There are several factors to consider – where is the consumer located, what kinds of phone numbers do you have for the consumer, and how many different numbers are tied to this account? By training a ML model on an agencies’ historical data, the model can recommend the best time of day to call a particular account. By augmenting this data with external data features, such as census and NAICS data, models can paint a pretty good generalized picture of consumer behavior and provide a better understanding of anticipated schedules. This comprehensive approach ensures you are making the most of your limited calls. (click here to learn more about collection scoring)
One of the great benefits of using machine learning models is that the “best time of day” recommendations is only one of the possible outputs available. That same ML model can also provide:
- Propensity to pay score
- Recommended outreach channel (phone, letter, text, email)
- Expected Value
- Payment option recommendations and suggested payment plan amounts
All of these ML models run behind the scenes and all accounts are ingested into the model and scored daily to ensure that up to date information is incorporated. Accounts are scored, prioritized, and incorporated into your calling strategy.