Iterative Mixed Method Approach to B2B SaaS User Personas

被引：4
作者：
Boyle R.E. [1 ]
Pledger R. [2 ]
Brown H.-F. [1 ]
机构：
[1] LiveRamp, San Francisco
[2] LiveRamp, Seattle
来源：
Proceedings of the ACM on Human-Computer Interaction | 2022年 / 6卷 / EICS期
关键词：
analytics; b2b; cluster analysis; data-driven personas; mixed-method personas; personas;
D O I：
10.1145/3534523
中图分类号：
学科分类号：
摘要：
User persona research has primarily been done on Business-To-Consumer (B2C) products, generated hybridly with interviews or surveys or purely quantitatively through clickstream data, often to serve sales / marketing teams. However, the application of these approaches in different circumstances to this is still relatively novel. How can we segment users in a way that is both helpful and accurate for a Business-To-Business (B2B) Software-As-A-Service (SaaS) product and its product development stakeholders, for a product with "imperfect"/ obfuscated product analytics data and disjointed user journeys? We performed an iterative, qualitative and mixed quantitative approach to user personas that utilized survey data, user interviews, and preprocessed / manipulated web analytics data to provide a complete picture of the user. Survey data provided user background and insight into who to recruit while interviews provided insight into user journeys, personalities, and pain points. Using dimensionality reduction and an iterative clustering approach informed by survey and interview insights, we generated clusters based on users' time per page and clicks per feature data from our web analytics. Diving into the data behind each cluster and triangulating with interview and survey data enabled us to identify six distinct user personas. We recommend our approach to user segmentation, an as yet unexplored combination of qualitative and mixed quantitative methods, for the research teams of other mid-sized B2B SaaS companies as a way to address domain-specific challenges and bridge the data and user journeys gaps to paint a full, accurate picture of the user. © 2022 ACM.
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