Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now. To understand this tension, we propose a multiserver queueing model with two customer classes: moderate and urgent. We allow customers to transition classes while waiting. In this setting, we characterize how moderate and urgent customers should be prioritized for service when proactive service for moderate customers is an option. We identify an index, the modified [Formula: see text]-index, which plays an important role in determining the optimal scheduling policy. This index lends itself to an intuitive interpretation of how to balance holding costs, service times, abandonments, and transitions between customer classes.
This paper was accepted by David Simchi-Levi, stochastic models and simulation.
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Experience: towards automated customer issue resolution in cellular networks
Cellular service carriers often employ reactive strategies to assist
customers who experience non-outage related individual service
degradation issues (e.g., service performance degradations that do
not impact customers at scale and are likely caused by network provisioning
issues for individual devices). Customers need to contact
customer care to request assistance before these issues are resolved.
This paper presents our experience with PACE (ProActive customer
CarE), a novel, proactive system that monitors, troubleshoots and
resolves individual service issues, without having to rely on customers
to first contact customer care for assistance. PACE seeks to
improve customer experience and care operation efficiency by automatically
detecting individual (non-outage related) service issues,
prioritizing repair actions by predicting customers who are likely
to contact care to report their issues, and proactively triggering
actions to resolve these issues. We develop three machine learning-based
prediction models, and implement a fully automated system
that integrates these prediction models and takes resolution actions
for individual customers.We conduct a large-scale trace-driven evaluation
using real-world data collected from a major cellular carrier
in the US, and demonstrate that PACE is able to predict customers
who are likely to contact care due to non-outage related individual
service issues with high accuracy. We further deploy PACE into this
cellular carrier network. Our field trial results show that PACE is
effective in proactively resolving non-outage related individual customer
service issues, improving customer experience, and reducing
the need for customers to report their service issues.
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- NSF-PAR ID:
- 10197153
- Date Published:
- Journal Name:
- MobiCom ’20, September 21–25, 2020, London, United Kingdom
- Page Range / eLocation ID:
- 610-622
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3372224.3419203
