Perspectiva de la dependencia y el subdesarrollo

Summary: Companies are increasingly relying on data to predict consumer behavior in a variety of applications such as demand forecasting, revenue management, and advertising. One way that companies could use their data is by predicting caller abandonment and redialing behavior. Given that predicting caller abandonment and redialing behavior can increase delay announcement accuracy, improve call volume forecasting, and be used to better prioritize callers, managers may be interested in determining which prediction method they should use, what types of caller data they should collect to generate their predictions, and how the accuracy of their predictions will impact the above applications (delay announcement, volume forecasting, and prioritization). In this preliminary study we begin answering these questions by training several machine learning classifiers to predict caller abandonment and redialing behavior using caller history.

We briefly summarize the findings of our preliminary study. We first identify several trends that demonstrate correlation between callers’ current abandonment and redialing behavior and their history of waiting times, abandonment decisions, and intercontact times. Based on these correlations, we propose a framework to use various methods for predicting caller abandonment and redialing behavior based on their history. Specifically, we frame the problem of predict- ing caller abandonment and redialing behavior as a probabilistic classification problem, where classifiers generate the probability of callers abandoning in a given period (redialing after aban- donment) based on a rich set of features. We train several classifiers including k-nearest neighbor voting, decision trees, logistic regression, and 6 different configurations of neural networks. We then measure the performance of each classifier using the AUC of their ROC curves and find that various neural network configurations provide the best predictions. However, we also find that logistic regression and decision trees predict nearly as well, indicating that managers of this

call center could predict caller redialing and abandonment behavior using simpler algorithms. Furthermore, for each type of classifier that we train the average AUC is highest for the calls that have the full set of history features, indicating that managers who collect caller history data can substantially improve the accuracy of their redialing and abandonment predictions. Finally, we validate that our classifiers provide good predictions on a test dataset, as evidenced by their high lift values, and show that our classifiers can also be used to accurately predict call center performance measures during the test set.

Future Work: Going forward, we intend to supplement this preliminary study in three ways. First, while the dataset in this study contains only caller history, we intend to obtain a dataset with a various sources of caller data and train our classifiers using various combi- nations of the data sources. Examples of additional data sources include the callers’ account type, demographic information, the options they selected in the phone tree, and their history of interactions with other service channels such as email and chat. Given a dataset with this richer set of features, we should be able to determine which features are most critical in gener- ating accurate predictions, and hence which features managers should invest effort in collecting. Second, while we have trained k-nearest neighbor, decision tree, logistic regression, and neu- ral network classifiers, there are several other prediction methods we intend to train. These include Probit models, support vector machines, naive Bayes, random forests, linear discrimi- nant analysis, various boosting algorithms as well as the mixed-logit models of Hathaway et al. (2019b) and the structural model of Emadi and Swaminathan (2018). By expanding the set of classifiers, we can determine which prediction methods provide the most accurate predictions and by comparing their relative performance managers can determine whether it is worthwhile to train complex classifiers or to train more parisimonious classifers. Third, we discussed how managers could predict caller abandonment and redialing behavior to generate more accurate call volume forecasts, provide more accurate delay announcements, and better prioritize callers. Going forward, we intend to quantify the impact of prediction accuracy on these various appli- cations. For example, Hathaway et al. (2019b) demonstrated with their mixed-logit models that prioritizing callers based on their predicted abandonment and redialing behavior can reduce av- erage waiting times and increase opportunities for agents to sell additional products. Using our

framework, we should be able to predict call center performance measures under the same set of prioritization policies as Hathaway et al. (2019b), but under a variety of prediction methods and data sources. Hence, managers could compare the costs and benefits of implementing a particular prediction method or collecting a particular data source.

Finally, with respect to future research in this stream, our framework could also be used to help managers of other service systems determine the value of using various prediction methods and collecting various data sources to predicting consumer abandonment and returning behavior. For example, restaurants could use our framework to determine the best method for predicting how long customers will be willing to wait to be seated and whether they will return for service if they leave the lobby. Based on these predictions, they may be able to provide customers with better estimated seating times, better predict future customer traffic, and better prioritize customers.

