One of my papers from 2 years ago is still causing discussion: “The Personasâ€™ New Clothes: Methodological and Practical Arguments against a Popular Method” by me and Russ Milham. Email from researchers I didn’t know led me to look up citations, and the article appears to be commonly cited when people present criticism of the personas method. Google search. The paper itself is here.
There are a few misunderstandings of our position out there. Our basic argument is simple. Persona authors often make two claims: (1) personas present real information about users; and (2) using personas leads to better products. In a nutshell, we argue that neither claim has been supported by empirical evidence; rather, the claims for personas’ utility are based on anecdotes, generally from their own authors or other interested parties (such as consultants selling them).
This does not mean that personas are bad, but they cannot be taken at face value. As researchers, we suggest that persona authors should either provide better evidence (and we suggest how) or make weaker claims.
Some persona users don’t make claims about their personas’ usefulness or correspondence to reality; they simply say that personas might be helpful for inspiration for some people or teams. We take no issue with that, as long as they don’t forget those caveats and reify the persona. Unfortunately it is probably very difficult for people to read a persona and not think that it describes a user group.
We’ve recently published empirical work on (quasi-)persona prevalence using several large datasets, demonstrating that once a description has more than a few attributes it describes few if any actual people. I’ll put that paper up as soon as I get reprint permission. (If you have access to HFES archives, it is “Quantitative Evaluation of Personas as Information”, Christopher N. Chapman, Edwin Love, Russell P. Milham, Paul ElRif, James L. Alford, from HFES conference 2008, New York.)
What should one do instead of personas? I advocate stronger empirical methods that have more demonstrable validity.