On the scheduling of operations in a chat contact center

Abstract

Unlike calls, the chat channel allows a contact center agent to simultaneously work with many customers. The benefit of this flexibility can be however challenged by a new abandonment feature during service. In this context, new flow routing questions arise. How many chats should an agent serve? Which agent should be selected? Or may a chat be served by more than one agent? We aim to answer these questions so as to find the best trade-off between time spent in service, queueing delay and abandonment.For this purpose, we determine conditions under which the traditional Least Busy First or Most Busy First policies are optimal for agent selection. Using a dynamic programming analysis, we prove that a state-dependent threshold reservation policy is optimal when a chat can be handed to other agents without loss of efficiency. This analysis reveals that reservation is useful as it allows to maintain of sufficient agent productivity and abandonment from the queue which in turn reduce the system's congestion. In addition, we show the closeness between the optimal policy and the easier-to-implement fixed capacity policy if this fixed capacity is optimized. When a chat cannot be handed to other agents, we develop a dispatching policy improvement algorithm based on the explicit computation of the relative value function for an initial policy with fixed probabilities. The dispatching policy significantly outperforms non state-dependent ones, as it partly compensates the agents' inability to share a chat service.

Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters

“…This optimization can result in increased customer satisfaction and loyalty, ultimately leading to higher revenues for the business. Additional examples include [255,256].…”

Modeling Customer Experience in a Contact Center through Process Log Mining

“…In parallel, phone call data was used to automate and optimize other tasks including customer recognition [34], customer routing [17], adequately pairing customer and agent based on historical data [26] and evaluating the work carried out by agents [15]. In other types of channels data mining has been studied for instance in the context of chat routing modeling [20], emotion prediction from chats [28] and video chats [38].…”

Customer Service Chat Systems with General Service and Patience Times

“…Intuitively, (10) implies that the departure rate due to completion of service by an agent working at the maximum level I should be higher than that for any other level. In fact, we show in Lemma EC.2 that if (10) does not hold, it is not optimal to use level I, under the assumption that our approximations are exact and we use a non-idling policy in the sense that agents continue accepting customers up to level I (see Legrosa and Jouinib (2018) for the case when this decision is made dynamically). Hence if (10) does not hold, it is optimal to have customers wait in queue instead of having them served by an agent at level I and we can restrict the maximum level to I − 1.…”