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Engagement Index

The purpose of MessageGears’ Engagement Index model is to predict your customers' likelihood to engage with your brand.

 

How does it work?

This model evaluates your customers' engagement across available channels and generates a score representing their predicted level of engagement with your brand. The model’s output is a normalized SCORE that ranks customers with a range from 100 (most likely) to 1 (least likely) to engage. A customer with a score of 99 is considered more likely to engage than a customer with a score of 75, who in turn more likely to engage than a customer with a score of 25. You can then use the engagement score in segmentation or personalization for a wide variety of marketing use cases.

 

Requirements

 

Schema

MG Field Name Type Range Description
RecipientId varchar Variable The unique customer identifier, the key to join back to customer tables.
ML-EngagedChan varchar   (email, push, SMS)
ML-EngagedChanAddr varchar 96 Hashed email address, SMS number, or device ID.
ML-EngagedChanScore Int 3 For example, 0-100
ML-EngagedLow BOOL 0 or 1 Predefined segments.
Our recommended audience definitions today break out high/medium/low, where the top quintile = high; mid three quintiles = medium; bottom quintile = low. However, we can customize this during implementation.
ML-EngagedMedium BOOL 0 or 1
ML-EngagedHigh BOOL 0 or 1
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