Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data
We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We e...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2018
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5326 https://ink.library.smu.edu.sg/context/sis_research/article/6330/viewcontent/Customer_segmentation___PV.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-6330 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-63302020-10-23T07:44:11Z Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data AN, Jisun KWAK, Haewoon JUNG, Soon‑gyo SALMINEN, Joni JANSEN, Bernard J. We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people. 2018-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5326 info:doi/10.1007/s13278-018-0531-0 https://ink.library.smu.edu.sg/context/sis_research/article/6330/viewcontent/Customer_segmentation___PV.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Web analytics Social computing Personas Marketing System design Customer segmentation Computer and Systems Architecture Databases and Information Systems |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Web analytics Social computing Personas Marketing System design Customer segmentation Computer and Systems Architecture Databases and Information Systems |
spellingShingle |
Web analytics Social computing Personas Marketing System design Customer segmentation Computer and Systems Architecture Databases and Information Systems AN, Jisun KWAK, Haewoon JUNG, Soon‑gyo SALMINEN, Joni JANSEN, Bernard J. Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
description |
We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people. |
format |
text |
author |
AN, Jisun KWAK, Haewoon JUNG, Soon‑gyo SALMINEN, Joni JANSEN, Bernard J. |
author_facet |
AN, Jisun KWAK, Haewoon JUNG, Soon‑gyo SALMINEN, Joni JANSEN, Bernard J. |
author_sort |
AN, Jisun |
title |
Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
title_short |
Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
title_full |
Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
title_fullStr |
Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
title_full_unstemmed |
Customer segmentation using online platforms: Isolating behavioral and demographic segments for persona creation via aggregated user data |
title_sort |
customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2018 |
url |
https://ink.library.smu.edu.sg/sis_research/5326 https://ink.library.smu.edu.sg/context/sis_research/article/6330/viewcontent/Customer_segmentation___PV.pdf |
_version_ |
1770575404625559552 |