Gauge Your Einstein Engagement Scoring Data Quality

Check your mobile app’s data quality score for insight about the engagement data that Einstein uses to predict your customers’ likelihood to engage. Data quality scores for Einstein Engagement Scoring are generated at the mobile app level only.

Each mobile app’s data quality score is generated by analyzing the app’s engagement data. This analysis determines a Pattern Predictability score and a Data Maturity score.

• To determine the Pattern Predictability score, Einstein analyzes the distinctness of your customers’ engagement patterns with your mobile app. Greater pattern predictability leads to more accurate predictions of future engagement behavior. When your messages are more compelling and drive higher engagement rates, your Pattern Predictability score is higher. When your engagement rates are lower, your Pattern Predictability score is lower. Pattern Predictability differs from engagement rate because it evaluates engagement events as they correlate to one another throughout your customers’ engagement history.
• To determine the Data Maturity score, Einstein analyzes the length of time that each of your customers has had data available to make predictions, and how much data is available. This score helps Einstein understand how sensitive your subscribers are to changes in your sending patterns over time. When you have a greater history of data for each of your customers, your Data Maturity score is higher.

To find your data quality score, navigate to the Einstein Engagement Scoring for MobilePush dashboard. To see your scores and tips that can help raise your data quality score, click Data Quality Score.

Note Transactional sends and test sends are excluded when generating your data quality score.