{"id":1,"date":"2021-05-02T19:06:04","date_gmt":"2021-05-02T19:06:04","guid":{"rendered":"https:\/\/quecst.qcri.org\/blog\/?p=1"},"modified":"2021-08-15T11:13:55","modified_gmt":"2021-08-15T11:13:55","slug":"persona_analytics_eswa","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/persona_analytics_eswa\/","title":{"rendered":"Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization"},"content":{"rendered":"\r\n<figure id=\"attachment_13\" aria-describedby=\"caption-attachment-13\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-13\" src=\"https:\/\/quecst.qcri.org\/blog\/wp-content\/uploads\/2021\/05\/persona_analytics_changing_segments-300x150.jpg\" alt=\"Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization\" width=\"300\" height=\"150\" srcset=\"https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2021\/05\/persona_analytics_changing_segments-300x150.jpg 300w, https:\/\/acua.qcri.org\/blog\/wp-content\/uploads\/2021\/05\/persona_analytics_changing_segments.jpg 686w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-13\" class=\"wp-caption-text\"><em><a href=\"http:\/\/www.bernardjjansen.com\/uploads\/2\/4\/1\/8\/24188166\/persona_analytics_eswa2021.pdf\" target=\"_blank\" rel=\"noopener\">Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization<\/a><\/em><\/figcaption><\/figure>\r\n<p>Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>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.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>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).\u00a0<\/p>\r\n\r\n\r\n\r\n<p>Results show an average 40% change in the personas, and 78% of the personas experience more change than consistency for topic interests.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>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.<\/p>\r\n\r\n\r\n\r\n<p>\u200bJansen, B. J., Jung, S.G., Chowdhury, S., and Salminen, J. (2021)\u00a0<em><a href=\"http:\/\/www.bernardjjansen.com\/uploads\/2\/4\/1\/8\/24188166\/persona_analytics_eswa2021.pdf\" target=\"_blank\" rel=\"noopener\">Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization<\/a><\/em>.\u00a0<u>Expert Systems with Applications<\/u>, 185, Article\u00a0115611.<\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences.\u00a0 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.\u00a0 We conduct 32 monthly rounds of&hellip; <a class=\"more-link\" href=\"https:\/\/acua.qcri.org\/blog\/persona_analytics_eswa\/\">Continue reading <span class=\"screen-reader-text\">Persona Analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[7,6,8,9],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.13 - 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