On the estimation of the true demand in call centers with redials and reconnects

In practice, in many call centers customers often perform redials (i.e., reattempt after an abandonment) and reconnects (i.e., reattempt after an answered call). In the literature, call center models usually do not cover these features, while real data analysis and simulation results show ignoring them inevitably leads to inaccurate estimation of the total inbound volume. Therefore, in this paper we propose a performance model that includes both features. In our model, the total volume consists of three types of calls: (1) fresh calls (i.e., initial call attempts), (2) redials, and (3) reconnects. In practice, the total volume is used to make forecasts, while according to the simulation results, this could lead to high forecast errors, and subsequently wrong staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh and what fractions of the calls are redials and reconnects. Motivated by this, we propose a model to estimate the number of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empirically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estimations of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data analysis and simulation

On the estimation of the true demand in call centers with redials and reconnects

In practice, in many call centers customers often perform redials (i.e., reattempt after an abandonment) and reconnects (i.e., reattempt after an answered call). In the literature, call center models usually do not cover these features, while real data analysis and simulation results show ignoring them inevitably leads to inaccurate estimation of the total inbound volume. Therefore, in this paper we propose a performance model that includes both features. In our model, the total volume consists of three types of calls: (1) fresh calls (i.e., initial call attempts), (2) redials, and (3) reconnects. In practice, the total volume is used to make forecasts, while according to the simulation results, this could lead to high forecast errors, and subsequently wrong staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh and what fractions of the calls are redials and reconnects. Motivated by this, we propose a model to estimate the number of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empirically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estimations of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data analysis and simulation

On the Estimation of the True Demand in Call Centers with Redials and Reconnects

Real data analysis and simulation results show that call centers have the fea- tures of redial (re-attempt after an abandonment) and reconnect (re-attempt after an answered call), and ignoring them would lead to inaccurate estima- tion of the total inbound volume. Thus, we propose a queueing model that includes both features. In such a model, the total volume consists of the fresh calls (initial call attempts), the redials and reconnects. In practice, the total volume is used to make forecasts, while according to the simulation re- sults, this could lead to high forecast errors, and subsequently wrong stang decisions. However, most of the call center data sets do not have customer- identity information, which makes it dicult to identify how many calls are fresh and what fractions of the calls are redials and reconnects. Motivated by this, we propose an estimation model to estimate the num- ber of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empir- ically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estima- tions of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data and simulation

On the Estimation of the True Demand in Call Centers with Redials and Reconnects

Real data analysis and simulation results show that call centers have the fea- tures of redial (re-attempt after an abandonment) and reconnect (re-attempt after an answered call), and ignoring them would lead to inaccurate estima- tion of the total inbound volume. Thus, we propose a queueing model that includes both features. In such a model, the total volume consists of the fresh calls (initial call attempts), the redials and reconnects. In practice, the total volume is used to make forecasts, while according to the simulation re- sults, this could lead to high forecast errors, and subsequently wrong stang decisions. However, most of the call center data sets do not have customer- identity information, which makes it dicult to identify how many calls are fresh and what fractions of the calls are redials and reconnects. Motivated by this, we propose an estimation model to estimate the num- ber of fresh calls, and the redial and reconnect probabilities, using real call center data that has no customer-identity information. We show that these three variables cannot be estimated simultaneously. However, it is empir- ically shown that if one variable is given, the other two variables can be estimated accurately with relatively small bias. We show that our estima- tions of redial and reconnect probabilities and the number of fresh calls are close to the real ones, both via real data and simulation

Improvement business communications in banking by optimization of contact centres

S obzirom na brojne i značajne tehnološke promene koje karakterišu savremeno poslovanje, postalo je neophodno da se unapređuje i poslovna komunikacija. Da bi se unapredila poslovna komunikacija, između ostalih referentnih inovacija, organizuju se kontakt centri. Organizacija kontakt centra ima ključnu ulogu u obezbeđivanju njegove produktivnosti i efikasnosti, jer na osnovu nje klijenti i potencijalni klijenti ocenjuju ne samo rad kontakt centra, nego i rad banke, kao i njenih usluga i proizvoda. Da bi se izvršila optimizacija rada kontakt centra, potrebno je da se identifikuju sledeće promenljive i pojave: tip kontakt centra, tehnologija, kanali komunikacije, zaposleni i njihova obuka, zatim, potrebno je da se odredi potreban broj zaposlenih, ali i da se zaposleni pravilno rasporede po smenama, kao i da se prati njihov rad. Stavljanjem u optimalnu korelaciju navedenog, utiče se na poboljšanje poslovne komunikacije. Početkom pedesetih godina prošlog veka počeli su da se formiraju prvi kontakt centri i do danas bilo je dosta transformacija koje su uticale i na poslovnu komunikaciju. Poslovna komunikacija na više načina može da se unapređuje različitom organizacijom kontakt centara.Many significant tehnological changes describe modern business. It is necessary to improve business communication with the help of referential innovation i.e. contact centers. The contact center has an important role in securing productivity and effectiveness because clients and potential client evaluate the contact center, bank`s work services and products. In order to optimize the contact center`s work, certain variables must be identified e.g. the type of contact center, technology, channels of communication, employees and their training, the number of employees, shift working, supervision over employees. All of the issues mentioned above have great influence on business communication. In the early fifties of the last century began to form the first contact centers, and there have been a lot of transformations that have affected to the business communication. Business communication in many ways can promote contact centers with different organization