follows: Help team members share a specific, consistent understanding of various audience groups. Data about the groups can be put in a proper context and can be understood and remembered in coherent stories. Proposed solutions can be guided by how well they meet the needs of individual user personas. Features can be prioritized based on how well they address the needs of one or more personas. Provide a human "face" so as to create empathy for the persons represented by the demographics. Help support better design choices by limiting the focus of user for the designers. Helps understands what motivates audience to learn more about products/service. Criticism Criticism of personas falls into three general categories: analysis of the underlying logic, concerns about practical implementation, and empirical results. In terms of scientific logic, it has been argued that because personas are fictional, they have no clear relationship to real customer data and therefore cannot be considered scientific. Chapman and Milham described the purported flaws in considering personas as a scientific research method. They argued that there is no procedure to work reliably from given data to specific personas, and thus such a process is not subject to the scientific method of reproducible research. Other critics argue that personas can be reductive or stereotypic, leading to a false sense of confidence in an organization's knowledge about its users. Critics like Steve Portigal argue that personas' "appeal comes from the seduction of a sanitized form of reality," where customer data is continuously reduced and abstracted until it is nothing more than a stereotype. Critics claim that persona creation puts the onus on designers, marketers, and user researchers to capture multiple peoples' opinions and views into predefined segments, which could introduce personal bias into the interpretation. Additionally, personas often feature gendered and racial depictions, which some argue is unnecessary and distracts the target audience of the personas from true consumer behaviors and only enhances biased viewpoints. Finally, it is worth acknowledging that proto-personas and personas are often generalized as the same resource, however, proto-personas are a generative tool used to identify a team's assumptions about their target users. Personas, on the other hand, should be rooted in customer data and research, and be used as a way to coalesce insights about particular segments. Scientific research In empirical results, the research to date has offered soft metrics for the success of personas, such as anecdotal