---
_id: '46341'
abstract:
- lang: eng
  text: Customer Segmentation aims to identify groups of customers that share similar
    interest or behaviour. It is an essential tool in marketing and can be used to
    target customer segments with tailored marketing strategies. Customer segmentation
    is often based on clustering techniques. This analysis is typically performed
    as a snapshot analysis where segments are identified at a specific point in time.
    However, this ignores the fact that customer segments are highly volatile and
    segments change over time. Once segments change, the entire analysis needs to
    be repeated and strategies adapted. In this paper we explore stream clustering
    as a tool to alleviate this problem. We propose a new stream clustering algorithm
    which allows to identify and track customer segments over time. The biggest challenge
    is that customer segmentation often relies on the transaction history of a customer.
    Since this data changes over time, it is necessary to update customers which have
    already been incorporated into the clustering. We show how to perform this step
    incrementally, without the need for periodic re-computations. As a result, customer
    segmentation can be performed continuously, faster and is more scalable. We demonstrate
    the performance of our algorithm using a large real-life case study.
author:
- first_name: Matthias
  full_name: Carnein, Matthias
  last_name: Carnein
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
citation:
  ama: 'Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data
    Using Stream Clustering. In: <i>Proceedings of the 23$^rd$ Pacific-Asia Conference
    on Knowledge Discovery and Data Mining (PAKDD ’19)</i>. ; 2019:280–292.'
  apa: Carnein, M., &#38; Trautmann, H. (2019). Customer Segmentation Based on Transactional
    Data Using Stream Clustering. <i>Proceedings of the 23$^rd$ Pacific-Asia Conference
    on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 280–292.
  bibtex: '@inproceedings{Carnein_Trautmann_2019, place={Macau, China}, title={Customer
    Segmentation Based on Transactional Data Using Stream Clustering}, booktitle={Proceedings
    of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining
    (PAKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={280–292}
    }'
  chicago: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on
    Transactional Data Using Stream Clustering.” In <i>Proceedings of the 23$^rd$
    Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>,
    280–292. Macau, China, 2019.
  ieee: M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional
    Data Using Stream Clustering,” in <i>Proceedings of the 23$^rd$ Pacific-Asia Conference
    on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292.
  mla: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional
    Data Using Stream Clustering.” <i>Proceedings of the 23$^rd$ Pacific-Asia Conference
    on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292.
  short: 'M. Carnein, H. Trautmann, in: Proceedings of the 23$^rd$ Pacific-Asia Conference
    on Knowledge Discovery and Data Mining (PAKDD ’19), Macau, China, 2019, pp. 280–292.'
date_created: 2023-08-04T07:47:20Z
date_updated: 2023-10-16T13:30:10Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 280–292
place: Macau, China
publication: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery
  and Data Mining (PAKDD ’19)
status: public
title: Customer Segmentation Based on Transactional Data Using Stream Clustering
type: conference
user_id: '15504'
year: '2019'
...