TY - JOUR
T1 - Persona analytics
T2 - Analyzing the stability of online segments and content interests over time using non-negative matrix factorization
AU - Jansen, Bernard J.
AU - Jung, Soon gyo
AU - Chowdhury, Shammur A.
AU - Salminen, Joni
N1 - Publisher Copyright:
© 2021
PY - 2021/12/15
Y1 - 2021/12/15
N2 - Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences. Applying data-driven persona generation on online customer statistics from a real organizational social media channel, we demonstrate how personas can be deployed to understand online customer patterns over time. We conduct 32 monthly rounds of data collection of customer demographics and content consumption patterns on the YouTube channel of a major publishing organization posting thousands of items of content and then algorithmically generate 15 personas monthly. We analyze the data-driven persona for changes monthly, yearly, and lifetime (period). Results show an average 40% change in the personas, and 78% of the personas experience more change than consistency for topic interests. The implications are that organizations frequently publishing online content should employ automatic data collection and periodic persona creation to ensure their customer understanding is current. For this, algorithmic data-driven systems that leverage methods for persona creation are recommended.
AB - Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences. Applying data-driven persona generation on online customer statistics from a real organizational social media channel, we demonstrate how personas can be deployed to understand online customer patterns over time. We conduct 32 monthly rounds of data collection of customer demographics and content consumption patterns on the YouTube channel of a major publishing organization posting thousands of items of content and then algorithmically generate 15 personas monthly. We analyze the data-driven persona for changes monthly, yearly, and lifetime (period). Results show an average 40% change in the personas, and 78% of the personas experience more change than consistency for topic interests. The implications are that organizations frequently publishing online content should employ automatic data collection and periodic persona creation to ensure their customer understanding is current. For this, algorithmic data-driven systems that leverage methods for persona creation are recommended.
KW - Big data
KW - Customer segmentation
KW - Data-driven personas
KW - Longitudinal analysis
KW - Social media analytics
UR - http://www.scopus.com/inward/record.url?scp=85110741454&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.115611
DO - 10.1016/j.eswa.2021.115611
M3 - Article
AN - SCOPUS:85110741454
SN - 0957-4174
VL - 185
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 115611
ER -