CHAPTER 5

CONCLUSION AND FUTURE RESEARCH

Call centers are increasingly relying on innovations to improve customer service, drive down operating costs, and increase sales revenue. In this dissertation we conduct data-driven studies of caller behavior under call center innovations in three areas including new technology, new types of data about customers, and new predictive methods that can be applied to customer data. Specifically, we study caller behavior under callback technology, how call centers can use customer history to improve their performance measures, and how call centers can use a variety of prediction methods to better predict caller abandonment and redialing behavior. Through our studies we have contributed to the understanding of how callers will behave under these innovations and what drives their decision-making process. Furthermore, we have quantified the impact of each of the innovations on operational performance measures such as service quality, average waiting times, system throughput, sales opportunities, and prediction accuracy. Finally, we have provided normative policy guidance for managers who are interested in implementing these new innovations. We briefly summarize the findings, managerial insights, and limitations from each study, and then discuss future research opportunities.

In our first chapter we use a structural estimation approach to impute caller preferences under callback technology. We find that, after controlling for their expected waiting times in each channel, callers generally prefer the traditional phone channel. However, we also find that their per unit online waiting cost is 3 to 6 times higher than their offline waiting cost, meaning that callers experience far more discomfort while waiting on the phone than while waiting for a callback to arrive. Hence, managers can improve service quality by substituting offline waiting time for online waiting time. Using the imputed caller preferences, we predict how callers would behave and collect call center performance measures under a variety of callback policies. We find that offering to hold callers’ spot in line or call back within a short window improves service

quality by decreasing their average incurred waiting cost. However, under high loads offering to call back within a long window actually decreases service quality in this call center, indicating that managers who are interested in implementing callback technology should carefully consider their callback policies. Moreover, we find that offering callbacks as a demand postponement strategy during periods of temporary congestion substantially increases service quality and system throughput, and that offering callbacks under a variety of policies in call centers that operate at a smaller scale can also improve service quality and system throughput. Hence, callback technology could be particularly valuable for call centers that routinely experience temporary periods of congestion and for call centers that operate at a smaller scale. Finally, one of the limitations of our study is that we do not determine the optimal policies under callback technology. Rather, we test some of the most popular callback policies observed in industry. Hence, a future study that uses a data-driven optimization approach to determine the optimal callback policy could be valuable.

While our first chapter provides insights regarding customer behavior under callback tech- nology in a call center with queues invisible to customers, we see opportunities to conduct several other studies regarding customer behavior under various technology-driven customer service innovations, including settings where queues are visible to customers. For example, restaurants often offer customers a pager or text to notify them when their table is available, allowing them to avoid waiting in the lobby. Hence, the restaurant is offering an offline waiting option similar to the callback option that we studied. Understanding how customers choose whether to balk, accept the pager/text offer, or wait in the lobby could help managers determine when they should offer the notification. Similarly, the healthcare industry could benefit from offline waiting options, as it would allow patients to perform other activities while waiting for care and could reduce waiting room congestion. Furthermore, in addition to offering callbacks, call centers are increasingly offering customers additional service channels such as email and chat. Managers may be interested in how customers choose between phone and non-phone channels, how non-phone channels affect call center performance measures, and under what cir- cumstances they should offer customers the respective channels. Given a dataset that includes customer decisions in these various channels, the structural model that we formulate in the first

chapter could be extended to impute customer preferences under each channel. These prefer- ences could then be used to predict customer behavior and call center performance measures under various service channel policies.

