh date for a period of time that you specify.

# Terms you should know

These terms can help you understand your predictions.