Persona analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization

摘要

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.