skip to main content

Title: 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.  more » « less
Award ID(s):
1738981 1717493 1943364 1750953
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
MobiCom ’20, September 21–25, 2020, London, United Kingdom
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. As empirically observed in restaurants, call centers, and intensive care units, service times needed by customers are often related to the delay they experience in queue. Two forms of dependence mechanisms in service systems with customer abandonment immediately come to mind: First, the service requirement of a customer may evolve while waiting in queue, in which case the service time of each customer is endogenously determined by the system’s dynamics. Second, customers may arrive (exogenously) to the system with a service and patience time that are stochastically dependent, so that the service-time distribution of the customers that end up in service is different than that of the entire customer population. We refer to the former type of dependence as endogenous and to the latter as exogenous. Because either dependence mechanism can have significant impacts on a system’s performance, it should be identified and taken into consideration for performance-evaluation and decision-making purposes. However, identifying the source of dependence from observed data is hard because both the service times and patience times are censored due to customer abandonment. Further, even if the dependence is known to be exogenous, there remains the difficult problem of fitting a joint service-patience times distribution to the censored data. We address these two problems and provide a solution to the corresponding statistical challenges by proving that both problems can be avoided. We show that, for any exogenous dependence, there exists a corresponding endogenous dependence, such that the queuing dynamics under either dependence have the same law. We also prove that there exist endogenous dependencies for which no equivalent exogenous dependence exists. Therefore, the endogenous dependence can be considered as a generalization of the exogenous dependence. As a result, if dependence is observed in data, one can always consider the system as having an endogenous dependence, regardless of the true underlying dependence mechanism. Because estimating the structure of an endogenous dependence is substantially easier than estimating a joint service-patience distribution from censored data, our approach facilitates statistical estimations considerably. Funding: C. A. Wu received financial support from the Hong Kong Research Grant Council [Early Career Scheme, Project 26206419]. A. Bassamboo and O. Perry received partial financial support from the National Science Foundation [Grant CMMI 2006350]. 
    more » « less
  3. Historically Black Colleges and Universities (HBCUs) innovators lag behind their non-HBCU counterparts in the commercialization of innovations as they were originally set up as teaching and blue-collar trade institutions. There exists a strong need for education and training to bridge this gap by promoting the commercialization of innovations in HBCUs and thus transform next-generation HBCU innovators into entrepreneurs. HBCUs are promoting entrepreneurial education and mindset via changes in engineering education programs and curriculums. Several federally funded programs like the National Science Foundation (NSF) Center of Research Excellence in Science and Technology (CREST) Center for Nanotechnology Research Excellence (CNRE) are promoting innovation and intellectual property generation at HBCUs. NSF I-Corps Program supports the education and training of innovators about the commercialization of mature or patented innovations at HBCUs. The NSF I-Corps Introduction to Customer Discovery explores strategies in identifying key customer segments through extensive customer interviews, which is a fundamental step in the commercialization process. This paper discusses our educational experience in the customer discovery process for Pumpless Solar Thermal Air Heater (Patent Number 10775058). To learn about prospective customers’ attitudes and perceptions of the innovation, we conducted 30 interviews with potential customers (end users). Our innovation is focused on providing portable, cost-effective, healthy, and environmentally friendly space heating solutions. We tested several hypotheses about the value proposition of our innovation during interviews to explore the market segments for potential commercialization. During the Customer Discovery process, we came to know about new issues such as health issues caused by the dry air in winter. We also learned that mitigation of problems due to the current heating system required a humidifier to reduce health issues that added additional cost. Based on our interviews our innovation is suitable for customers needing: (i) Heating source mitigating health issues, (ii) add-on technology to reduce their heating bills. Our next step is to pursue market segments for our innovation. We plan to utilize the current experience of commercialization of intellectual property to develop training modules for the MECH 302 Undergraduate Research Experience and MECH 500 Research Methods and Technical Communication courses offered under the mechanical engineering program at the University of the District of Columbia (UDC). 
    more » « less
  4. Increased network wide energy consumption is a paramount challenge that hinders wide scale ultra-dense networks (UDN) deployments. While several Energy Saving (ES) enhancement schemes have been proposed recently, these schemes have one common tenancy. They operate in reactive mode i.e., to increase ES, cells are switched ON/OFF reactively in response to changing cell loads. Though, significant ES gains have been reported for such ON/OFF schemes, the inherent reactiveness of these ES schemes limits their ability to meet the extremely low latency and high QoS expected from future cellular networks vis-a-vis 5G and beyond. To address this challenge, in this paper we propose a novel user mobility prediction based AUtonomous pROactive eneRgy sAving (AURORA) framework for future UDN. Instead of observing changes in cell loads passively and then reacting to them, AURORA uses past hand over (HO) traces to determine future cell loads. This prediction is then used to proactively schedule small cell sleep cycles. AURORA also incorporates the effect of Cell Individual Offsets (CIOs) for balancing load among cells to ensure QoS while maximizing ES. Extensive system level simulations leveraging realistic SLAW model based mobility traces show that AURORA can achieve significant energy reduction gain without noticeable impact on QoS. 
    more » « less
  5. Large‐area, long‐duration power outages are increasingly common in the United States, and cost the economy billions of dollars each year. Building a strategy to enhance grid resilience requires an understanding of the optimal mix of preventive and corrective actions, the inefficiencies that arise when self‐interested parties make resilience investment decisions, and the conditions under which regulators may facilitate the realization of efficient market outcomes. We develop a bi‐level model to examine the mix of preventive and corrective measures that enhances grid resilience to a severe storm. The model represents a Stackelberg game between a regulated utility (leader) that may harden distribution feeders before a long‐duration outage and/or deploy restoration crews after the disruption, and utility customers with varying preferences for reliable power (followers) who may invest in backup generators. We show that the regulator's denial of cost recovery for the utility's preventive expenditures, coupled with the misalignment between private objectives and social welfare maximization, yields significant inefficiencies in the resilience investment mix. Allowing cost recovery for a higher share of the utility's capital expenditures in preventive measures, extending the time horizon associated with damage cost recovery, and adopting a storm restoration compensation mechanism shift the realized market outcome toward the efficient solution. If about one‐fifth of preventive resilience investments is approved by regulators, requiring utilities to pay a compensation of $365 per customer for a 3‐day outage (about seven times the level of compensation currently offered by US utilities) provides significant incentives toward more efficient preventive resilience investments. 
    more » « less