In this research, we collect monthly content consumption and demographic data from YouTube over two years for a large media publisher. We use automation to generate 15 personas each month and examine the consistency of the generated personas over time. We find that there are 35 unique personas in total for the entire period, reflecting the changes in the underlying audience population. For each persona, we generate topics of interest and identify the top three monthly topics for each of the 35 personas following an identical algorithmic approach each month. We then compare the sets of topical interests of the personas month-over-month for the entire two-year period. Findings show that there is an average 20.2% change in topical interests and that 68% of the personas experience more topical change than topical consistency. Findings suggest that the topical interests of online audiences are fluid and changes in the underlying audience data can occur within a relatively short period, resulting in the need for constant updating of personas using data-driven methods. The implications for organizations seeking to understand their online audience are that they should employ routine data analysis to detect changes in the audience interests and investigate ways to automate their persona generation processes.
|Original language||English (US)|
|Number of pages||10|
|Journal||Proceedings of the Association for Information Science and Technology|
|State||Published - Jan 1 2019|
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Library and Information Sciences