The Gatekeeper’s Dilemma: “When Should I Transfer This Customer?”

When Should I Transfer This Customer? “Please hold while I transfer you to next level of support.” Most of us have been on the receiving end of this message. In this study, the authors look at transfers from the service worker’s perspective. They create an online experiment in which participants play the role of call center agents who need to decide whether to transfer a virtual service request or continue attempting to resolve it. Consistent with compensation schemes common in call centers, participants receive a bonus for each successful resolution and may pay a penalty if they transfer. The authors find that these incentives generally work well; however, agents appear to overreact to transfer penalties by handling more requests than they should and transferring too few requests. Although this may be good news for customers who dislike being transferred, such behaviors may be costly for the call center; thus, managers need to be careful when rolling out complex compensation schemes.

The Speech Act of Apology by Filipino Call Center Agents

The Philippines is a global leader in business process outsourcing (BPO). Many foreign investors view the Philippines as a viable location for their call center operations due to the Filipinos' strength in English proficiency. This study focuses on inbound call center accounts that deal with a variety of call situations, ranging from information requests to difficult calls that require more time to handle, such as complaint calls. Since the goal of any business is customer satisfaction, this research aims to investigate how Filipino call center agents mitigate and reduce the liability and guilt towards customers. Results show that the 90 call center representatives have successfully produced 'perfect apologies' by providing all five strategies posited by Cohen et al. (1986) in most of their complaint calls. However, the sequence is distorted by emphasizing more on offering a repair. This leads to a recommendation that calls center training on apology be emphasized on building personal connections rather than a mechanical response to situations.

Quality assessment of the contact center while implementation the IP IVR system by using teletraffic theory

The paper considers the form of taking into account the specialization of information needs. An analysis of the work of modern call centers has been carried out. The authors noted the effectiveness of using IVR devices, operators, and consultants for differentiated customer service and the need to take feedback into account when forming the revenue stream of applications. The models make it possible to determine the leading indicators of the quality of service for applications arriving at the call center. Formal expressions for descriptions are derived from the input parameters' values and the model's stationary probability. The relationships between the characteristics of the call center that regulate the intensity of incoming and outgoing calls, call processing through 3CXPhone, corporate mail, and social networks were obtained using Global Statistic. The developed methodology for organizing information and reference systems makes it possible to consider modern trends in the development of call centers. The paper presents the results of research using the IP IVR system. The results of calculating service characteristics are given for two different types of calls with mixed order ω=(0.5; 0.7; 0.9). The presented results were obtained by using experimental data of the JSC Kazakhtelecom's call center. For the calculations, the authors used the formulas of the teletraffic theory for a mixed service system. It also assesses the extent of combined service model effects for the contact center's call quality. It is shown that the probability of lost calls depends on the incoming load. The obtained results show that the mixed order for incoming calls servicing affects the probability of service failure.

Impact of Outsourcing on Effectiveness of Call Center Services

Call center is the frontline of communicating with customers. This paper reports the findings of a survey on service effectiveness of call center operations of mobile communication service providers in Turkey that either use in-house or outsourcing method. The primary objective of this paper is to explore how the customers of mobile communications service provider companies perceive the inbound call center processes for speed, easiness of use, first call resolution, effective inquiry and in doing that what qualities they display. For this purpose a survey was conducted with the customers who use call centers either to make an inquiry or to complain about the services. The aims were: to specify the typical behavior of mobile communications service users; how the firms handle inquiries or complaints; if they solve the problems for good and in what time or how long it takes to get an answer practically useful; to reveal the speed and timeliness of the processes. The survey was conducted with the mobile phone users in a highly populated area of Istanbul.

Experiencia de la estrategia implementada por los equipos de respuesta rápida y seguimiento clínico para reducir la letalidad por COVID-19, Lambayeque-Perú 2020

Introducción: El Perú fue considerado la mayor tasa de letalidad por COVID-19 (9.39%); siendo Lambayeque en julio del 2020 presentó seroprevalencias más altas reportadas en el mundo para SARS-CoV-2 con 29,5%. Objetivo. Describir la experiencia de la estrategia implementada por Equipos de Respuesta Rápida y Seguimiento Clínico de Casos de COVID-19 (ERSC) para reducir la letalidad por COVID-19 durante la primera ola en el 2020. Descripción de la estrategia. Se realizó una investigación operativa en una cohorte retrospectiva. La población estudiada estuvo conformada por los casos COVID-19 sospechosos y confirmados durante el periodo abril-diciembre 2020, atendidos en la Red Asistencial de Lambayeque. La metodología se llevó a cabo bajo la Implementación y funcionamiento de los ERSC que realizaban la Vigilancia Epidemiológica, rastreo y seguimiento clínico domiciliario de los casos sospechosos y confirmados de COVID-19. El proceso de captación y tamizaje inicial de los pacientes se realizó mediante tres procesos: tamizaje diferenciado, “call center” y vigilancia de rumores. Tras el tamizaje, se categorizaba al paciente en base a la atención que requería. Los casos eran asignados a los equipos de respuesta rápida quiénes acudían al domicilio del paciente para realizar las actividades indicadas de acuerdo al tipo de brigada. Las brigadas eran cuatro: brigadas de evaluación clínica domiciliaría, de diagnóstico o laboratorio a domicilio, de seguimiento clínico y brigada topo. c. La letalidad en pacientes moderados/severos disminuyó de 60% (Semana Epidemiológica 19) a 10% (Semana Epidemiológica 50) (p<0,001). La estrategia implementada y ejecutada redujo la letalidad por COVID-19 en población atendida.

