Self­-service for public transport payments: A business case for conversational artificial intelligence

Although self-service (i.e. mobile top-ups) is at the heart of Snapper’s customer service offering, customers have a disjointed experience managing their public transport payment cards across a range of customer service touchpoints, including more traditional support channels such as helpdesks and in-person support centres. Customer feedback indicates that some Snapper users perceive the process of resolving support issues through these traditional support channels to be inconvenient and time-consuming. Activity through these traditional channels still forms a large proportion of Snapper’s customer service, despite Snapper’s ongoing investment in their self-service channels; including mobile applications, the website, the MySnapper desktop application, and kiosks. Just as Snapper innovated to meet customer demand for self-service through a mobile app (Snapper Services Ltd., 2017a), the evolution of conversational artificial intelligence (AI), or chatbot technology, presents an opportunity for Snapper to lead the way in meeting customer demand for a faster, more accessible way to resolve common support issues. The successful development of such a solution will further position Snapper as a market-leader in customer-centric innovation. In order to understand the commercial potential of such an automated customer service offering, the research aims to understand customer use and perceptions of Snapper’s support channels; to identify barriers to the adoption of self-service, and understand how these can be addressed; and to understand customer attitudes towards automated customer service. Using a mixed methods approach, research began with analysis of secondary data accessed from Snapper’s internal customer service reporting. Findings validated customer demand for additional self-service options, as well as the repetitive nature of Snapper’s customer service queries, indicating that these are ripe for automation. In-depth interviews were conducted with Snapper cardholders, giving further insight into how they select and interact with Snapper’s customer service channels. The avoidance of perceived effort was identified as a key theme when explaining how customers navigate service channels, supporting the role of “ease of use” in explaining customer adoption of self-service technologies (Davis, Bagozzi, & Warshaw, 1989). Types of perceived effort were identified as social, cognitive and logistical effort. These categories are proposed as an extension to the Technology Acceptance Model (Davis et al., 1989), giving additional insight into what constitutes “ease of use”. Following the in-depth interviews, market analysis and discussions with AI and chatbot service providers explored best practice in automated customer service, to understand the adoption of conversational AI technology in the New Zealand context, as well as how other companies have successfully implemented a chatbot product. The project report concludes with a stand-alone business case for applying conversational AI technology to Snapper’s customer service offering. The business case summarises the business model and delivery methodologies recommended for the project development (see Section 6.1), including LEAN startup methods. The market validation phase (Section 6.2) then addresses the strategic business case, assessing the case for change and incorporating key findings from the customer and market research conducted earlier in the research. Building on the opportunities identified in the PESTEL analysis, the product validation phase (Section 6.3) utilises a SWOT analysis, before providing clear recommendations around the required feature-set of the proposed solution, and a possible roadmap for implementing these features. Finally, the economic, financial and commercial cases are addressed; including a cost-benefit analysis of the proposed solution, a recommended development methodology, and high-level resources and requirements required for implementation. By validating that delivering such an enhanced self-service offering is commercially viable, the project aims to deliver a more delightful experience to Snapper users, driving better uptake of Snapper’s self-service channels