Service touchpoint assessment and optimization of online meeting platforms on the basis of an experiential memory model.

To address the issues of high costs in optimizing service design and the singularity of evaluation dimensions for service touchpoints, in this study, a service touchpoint optimization process that is based on the experiential memory model is developed, and it is applied to an online meeting platform. First, the target user group is identified. Through user behavior analysis and interface disassembly, the main service touchpoints can be obtained. The effectiveness, efficiency, and satisfaction of each service touchpoint are initially assessed via user usability tests, and an experience score is derived. A memory score is subsequently determined on the basis of the memory intensity of users toward the service touchpoints. These two scores are then combined to construct an experiential memory model, which is used to identify the service touchpoints that require improvement. The model is then applied to conduct in-depth analyses of the actual demands of users and to formulate corresponding optimization schemes. Eventually, on the basis of user behavior patterns and optimization strategies, a prototype of an optimized design is developed. The case analysis reveals that the improved experiential memory model that is developed in this research can effectively discriminate the key service touchpoints and reduce the design workload. Moreover, the proposed service touchpoint optimization process can enhance the user experience for online meeting platforms, thus providing a reference for research on online meeting optimization and service touchpoint evaluation.