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In-session recommender
Decide which product features to highlight for each user as they browse, based on their adoption patterns and preferences.
For:
User Engagement Specialists
Increases user engagement
by highlighting relevant features based on user behavior.
Reduces churn
by keeping users engaged and satisfied with personalized experiences.
Enhances feature adoption
through targeted recommendations.
Boosts ROI
by leveraging machine learning without the need for extensive resources.
How it works
- Navigate to the Next Best Action section in Intempt and select "Create model"
- Create segments based on the model output
- Set up peronalizations
- Use the segments created from the NBA model as the targeting criteria
- Monitor and optimize results
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Features used
Metrics that move
- Increase in feature adoption rates
- Enhancement in user engagement metrics
- Reduction in user churn rate
- Improvement in user satisfaction scores
- Higher ROI through targeted, real-time recommendations
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Guides - get started
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