This feature leverages prior engagement history to determine the optimal time to send messages to your recipients.
How does it work?
MessageGears automatically calculates optimal send times for each member of your audience across all available channels using your historical customer engagement data and our predictive modeling. We update customer models daily, ensuring you’re adapting to changes in customer behavior.
The modeling process leverages previous messages sent combined with open and click engagement data to identify when customers are most likely to interact with specific campaigns and channels, providing the optimal window to reach out to each individual. The most up-to-date process also accounts for industry specific changes, such as Apple’s Mail Privacy Protection, filtering out automated behaviors, and keying on actual customer interactions where possible.
The model produces a time range value for each recipient, which you can then use to schedule campaign send times or create audiences.
Requirements
- Email and channel engagement data, such as clicks, opens, sends, and push interactions:
- Data Input Schemas for Transaction/conversion data
Schema
MG Field Name | Type | Range | Description |
RecipientId | VARCHAR | The unique customer identifier, the key to join back to customer tables. | |
ML-OptDay | INT | 0-6 | Optimal day of week to send. |
ML-OptHour | INT | 0-23 | Optimal time of day to send. |
ML-OptChan | VARCHAR | email, push, sms | Optimal channel to send. |
EmailSendTime | TIMESTAMP | 2019-06-20 16:00:00-04:00 | Optimal date/time to send email. |
PushSendTime | TIMESTAMP | 2019-06-20 16:00:00-04:00 | Optimal date/time to send push. |
SmsSendTime | TIMESTAMP | 2019-06-20 16:00:00-04:00 | Optimal date/time to send SMS. |
ML-OptDayGroup | CHAR(1) | C,R,O | C - Control group R - Random/Training O - Optimized |
ML-OptHourGroup | CHAR(1) | C,R,O | C - Control group R - Random/Training O - Optimized |
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