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Studying Long-Term User Behaviour Using Dynamic Time Warping for Customer Retention

Published: 15 February 2022 Publication History

Abstract

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.

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MP4 File (wsdmid11_doi10.1145:3488560.3510015.mp4)
Title: Studying Long-Term User Behaviour Using Dynamic Time Warping for Customer Retention 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).

References

[1]
Using Dynamic Time Warping to Find Patterns in Time Series" https://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf
[2]
Detecting and Classifying Events in Noisy Time Series: https://journals.ametsoc.org/view/journals/atsc/71/3/jas-d-13-0182.1.xml#bib43
[3]
Time-series clustering -- A decade review https://www.sciencedirect.com/science/article/abs/pii/S0306437915000733
[4]
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases https://link.springer.com/chapter/10.1007%2F3--540--45571-X_14

Cited By

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  • (2024)Modeling User Retention through Generative Flow NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671531(5497-5508)Online publication date: 25-Aug-2024
  • (2023)Interpretable User Retention Modeling in RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608818(702-708)Online publication date: 14-Sep-2023

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 15 February 2022

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    Author Tags

    1. customer retention
    2. dynamic time warping
    3. time series clustering

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    • (2024)Modeling User Retention through Generative Flow NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671531(5497-5508)Online publication date: 25-Aug-2024
    • (2023)Interpretable User Retention Modeling in RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608818(702-708)Online publication date: 14-Sep-2023

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