Machine learning and AI are frequently used buzzwords. In terms of application I’m not sure whether we’re in the killer app territory yet. I read someone on Twitter saying something to the effect of most AI can be replicated with some good SQL queries (which is a nice soundbite).
From a conversion perspective I use Eyequant quite a lot. It mimics heatmaps of user interaction from a design (either on live websites or on heatmaps/wireframes) so you can get an idea of how visitors will perceive a website and where they will focus on the design.
From their website:
“EyeQuant fuses leading neuroscience research with AI to accurately predict how people will react to digital designs.”
I have been working with a data scientist on a couple of research projects to try and parse reviews to get an idea of underlying perspectives. “Text mining” of reviews is not something unique, this article goes into some detail about how to use R to extract the data, but more importantly how to display it so that quantitative insights can be gleaned from the voice of consumer.
Here are two examples of how I’ve used it:
- A company was selling the same products that Amazon stocked and my thoughts were to understand both the reviews and the questions asked by customers (as Amazon has such a volume of feedback it made this process easier to look at Amazon rather than on their site). One of the key products looked at was a bundle. If I use the example of a keyboard, computer and mouse I was surprised to see that most reviews focussed on the mouse, rather than on the core product. This lead to testing exposing more details about the mouse as we knew it was such a focus for visitors.
- The other was also to look at reviews for a high-tech product, but to understand what features people were most interested in. Once I found out what the focus was this helped to prioritise what to surface more and how to modify the architecture of the page used to promote this.
These are some pretty straightforward applications, but its interesting to understand that there is a wealth of data available, and if we can learn to unlock it efficiently we can be more efficient.
Brendan McNulty 