About Session
Most AR workflows still follow static outreach playbooks—calls at set intervals, reminders on a fixed cadence. But this blanket approach leads to inefficiencies:
- Customers are contacted too early or too often.
- High-risk accounts get the same treatment as reliable payers.
- Attribution is unclear—what nudge caused the payment?
As AR teams increasingly look to AI to drive efficiency and improve outcomes, the question is no longer if automation can help, but how to deploy it intelligently. Our data science team analyzed real-world behavioral data to find ways to optimize collection outreach, boosting recovery rates while reducing manual effort and unnecessary customer contact.
In this session, we will dig into the research and provide attendees with practical ways to modernize collections processes for better outcomes.
Learning Objectives
- Understand how timing and attribution impact collection effectiveness.
Learn why certain outreach actions (calls, reminders) are most effective at specific intervals and how to measure their true impact using attribution windows.
- Discover how segmentation improves outcomes in AR outreach.
Explore how behavioral data and BuyerSense segmentation help tailor outreach strategies to different risk profiles—boosting recovery rates while reducing unnecessary contact.
- Learn how AI can generate optimized outreach procedures at scale.
See how Billtrust’s AI models analyze customer behavior and historical effectiveness to automatically recommend personalized action sequences that maximize collections and minimize manual effort.
