3 big ideas
Personas rely on customer research, and having skilled designers capable of identifying the patterns and traits which are most meaningful to the situation at hand.
AI such as IBM's Watson, allow you to feed in customer interviews and contextual inquiry outputs to create personas.
It's not easy to introduce context to the AI models - current configuration doesn't enable it to filter out the customer attitudes and needs that only relate to what you're researching.
What made us think
Our point of view
Why it matters
How it applies in the real world
Customer personas have been around for many years, as a useful design tool to understand customers and create better experiences.
They typically rely on a body of customer research, and on having skilled designers capable of identifying the patterns and traits which are most meaningful to the situation at hand. So can we create a persona using AI?
We've been experimenting with IBM's Watson product recently, feeding in customer interviews and contextual inquiry outputs to see what it can do for us - and we've learned something about how we create personas as well about AI.
We've used 2 of the Watson components; Personality Insights and Tone Analyser.
This is what we did:
- We recorded a range of customer research interviews and inquiries (with their permission of course).
- We decided against feeding in the raw audio, though that's a future option future and would pick up more sentiment. Why? Filtering our own researchers' voices out was too hard for our initial testing. So we had the customers' words transcribed, then fed in the text.
- The 2 main outputs are a summary of key traits, both as words and on personality scales. Then there's a more detailed 'wheel' of a wider range of traits (see image).
This is what we learned:
- Some of the outputs aren't useful. Statements like "Unlikely to enjoy country music" haven't been useful for personas we've ever done!
- Other traits are much more interesting, e.g. is this person logical or emotional?
- Spotting the dichotomies, i.e. the important differences between different customers, is a manual process if you're using the basic visual output. But linking via APIs means you can use the data outputs and it's much faster to spot those traits where the widest differences occur.
- Lastly, we haven't found a way to easily introduce context to the AI models. In other words, if you're researching customer attitudes and needs related to bank mortgage products, then Watson isn't going to pick out anything specific to that context without more configuration and training than we've yet been prepared to attempt for a one off project.
Service Design & Research
Understanding and designing for customers