--- title: "Predictive Segments: User Engagement" slug: "predictive-segments-user-engagement" description: "Learn how to use predictive segments for user engagement in InOne to target high-value audiences and improve campaign performance." tags: ["likelihood to open", "predictive segmentation", "predictive segments", "user engagement"] updated: 2026-07-06T09:39:26Z published: 2026-07-06T09:39:26Z canonical: "academy.insiderone.com/predictive-segments-user-engagement" --- > ## Documentation Index > Fetch the complete documentation index at: https://academy.insiderone.com/llms.txt > Use this file to discover all available pages before exploring further. # Predictive Segments: User Engagement User Engagement helps you target users likely to engage with your brand. It enables you to develop an engagement strategy that maximizes conversions and minimizes the number of unsubscribed users. In predictive modeling, a user’s behavior is compared to the journeys and historical data of other users. User Engagement uses a machine learning algorithm to analyze user data and events (e.g., email open rates, recency, user lifespan). The User Engagement algorithm learns from users' behavior when opening Email, Web Push, App Push, SMS, and WhatsApp messages. It predicts which users will likely open the email, push, or messages within the next seven days. To segment your users, select **Predictive Segments** > **User Engagement**. Choose the product from the dropdown menu to target users, and set your operator to **High**. ![](https://cdn.document360.io/c6df4583-da94-4cb2-bb8a-be0cbdd11109/Images/Documentation/image-1694160124929.png) ## Model Details The User Engagement model applies either a regression model for scoring engagement or a multi-class classification model to group users into High, Medium, or Low engagement tiers. It is designed to assess overall user engagement with the platform, helping inform upsell, cross-sell, or re-engagement strategies. - **Model type**: Regression model or multi-class classification - **Segment update frequency**: Daily ## Data Used The model processes historical engagement and behavior data from multiple touchpoints: - Website and app visit frequency - Email open and click-through rates - Time spent per session - Interactions with marketing messages across push, SMS, and WhatsApp - Delivered, opened, and clicked event metrics across channels ## Key Features Key behavioral indicators used to evaluate engagement include: - Email click-through rates - Frequency and depth of site or app sessions - Interaction frequency with marketing messages To run this algorithm, you will need the required event numbers and product quantities below: - Both **Likelihood to Open** and **Likelihood to Engage** algorithms should run for all channels. - To run the **Likelihood to Open** algorithm for [Email](/docs/email-overview), you should run the algorithm for at least 30 days with at least 1000 email-delivered events, at least 100 email open events, and more than 10 email click events. - To run the **Likelihood to Engage** algorithm for [SMS](https://academy.useinsider.com/docs/sms-overview), you need at least 100 sms click events in 7 days and at least 1000 sms delivered events in 7 days. - To run the **Likelihood to Engage** algorithm for [Web Push](/docs/web-push-category), you should run the algorithm for at least 21 days. The total page view event count should be above 1000 in 7 days, the Web Push click event count should be above 100 in 7 days, and the Web Push view event count should be above 1000 in 7 days. - To run the **Likelihood to Engage** algorithm for [WhatsApp](/docs/whatsapp-category), the required WhatsApp click event count should be at least 100 in 7 days, and the WhatsApp delivered event count should be at least 1000 in 7 days. - To run the **Likelihood to Engage** algorithm for [App Push](/docs/app-push), the following conditions must be met: a total of at least 1000 page views and push delivered events, at least 100 push sessions on events, and the algorithm must run for a minimum of 21 days. ## Use Cases The User Engagement model enables precise campaign strategies: - Target highly engaged users with an upsell or cross-sell offer. - Re-engage low-engagement users with tailored content or incentives. - Adjust communication frequency and content based on predicted responsiveness. ## Routing users to their best channel User Engagement predicts openness across channels: Email, Web Push, App Push Notification, [SMS](/v1/docs/sms-overview), and [WhatsApp](/v1/docs/whatsapp-overview). The same per-channel engagement-probability signals can be used to select the single channel a user is most likely to respond to, rather than messaging every channel. In Architect, the [Next Best Channel](/v1/docs/what-is-next-best-channel) element reads each user's per-channel engagement-probability scores, filters to the channels the user is reachable on, and routes the user down the path for their highest-scoring channel. Users with no engagement signal follow a fallback channel if one is configured; otherwise, they are dropped at that step. Because it reuses the same engagement model used by User Engagement segments, a single journey can adapt its delivery channel for each user without you maintaining separate channel logic. ## Related - [Predictive Segments](/audience-predictive-segments.md) - [Use Predictive Segments](/use-predictive-segments.md)