Blobbing-Up Users

We’ve been going all gooey over ‘blob charts’ – our answer to persona data analysis – a godsend when faced with dozens of interview transcriptions and observations.

When analysing users we look at a huge number of facets including:

…Gender, Age group, Family situation, Income, Housing, Where living, Occupation, Education, Race/Ethnicity, Nationality, Language skills, Social networks , Lifestyle traits, Interests/Hobbies, Media read/watched/listened to, Relationship (i.e. Employee), Job title, Time in job, Previous jobs, Percentage of overall users, Importance of person relative to [Org], Usage rate of [Org], Attitude towards [Org], Frustrations, Hours of work, Place of work, Work environment, Computer reliance, Reliance on [Site], Intensity of use, Emotional goals, Needs, Frustrations, Attitude to [Site], Users role, People interactions, Surrounding environment, Traceability, Accuracy, Confidentiality, Flexibility, Operational risk, Type of usage, Connection speed, Browser setup, Operating System, Screen Resolution, Monitor Hardware, Input Devices, Base Computer, Accessibility, Software and (importantly) Workplace Scenarios…

It’s enough to make our heads spin!

In the past we’ve painfully plotted each persona on a polar chart in Visio, then visually identified where users appear in common clusters…


What we do is map user behaviour on two extremes (or opposites) – here’s some examples for a user of an online trading site:

Freq of use?       ‘Every day’ vs ‘Several times a month’
Doing what?       ‘Buying stuff’ vs ‘Selling stuff’
If buying?             ‘Know what I want’ vs ‘Random browsing’
If selling?             ‘One thing at a time’ vs ‘Several at a time’
Usage?                   ‘Keyboard’ vs ‘Mouse clicker’

So this time I said “no way – let’s automate!”

This is what Bob and Random Nat came up with…

…we rated each user on a scale of 1 to 5 in a spreadsheet – and viola! We could now generate a funky ‘blob’ chart like this…
(The larger the blob the larger the number of users with the same behaviour).

We can also plot the average for each user group…

And then overlay different user groups to show similarities or differences…

So, what’s the point of all this?

This data allows us to whittle down our many possible personas by merging users with similar behaviours and characteristics.

It’s also another rich visual tool to describe the persona, along with photographs, quotes, written content and scenario descriptions.

Here’s a portion of one of our persona profiles…


Next Steps

We’re considering refining the persona mapping tool and releasing it online so other User Experience people can generate their own blob charts.

If you think this would be useful or have any suggestions please post some feedback.

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