Dialogue between the client and customer service agent

Contoh Percakapan Customer Service Menangani Komplain

dialog customer service

Use poor, as well as positive, feedback to gain a better grasp of what your business is doing well and where improvements need to be made to increase consumer happiness. As a cofounder of a review platform, I often find that bad comments, low ratings or scathing reviews can do immense damage when it comes to future prospects. They are powerful tools for companies to get their names out there and for customers to find new products and services.

dialog customer service

When the system is intended to replace a human expert (e.g., for customer support), human-like characteristics are considered beneficial for generating familiarity and trust with the agent. By contrast, the humanness of a system is not considered helpful when it is designed to substitute an existing computer system given the “automation bias” (Diederich et al. 2020). Guided by these findings, we adopt the view of human characteristics being positively related to the trustworthiness of conversational agents for deriving the requirements and DPs for our design theory in the context of customer support. Based on this body of academic and business knowledge, we start the design of the artifact (Activity 3) by identifying a set of 14 requirements to help us to address the class of goals to be achieved. Guided by these requirements, we explicate five DPs following the principles of Gregor et al. (2020) to meet the requirements for designing a dialog strategy for SDSs in customer service. Central to each design theory is a set of hypotheses for testing the question of whether the proposed DPs meet the requirements (Walls et al. 1992; Gregor and Jones 2007).

Evaluation of the Speech Dialog Systems

Thus, user satisfaction with the open SDS in terms of likability or humanness may lead users to underestimate the error rate. However, further research is needed to gain deeper insights into the importance of different system characteristics on the user experience. Future design studies could rank the requirements and DPs by their relative importance (e.g., based on frequency analysis, factor analysis, or other ranking methods). We follow the framework of Venable et al. (2016) to develop an appropriate evaluation strategy comprising three evaluation rounds in a naturalistic setting (Activity 5).


Furthermore, the findings indicate that the clear confirmation of inputs in the closed system is beneficial for enhancing habitability toward the system. Accordingly, the implicit input confirmation should be taken into account more consequently in the design of the open SDS. However, an issue that remains unanswered relates to how an optimal level of assistance can be achieved in an open SDS while benefiting from open expression for an intuitive human-like conversation, which is frequently perceived as positive by the users.

Customer Care Scripts for Apologizing for Order Mixups, Product Issues, and Other Concerns

Although closed dialog strategies predominate in business practice (Dale 2016), researchers have been interested in the differences and comparisons between dialog strategies since the 1990s. Delogu et al. (1998) examine the first forms of interactive voice response systems based on natural speech input and compare them with closed dialog technologies such as dual-tone-multi-frequency, which allows the user to interact with the system via a closed menu using phone keys. Similarly, a number of more recent studies compare open with closed SDSs, which yield rather contradictory results. For example, Meng et al. (2003) demonstrate that open dialog systems are superior to closed dialog systems in terms of performance accuracy and error rates when used in a simple foreign exchange domain. On the contrary, the study by Savcheva and Foster (2018) shows that open dialog systems do not provide higher customer satisfaction, but the more human-like interaction has led to a somewhat more efficient interaction in terms of errors encountered. As these studies indicate (Meng et al. 2003; Savcheva and Foster 2018), we do not think that a comparison to determine whether an open or a closed dialog strategy is generally preferable to the other is beneficial, as both strategies have their strengths and weaknesses, depending on the use scenario.

The reason is that only a few survey participants attempt to capture several variables at once to benefit from the slot filling function of the open SDS. The rationale for the non-consideration of the slot filling function can probably be found in the lack of awareness of or the lack of experience with slot filling. To increase the probability that users utilize slot filling, they should be informed in the welcome prompt of the first call about the available function, including an example statement (see also P3d). In addition to the menu options, the closed SDS lists the command options to help the user in understanding how to operate the SDS.

However, aspects such as data privacy, user data protection, or economic factors may have an equally significant impact on the technical design of such a class of artifacts. When using SDS, many users are concerned with the protection of their data (Luo et al. 2019). Particularly in the financial and healthcare sectors, dialog systems are met with skepticism and resistance by the end users, as the mere disclosure of confidential information poses a risk to the user (Carter and Knol 2019). In the course of the dialog, multiple user data are collected, including personal information such as name or address, customer number, credit card data or bank accounts, and these data must be adequately stored and properly handled. Given the sensitivity of such information, many users have privacy concerns (Lopatovska et al. 2020).

Thus, the underlying closed dialog strategy provides system-guided support, which aims to collect relevant data successively through a fixed sequence of questions (McTear 2002). Accordingly, the main goal of the closed dialog strategy is successful task realization through system-controlled guidance, thereby providing structure for all menu and error correction options and narrowing down the possible utterances (Lee et al. 2017). By contrast, the frame-based approach (open dialog strategy) merely determines the boundaries of the conversation and offers users the possibility to freely express their concerns (Torres et al. 2019). Instead of detailed menu prompts, open questions convey a natural conversation, thus imitating human dialogs (Griol et al. 2017). This feature allows users to directly name multiple entities to be captured and, if necessary, supplemented by specific system questions to obtain the required information (slot filling) (Singh and Arora 2020).

How to Greet a Returning Customer on the Phone

In addition, the limitations of this study are outlined with further propositions for future research. Aside from methodological issues, another limitation can be found in the explicit focus on the dialog management of task-oriented SDSs. Thus, the design theory proposed in this paper is only suitable for serving as design knowledge for task-oriented SDSs, and it cannot be generalized in the context of non-task-oriented SDS or text-based dialog systems. Among others, future research could address the extent to which the requirements and DPs for a speech-based dialog system can be adopted for the design of text-based dialog systems.

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Posted: Tue, 31 Oct 2023 00:01:07 GMT [source]

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