Abstract

Traditional customer experience measurement often fails to capture dynamic, multidimensional insights. This research develops a recursive analytics framework combining advanced computational techniques. The framework integrates topic modelling, sentiment analysis, and temporal tracking. Using Design Science Research, we create a systematic four-step process enabling hierarchical topic decomposition.

We demonstrate the framework using Netflix customer feedback from July 2022 to November 2024. Analysis reveals three-tiered hierarchical patterns in customer concerns. These relate to pricing, customer service, and content representation. Results show the framework transforms noisy customer data into actionable insights. The approach offers a powerful tool for managing customer experience in rapidly changing markets. This research-in-progress demonstrates framework capabilities; comprehensive evaluation is planned future work.

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