Dynamic Time Warping (DTW) is an asynchronous alignment algorithm used to measure similarities between temporal sequences. DTW is advantageous for comparing sequences when the shape of the pattern is more important than the speed of events. It has been widely used for automated speech recognition, pattern detection in stock pricing data, studying energy consumption patterns for appliances, etc. In this presentation, we discuss the use of dynamic time warping for understanding long-term usage patterns on Twitter. Time series data are more useful to understand repeat user behavior and build user narratives than aggregated and/or snapshot user features. We utilize the time series of different user metrics as temporal signals and cluster them to identify specific usage and engagement patterns for churning users (users who become inactive). This approach led to more accurate opportunity sizing for the different user personas which in turn helped prioritize interventions for customer retention. We will discuss the implementation of this approach for large-scale data, understanding the time series clusters in a human-understandable manner, and challenges associated with multi-dimensional time-series data in the presentation.