A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector

The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A literature analysis on applying such methods in this particular area is presented. As a direct result of the described research, a universal scenario for forecasting various non-stationary time series in automatic mode was developed. The developed scenario for solving specific banking tasks to improve business efficiency, including optimizing demand for ATMs, forecasting the load on the call center and cash center, is considered. A machine learning methodology in economics that can yield robust and reproducible results and can be reused in solving other similar tasks is described. The methodology described in the article was tested on three cases and showed the ability to generate models that are superior in accuracy to similar predictive models described in the literature by at least three percentage points. This article will be helpful to specialists dealing with the problem of forecasting economic time series and students and researchers due to a large number of links to systematic literature reviews on this topic.

Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

Modern call centers require precise forecasts of call and e-mail arrivals to optimize staffing decisions and to ensure high customer satisfaction through short waiting times and the availability of qualified agents. In the dynamic environment of multi-channel customer contact, organizational decision-makers often rely on robust but simplistic forecasting methods. Although forecasting literature indicates that incorporating additional information into time series predictions adds value by improving model performance, extant research in the call center domain barely considers the potential of sophisticated multivariate models. Hence, with an extended dynamic harmonic regression (DHR) approach, this study proposes a new reliable method for call center arrivals’ forecasting that is able to capture the dynamics of a time series and to include contextual information in form of predictor variables. The study evaluates the predictive potential of the approach on the call and e-mail arrival series of a leading German online retailer comprising 174 weeks of data. The analysis involves time series cross-validation with an expanding rolling window over 52 weeks and comprises established time series as well as machine learning models as benchmarks. The multivariate DHR model outperforms the compared models with regard to forecast accuracy for a broad spectrum of lead times. This study further gives contextual insights into the selection and optimal implementation of marketing-relevant predictor variables such as catalog releases, mail as well as postal reminders, or billing cycles.

Author Profiling in Informal and Formal Language Scenarios Via Transfer Learning

The interest in author profiling tasks has increased in the research community because computer applications have shown success in different sectors such as security, marketing, healthcare, and others. Recognition and identification of traits such as gender, age or location based on text data can help to improve different marketing strategies. This type of technology has been widely discussed regarding documents taken from social media. However, its methods have been poorly studied using data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks and a transfer learning strategy to recognize two demographic traits, i.e., gender and language variety, in documents written in informal and formal language. The models were tested in two different databases consisting of tweets (informal) and call-center conversations (formal). Accuracies of up to 75 % and 68 % were achieved in the recognition of gender in documents with informal and formal language, respectively. Moreover, regarding language variety recognition, accuracies of 92 % and 72 % were obtained in informal and formal text scenarios, respectively. The results indicate that, in relation to the traits considered in this paper, it is possible to transfer the knowledge from a system trained on a specific type of expressions to another one where the structure is completely different and data are scarcer.

Experiência extensionista durante a pandemia de COVID-19

A extensão universitária como elo entre o ensino e a ação comunitária vem de encontro ao modelo atual de ensino ao estudante da área da saúde, rompendo com aquele antes centralizado no docente e na transmissão do conhecimento. A experiência prática proporcionada pela extensão transforma o estudante, antes passivo, em agente ativo no processo de aprendizagem e construção de experiências e de um pensamento social. O projeto de extensão “Prevenção, cuidados e enfrentamento à pandemia do novo coronavírus na região metropolitana de Curitiba” contou com a participação de cinco extensionistas. Realizado no segundo semestre de 2020, permitiu aos discentes a prática na atenção primária à saúde da rede do SUS e a aproximação com mais de 1800 pacientes, tanto remotamente, através da atuação em Call Center da SMS de Curitiba, quanto presencialmente, com o evento “Saúde na Estrada”, pareceria entre universidades, forças policiais e a SMS de Curitiba. Este relato objetiva descrever as ações realizadas pelos extensionistas durante suas atuações na atenção primária à saúde através da extensão universitária, bem como as contribuições que tal prática acadêmica possibilitou na formação profissional e humanizada dos discentes.