Dialogue between the client and customer service agent

Rather, dialog strategies must be adapted to the specific use case to ensure a satisfactory user experience (Kvale et al. 2021). Therefore, our study focuses on devising a design theory that integrates design principles that apply to both strategies and aim for a satisfactory user experience in customer service. We instantiate an SDS with an open and a closed dialog strategy to evaluate the utility and effectiveness of the devised design theory for both dialog strategies and to highlight the strengths and weaknesses in the user experience in the context of a task-oriented use case.

For the open SDS, the results of these cognitive walkthroughs particularly related to issues of unrecognized intents and prompt wording; for the closed SDS, the results pertain to the number of options and prompt length as well as the categorization and order of prompts. The idea that the implicit confirmation strategy poses the most challenging issue for the experts becomes apparent, as it is not always understood as data entry confirmation (cf. DP3). Based on the experts’ feedback, we refine the prototypes in the second design cycle and correct major pitfalls (e.g., change to an explicit confirmation strategy, addition of test phrases for the model training, improvement of prompt design, categorization of the service offerings). By subsequently conducting in-house user tests with five potential users, we aim to ensure that users can master the tasks in the dialog systems without prior experience and further assistance.

DP4

However, navigation errors occur more frequently with the open SDS (average 1.46) than with the closed SDS (average 1.07). Moreover, the success rate in fulfilling both tasks for the closed system is a convincing 96.10%, compared to 91.22% for the open SDS. The survey data are validated for the internal consistency reliability of our latent constructs by calculating the Cronbach’s alpha (α) and the composite reliability that exceeds the recommended limit of 0.7 (Nunnally and Bernstein 1994). Descriptive statistics reveal higher subjective average scores using the open SDS in the area of perceived humanness, system response accuracy, and likability, whereas the closed SDS is more convincing in habitability. In customer service, customer satisfaction depends not only on measurable criteria (i.e., the time required to process the request) but also on social factors such as the feelings of users (Hudson et al. 2017).

While it’s important to provide a cordial goodbye, ask if your customer other questions, and thank them for their business, the end of the conversation is also a great opportunity for upselling and cross selling. You may not always be in the position or have the authorization to answer some questions or meet requests, and there’s nothing wrong with that. Delegating certain requests to other departments or team members, while putting the customer on hold, is a common task in customer service. This “putting customer on hold script” below will help you transfer requests for a number of scenarios.

US decides Southwest’s customer service failed during flight cancellations last winter

A logical dialog structure should enable the effortless handling of the system by incorporating the user perspective and using available information (Gardner-Bonneau and Blanchard 2007). The automatic verification and completion of user input ensures a more efficient dialog flow (Jain et al. 2018). For example, incomplete addresses can be completed with the help of the Google Maps API to enable a more efficient dialog (Vaira et al. 2018). As another example, the integration of mathematical checksums can help to validate credit card or customer numbers (Pearl 2016). The development process is initiated by a brief problem identification and motivation (Activity 1) to justify the value of a solution for the problem. Not only do scripts provide your representatives with on-hand support should they need it, they are also excellent tools to train new team members.

Depending on the utterance of the customer and the selected dialog strategy, the system response is generated by the response generation module (Klüwer 2011). The most widespread method is the use of response templates with so-called slots or placeholders filled with the entities from dialog management (Singh and Arora 2020). In the last step, the generated response is reproduced in natural language by the speech generation module that synthetically generates speech (Burgoon et al. 2017, p. 257). Depending on the dialog flow, multiple conversation turns may be necessary to fulfill the customer’s inquiry (Merdivan et al. 2019).

