What was your journey/career path to your current position?
I started my career as a market researcher, focused on delivering insights using traditional methods of research. I worked agency-side on quantitative datasets such as household panels, loyalty card data and had also worked on large quantitative trackers. During that time, I also worked clientside. In 2009, I started working for an innovative consultancy called Intrepid and my focus shifted on using a broader range of tools and techniques, including qualitative research and ethnography. That is when I came across the practice of analysing social media conversations as a source of consumer insights and I was hooked. My focus after that was firmly on the practice of social media intelligence. Joining Precise in 2012 (which became part of Kantar and is now Onclusive) allowed me to build a capability and best practice using research frameworks. During that time I continuously sought to develop approaches and find new ways of answering questions. Since September 2020, I have moved client-side, first working for NHS Digital and now with an FMCG company. My work is now more hybrid as it combines traditional research and where possible social intelligence. I continue to contribute to spreading best practice by leading an MRS training course on the use of social data in research.
What's your proudest achievement of your career to date?
Being recognised as an expert in my field and being invited to present at events and conferences is when I have been the proudest. Presenting at Esomar was the most memorable one as I was presenting a case study with Orange which was the culmination of several phases of work around the topic of customer relationships with brands.
What does social intelligence mean to you?
Social Intelligence has given me the opportunity to explore and experiment in a way which other research techniques would probably not have allowed me. I have been able to see time and time again how rich a source of consumer insight it is if you apply the right thinking and develop principles and frameworks. The fact that it is unstructured means that the possibilities are potentially endless which I think is what attracted me to it in the first place. It also allows you to conduct qualitative research at scale which only large scale online communities allow you to do.
What's been the biggest challenge you've faced while trying to get brands to integrate social intelligence within their growth strategy?
Educating stakeholders to the usefulness of social intelligence has always been the biggest challenge. It was at the beginning when the practice of analysing social conversations for insight was fairly new and still is. Most of the time this is because there is a still a narrow view of what it can provide and also a strong focus on social media as a communication channel rather than the organic body of conversations which exists outside of that.
What do you think is the biggest missed opportunity for social intelligence?
For years, the focus has been on social media listening tools and what the tools could provide rather than what social media conversations could surface. I also firmly believe that considering social media conversations as a 'big data' source has also limited the outputs from it. This is changing but perhaps not quickly enough.
What's on the cards for you and your team/organisation in 2022?
I am going to continue to advocate the social intelligence as part of our research toolkit. I'm also going to be continuing to experiment with social conversations to see whether it can help me surface new insights.
How do you see social intelligence and its use evolving?
While social intelligence is no longer niche, it is still not fully a mainstream practice. We therefore need to make sure it stays on the agenda and more and more researchers are trained in the practice so that they can continue developing its use and use cases. I see the evolution of social intelligence remaining organic and its development needs to be fueled by the imagination of researchers. I see technology still playing a part in this evolution particularly in helping us acquire the data more readily and structuring it more quickly using entity extraction, and text and image analysis so that the processes of getting the data ready for analysis are less cumbersome.
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