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

Abstract

a b s t r a c tIn 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.

Estimation of the Performance Measures of a Group of Servers Taking into Account Blocking and Call Repetition before and after Server Occupation

“…There are several approaches to dealing with this problem; see for example [12]. A substantial contribution to the formation of a backlog, especially in the case of overload, is repeated attempts that reflect the quality of request servicing has been observed in the past [13].…”

Staffing many‐server queues with autoregressive inputs

“…For example, when a firm sends notification letters/emails to its group of customers, or runs a promotion on social media, there could be a temporary surge in the number of calls for the purpose of inquiry; in call centers designated for emergency services, a common event (e.g., a wide‐scale power outage) may trigger bursts of calls within a short period of time, resulting in a much higher call volumes over that time duration. Redials and reconnects may also give rise lead to a certain degree of dependency in call arrival counts (see Ding et al, 2015). These features of dependencies observed in practice serve as the primary motivation for this work.…”

Call Center Arrivals: When to Jointly Forecast Multiple Streams?

“…A lot of progress has been made recently in the forecasting literature to better incorporate interday and within-day dependence, as reviewed in Ibrahim et al (2016). However, almost all the work is about forecasting a single arrival stream, including Mabert (1985), Avramidis et al (2004), Brown et al (2005), Weinberg et al (2007), Taylor (2008), Shen and Huang (2008a,b), Aldor-Noiman et al (2009), Matteson et al (2011), Taylor (2012), Gans et al (2015), Ding et al (2015), and the earlier works referenced in these papers.…”