In our second chapter we study how managers can use caller history to predict caller aban- donment and redialing behavior, and use the predictions to prioritize callers using history-based priority policies. To predict caller behavior under history-based priority policies, we formulate and estimate the parameters of mixed-logit models of caller abandonment and redialing behav- ior, where we assume that callers belong to two distinct latent segments. Our estimates reveal that callers differ intrinsically across segments in their behavior and that history (callers’ previ- ous waiting times and abandonment decisions) impacts the current behavior of callers in both segments. Hence, managers can use caller history to predict their current abandonment and redialing behavior. We simplify the design of history-based priority policies by proposing the Combination of Abandonment and Redialing Probabilities Policy Class (CARP) and predict how callers would behave under a variety of policies from the class. We find that, relative the first-come, first-served policy, certain CARP policies can reduce callers’ average waiting time and/or increase opportunities for agents to cross-sell additional products. Hence, managers can exploit caller history data to improve service quality and revenue-generating opportunities. We find two limitations of this study. First, while our mixed-logit models can be used to predict caller abandonment and redialing behavior, there are a number of other predictive analytics techniques (such as machine learning methods) that could be used for prediction. Second, while our model relies on caller history data to generate its predictions, there are several additional sources of customer data, such as demographics and account status, that could be used to generate more accurate predictions.

We see several ways to extend the work of our second chapter. First, while we have shown that history can be used to predict caller abandonment and redialing behavior, it may also be predictive of other behaviors. For example, managers may be able to use caller history to predict whether callers will be receptive to sales attempts, and, if so, which product they will be most likely to accept. Moreover, managers may be able to increase revenue by prioritizing callers based on their predicted sales receptiveness. Second, the approaches we use to study caller abandonment and redialing behavior could also be used to study customer behavior in

other service settings. For example, healthcare managers could use our framework to predict how long patients will wait before abandoning from emergency rooms. Patient data such as the severity of the condition and their medical history could be used to generate the predictions. Restaurant managers could also benefit by predicting customer abandonment and returning behavior. Specifically, by predicting how long customers will be willing to wait to be seated and whether they will return if they leave the lobby, managers may be able to provide better estimated seating times, better prioritize which customers to seat, and better predict future traffic.

The purpose of our third study is to explore how managers can use a variety of new predictive analytics methods and various sources of customer data to better predict caller abandonment and redialing behavior. In this chapter we perform a preliminary study of this topic by training several machine learning classifiers (including k-nearest neighbor voting, decision trees, logistic regression, and various neural networks) to predict caller abandonment and redialing behavior based on their history. We find that various neural network configurations provide the best prediction accuracy, but that logistic regression and decision trees provide nearly comparable prediction accuracy. Hence, managers may be able accurately predict caller abandonment and redialing behavior using a variety of methods. Furthermore, we find that caller history substantially improves the prediction accuracy of each prediction method. Hence, managers who are interested in predicting caller abandonment and redialing behavior using caller history should collect as much caller history data as possible. As this is a preliminary study, there is future work to be done on this topic. Specifically, we intend to supplement this study with more sources of customer data (such as their account type, their demographics, what service they are requesting, and their history of interactions with other non-phone service channels) and test more prediction methods (such as support vector machines, naive Bayes, and random forests). Thus, we can quantify the impact of using various prediction methods with a variety of customer data sources. Furthermore, we intend to quantify the impact of prediction accuracy on various applications such as delay announcements, call volume forecasting, and prioritization policies. Hence, managers can make better-informed decisions regarding which data sources they should collect and which prediction methods they should use.

Finally, while this dissertation focuses on callers’ callback, abandonment, and redialing deci- sions, there are other caller behaviors that could be interesting to study. For example, managers may be interested in understanding the caller decision-making process as they navigate through the phone tree. Understanding caller behavior in the phone tree could help managers better design their menu options. Furthermore, managers may want to better predict how callers will behave under various messages they receive during the waiting process. This could help managers assess the importance of providing accurate information and determine what infor- mation they should provide. Lastly, managers may also be interested in predicting caller service times or first-call resolution probabilities under each available agent in the system. This could help managers more intelligently match callers with agents to drive down average waiting times and/or increase first-call resolution rates. In short, we see a number of opportunities to conduct future studies of caller behavior under customer service innovations.

APPENDIX A

DON’T CALL US, WE’LL CALL YOU: AN EMPIRICAL STUDY OF CALLER BEHAVIOR UNDER A CALLBACK OPTION