人物角色随时间变化:分析数据驱动的人物角色在两年期间的变化

Soon-Gyo Jung, Joni O. Salminen, B. Jansen
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引用次数: 22

摘要

对人物角色的批评之一是,他们所基于的底层数据可能过时,需要进一步的数据收集。然而,我们找不到这种批评的经验证据。在这项研究中,我们为一家大型在线内容出版商收集了为期两年的月度人口统计数据,并按照相同的算法方法每月生成15个人物角色。然后,我们每月、每年和整个两年期间比较这些人物角色集。调查结果显示,人物角色每月平均变化18.7%,每年变化23.3%,整个时期变化47%。研究结果支持了人物角色确实会随着时间而变化的批评,也强调了基础数据的变化可以在相对较短的时间内发生。这意味着使用角色的组织应该采用持续的数据收集来检测可能的角色变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personas Changing Over Time: Analyzing Variations of Data-Driven Personas During a Two-Year Period
One of the critiques of personas is that the underlying data that they are based on may stale, requiring further rounds of data collection. However, we could find no empirical evidence for this criticism. In this research, we collect monthly demographic data over a two-year period for a large online content publisher and generate fifteen personas each month following an identical algorithmic approach. We then compare the sets of personas month-over-month, year-over-year, and over the whole two-year period. Findings show that there is an average 18.7% change in personas monthly, a 23.3% change yearly, and a 47% change over the entire period. Findings support the critique that personas do change over time and also highlight that changes in the underlying data can occur within a relatively short period. The implication is that organizations using personas should employ ongoing data collection to detect possible persona changes.
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