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
-
Email engagement data (clicks, opens, sends)
-
Web/Pixel engagement data
- Data Input Schemas for Transaction/conversion data
Schema
<tr">ML-EngagedChanScoreInt3For example, 0-100</tr">
| 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-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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