Measuring Attractiveness

How attractive is this exactly?

Attractiveness is not something that I’ve seen tested very much in teams, although it is super useful and surprisingly easy to do. We’ve all been the designer on a team where we wanted to improve aesthetics only to be told there’s no time, or it’s a nice to have. In truth it isn’t a nice to have, it’s essential a lot of the time.

You can make a good argument by using AttrakDiff to demonstrate the effect of your design. AttrakDiff is a product evaluation questionnaire developed by Marc Hassenzahl, Michael Burmester, and Franz Koller to focus on both the usability and the pleasantness of the experience.

How it works

It’s based on a model that divides the product experience into four elements:

  • The product quality intended by the designer
  • The subjective perception and evaluation of the quality
  • The independent pragmatic and hedonic qualities
  • The behavioural and emotional consequences

It does not measure specific emotions, but it includes an assessment of emotional impact on product evaluation. It measures attractiveness of a product based on 2 sets of scales:

  • Pragmatic scale — this measures usability
  • Hedonic scale — this measures emotional reactions.

The questionnaire consists of 28 sets of scales for users to evaluate. The scale’s poles are opposite adjectives (e.g. “confusing — clear”, “unusual — ordinary”, “good — bad”) related to these four elements i mentioned above. Each set of adjective items is ordered into a scale of intensity.

In addition, to help us get stronger data the survey asks:

What the output looks like

I’ve input some random data to show you what an output looks like in the app. I’ve chosen to show you an A/B test between 2 different directions. One is represented in blue and the other in orange. I’ve included the description of the diagrams from the app as well so you can understand what you’re looking at.

Grid view

The results are laid out on quadrant. The little square inside the big one is the Confidence rectangle. A small confidence rectangle is good because it means that the results are more reliable and less coincidental. The bigger the confidence rectangle, the more variable the evaluation ratings.

Average values

The average values of the AttrakDiff dimensions for the evaluated product are plotted on the diagram below. In this view, hedonic quality distinguishes between the aspects of stimulation and identity. We also get to see the overall rating of attractiveness.

Word Pairs

The mean values of the word pairs are presented here. Of particular interest are the extreme values. These show which characteristics are critical and where the opportunities lie.

How to use the output

Based on the (made-up) data above, I would dig deeper on the Subjective hedonistic aspects (in purple) because the gap between the two is widest. If this were a test between my app in blue and a competitor app in orange, I would have some good data to start progressing my own app further along and would start working with my team on how we bring in more boldness, creativity, and novelty into the design.

We also made flash cards of the different word sets (one on one side, one on the other) and we use them in sessions with customers and stakeholders alike. It’s a good way for us to get some constructive feedback on attractiveness and the more nebulous aspects of what makes great design.

In summary

AttakDiff is awesome because it produces good quantitative, and comparative data. Its limitation is that it assesses reflection on experiences, rather than actual experiences.

I like that:

  • You can scientifically measure the effect of look and feel
  • There’s a system 10 years in the making that is freely available online called AttrakDiff
  • You can use it to A/B test, evaluate and benchmark
  • You can really demonstrate the effect of going the extra mile to make something look and feel good
  • There are graphs
  • It’s free

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