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StirrPlanner produces daily schedules for traveling staff that are designed to maximize the number of appointments while minimizing the distance traveled. This solution meets the challenges of location-based appointment scheduling by utilizing AI and live location awareness of your travelling work force.
This is of particular importance now as in-home services experience significant growth. Such companies must set appointment times that are acceptable to the member and fit within the roving staff itineraries.
With StirrPlanner, a call center agent speaks directly with a customer whose address is automatically displayed on his or her monitor.
The agent is shown available time slots within the next several days which have been calculated to be logistically efficient for the traveling staff and thereby achieving an optimized travel day.
Benefits include more appointments, reduced mileage reimbursements, and fewer reschedules.
Simple and self-managed
Remote employee monitoring
No need for an app
Webpage-based
No login or passwords necessary
Self-reporting
Reminder if not checked in
Employers monitor each worker daily
Dashboard to monitor reports
See flagged workers
Intervene if a worker has concerns
View employee health trends
Immediate feedback
Confidence to report to work
Share health status with coworkers and customers
Your mobile workforce employee has 7, 8, maybe 10 appointments each day. And when you schedule their itinerary their travel is grossly inefficient and much time is wasted behind the wheel. If you could do it better, you might squeeze in another 1 or 2 appointments. Reimbursement for travel would decrease and you’d spend less time rescheduling.
StirrPlanner knows where your mobile worker is located and where they will be.
StirrPlanner also knows the locations of your customers and prospects to allow you to offer appointment times that are efficient for your traveling worker. Days of guessing are over. Looking at maps is unnecessary.
With StirrPlanner, optimizing travel is automatic.
Copyright © StirrData 2021 – All Rights Reserved