Version 1
: Received: 23 April 2023 / Approved: 25 April 2023 / Online: 25 April 2023 (09:17:29 CEST)
How to cite:
Gadepally, K. C.; Dhal, S. B.; Kalafatis, S.; Nowka, K. Privacy First Path Analysis Using Clickstream Data. Preprints2023, 2023040904. https://doi.org/10.20944/preprints202304.0904.v1
Gadepally, K. C.; Dhal, S. B.; Kalafatis, S.; Nowka, K. Privacy First Path Analysis Using Clickstream Data. Preprints 2023, 2023040904. https://doi.org/10.20944/preprints202304.0904.v1
Gadepally, K. C.; Dhal, S. B.; Kalafatis, S.; Nowka, K. Privacy First Path Analysis Using Clickstream Data. Preprints2023, 2023040904. https://doi.org/10.20944/preprints202304.0904.v1
APA Style
Gadepally, K. C., Dhal, S. B., Kalafatis, S., & Nowka, K. (2023). Privacy First Path Analysis Using Clickstream Data. Preprints. https://doi.org/10.20944/preprints202304.0904.v1
Chicago/Turabian Style
Gadepally, K. C., Stavros Kalafatis and Kevin Nowka. 2023 "Privacy First Path Analysis Using Clickstream Data" Preprints. https://doi.org/10.20944/preprints202304.0904.v1
Abstract
In today’s digital economy data-based decisions have become very important to meet the ev-er-growing needs of customer engagement, retention, and satisfaction. Clickstream data is one such data that is being used to better understand, predict and engage with customers. Unfortu-nately, clickstream data for understanding customers has raised privacy and security concerns with many internet providers selling data for monetary benefits. This paper showcases a meth-odology that is developed based on experiential learning and using the latest cryptographic methods including differential privacy and graph analytics for predicting customer lifetime value (CLV) using clickstream data. Results obtained show that a user’s engagement can be pre-dicted within a relatively acceptable range after preserving privacy.
Keywords
Clickstream; RFM; Privacy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
