Happen’s Head of Analytics Todd McLoughlin has been working in the world of quantitative data research since 1995, evaluating innovation concepts for the likes of Unilever, Reckitt Benckiser and Kraft. He joined Happen in 2018 after following the consultancies’ emotion analytics work with StarMaker. StarMaker uses big data to seek out online and offline conversations relevant to a business challenge, identifying expressions of excitement or frustration that explain consumer behaviour.
Here, McLoughlin discusses the potential and challenges of emotional analytics, and the future of the field.
What’s different about Happen’s emotion analytics tool, StarMaker?
For years I’d been using survey data and I was starting to see the limitations, especially in context of some of the questions clients were asking. I became aware of Happen and StarMaker and saw that it not only had the social listening element, but could also be used to analyse offline data – customer service transcripts, feedback forms and alike. In addition, it did something different that showed me it was far more advanced. I joined Happen because I could see that StarMaker goes a distinct step further by identifying specific areas driving excitement and frustration. That’s not the same as liking or disliking something. Innovators or marketers need to understand what pushes or pulls a consumer to purchase, as much as possible.
People talk about what they want online in a very clear way, and what we’ve discovered is that if you take the time to listen, you can fast forward the entire innovation process. It’s about getting to the right idea, first time.
How has the analytics market changed in the past five years?
Five years ago the gaps in knowledge, when it came to quantitative surveys, were increasing. People had busier lives, and that meant they had less time to do in-depth surveys. The industry responded by using shorter questionnaires with less depth, or accessing new and different pieces of data such as photos or videos. Forty-minute surveys can tell you how people respond to a category, but after that amount of time a lot of people probably mentally tap out. It meant lower quality data was being produced, and big data started to add value. This is because, unlike in some surveys, online data is not biased or guided commentary. It’s spontaneous and unsolicited, which makes it a really rich place to look for the answers people need.