Personas were first introduced by Cooper (1999), who was at the time working on the development of new software. As he interviewed potential future users to gather ideas for inclusion in his project, Cooper started to participate himself. He discovered that using play-acting to solve design questions about functionality and interaction was extremely effective, allowing him to understand and communicate what was necessary or unnecessary from a user-centered perspective. He has used this technique to design all of his products, keeping in mind the advantages of thinking from the perspective of the user. As a result, he and his clients were able to better understand the end user in their projects.

A persona is a fictitious character representation of an actual group of users who might use your product or service. Creating personas can assist designers in adopting a more objective understanding of users’ requirements, experiences, behaviors and goals. Personas have been well integrated into industry design processes (Nielsen & Storgaard Hansen, 2014), as companies have recognized that building them will help product designers address the right questions for their target users. Personas simplify design tasks by guiding designer ideation processes, and can assist designers in identifying product users. User-centric organizations use personas to facilitate the adoption of shared mental models about target users and to increase designers’ empathy for their users (Nielsen, 2019). Personas can result in a positive return on investment for organizations that use them (Drego et al., 2010).

Personas are created on data collected from multiple individuals as the process has traditionally relied on ethnographic methods (An et al., 2017). Because data collection events are one-time events, the personas that are created can become quickly out of date. In the absence of real-time data, designers are unable to determine whether the personas they have created are representative of current users (An et al., 2017). As a result, developing personas is not a low-cost or quick procedure.
These limitations are the motivations for this study. Web analytics and search platforms are providing a wealth of data that can be used for persona development, which has significantly increased the feasibility of using Data Driven Persona Development (DDPD) as a method of obtaining information about users or customers from online sources that contain big data (Del Vecchio et al., 2018). This research aims to contribute to the body of literature on data-driven persona development. This study will 1) Collect, analyze and synthesize relevant literature within the subject and 2) Provide an overview of how to use Google Analytics and Google Trends in persona development and its strengths and weaknesses. Below are the specific research questions this paper addresses:

RQ1: What information can Google Analytics and Google Trends provide for persona development for content creators?

RQ2: What discrete demographic customer segments correspond to each of these behavioral customer segments?

RQ3: How practical is this method in making personas that represent these customer groups?