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Additionally, using customer service scripts (while allowing for use of natural language in conversation) provides a consistent experience for shoppers. In this context, an interesting yet still unanswered question in the SDS research field relates to the question of the specific kernel theory that is best suited to guide the design of an SDS. To date, there is a lack of research that provides an interdisciplinary overview of available and appropriate kernel theories, regardless of the respective research disciplines. Such an overview would help to guide future DSR projects for a more rigorous design process. For SDS designers to ensure that an SDS has correctly captured all the required information during a conversation turn, a confirmation strategy should be implemented to guide customers in providing required and missing values for a structured and effective conversation (McTear et al. 2016, p. 214). In this intermediate-level role-play dialogue, you will get a better understanding of how to interact with a customer service representative.

You want to help resolve their issue, but you also want to make sure that their concerns are heard and understood. The scripts below will cover the basic issues of price errors, order mix-ups, and other product-related issues. Below, we’ve provided thorough guides that cover the most common ecommerce and retail customer service phone script needs. Creating a memorable customer experience, whether that is through service or sales, is the core of our business. Our approach leverages highly targeted and segmented communications distributed across a mix of channels. Create a methodical and flexible plan for incorporating consumer input into your strategy.

Instead, the function is triggered in a few cases and only in an unintentional manner, which in the dialogs caused more misunderstandings than being useful. Thus, small-talk intents should be avoided in a task-oriented SDS because too many different intents increase error probability. This finding is consistent with one of the major assumptions of dialog theory, which posits that task-oriented dialogs are instrumental, with people only engaging in a dialog when they intend to achieve a particular task or goal (Bunt 2000). One major implication that can be derived from this finding is that the design of task-oriented SDSs should be different from the design of social SDSs.

As stated in the methodology section, we conduct an empirical study to highlight the strengths and weaknesses in the user experience in the context of a task-oriented use case (Walls et al. 1992; Gregor and Jones 2007). To compare the effects of the open and closed dialog strategies on user experiences in detail, comparability between the systems is required. The main difference between the two strategies can be found in the menu-oriented structure of the closed dialog system. Menu prompts belong to the category of system prompts, and they should also fulfill the requirements of being efficient, precise, and understandable (Robertson et al. 2016).

On a conceptual level, dialog strategies can be operationalized through a frame-based or a finite-state dialog strategy. SDSs that follow the finite-state approach are characterized by a system-guided dialog based on predefined menu options (also referred to as closed dialog strategies). On the contrary, frame-based SDSs offer users the possibility to freely express their concerns based on open questions in a human-like conversation (also denoted as open dialog strategies) (Griol et al. 2017). However, the particular dialog strategy that is appropriate for providing a satisfying user experience in customer service settings is not evident (Meng et al. 2003; Savcheva and Foster 2018). The design theory draws on Bunt’s (2000) dialog theory and comprises both requirements and design principles (DPs) for SDS dialog strategies in customer service.

We record, transcribe, and analyze the conducted user tests through a qualitative content analysis (Mayring 2001). The results from the user tests reveal rather minor issues (wording of the prompts, isolated intent detection issues), which are resolved by the further refinement of the instances. According to Walls et al. (1992), a design theory includes prescriptive instructions for how to realize more effective and feasible design and use. With regard to our design theory for an SDS, we must therefore identify the main requirements and DPs to help us to achieve these goals. According to Bunt’s dialog theory (2000, p. 2), an SDS consists of “structures of goals, beliefs, preferences, expectations, and other types of information, plus memory and processing capabilities” that dynamically change during communicative acts as a reaction to other acts.

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As with any DSR project, the findings of this study are subject to some limitations that must be considered when interpreting the results. Some methodological limitations exist with regard to the systematic literature review conducted in this study to gather relevant literature that serves as justificatory knowledge. First, the literature search is conducted in six interdisciplinary databases for a broad and comprehensive search.

• To this end, we identify the requirements related to DP prompt design, menu design, persona design, confirmation strategy, error management, and functional design.
• Accordingly, we can confirm the findings of previous studies that human-like characteristics are considered beneficial for the design of conversation-based technologies when the system is intended to substitute a human expert, for example for customer support (Diederich et al. 2020).
• 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).
• These design features reflect a series of specific design choices that instantiate each DP (Meth et al. 2015; Schoormann et al. 2021).

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