Last week’s Market Research Summit 2017 was a hugely enjoyable and engaging day. For me, event highlights included Rhea Foxes’ discussion on the 5Cs for their research program: Cost, Culture, Communication, Confidentiality and (most importantly) Convergence; and Tim Quinlan’s presentation on the importance of understanding consumer emotions as a key value builder at Superdry.
It was also lots of fun to take part in the panel discussion: ‘Uberinsight or Datageddon’ How AI and Machine Learning will affect the Insight sector.
As mentioned in my last post, the panel was chaired by Colin Strong, Global Head of Behavioural Science at IPSOS with Julia Ayling from Mindshare and The University of London’s Professor of Artificial Intelligence, Chris Watkins.
One of the key points raised was how AI will and is assisting marketers to find key insights from the exponentially growing amount of information (IoT, Social etc) and data available to them – so insights managers can find needles in their haystacks in seconds, not days.
Colin raised fundamental questions about the future of marketing and insights: if the industry will be powered by AI algorithms, will we still need to ask questions, or will the machines do that for us? And will there be a role for humans in market research?
The unanimous response from the panel was that AI will create the need to pose better and more thoughtful questions. We agreed that machines already suggest answers to questions from existing research and data, so to create ground-breaking insights, insights managers and marketers will have to use AI to link disparate pieces of information together to help formulate better questions and theories.
As we’ve learned at Market Logic (with help from our IPSOS partners), curation is the future and will be enhanced by AI. Therefore, the role of the insight professional will be based more around connecting the dots and telling great stories than the current remit of data crunching and analysis.
The good news is this means insight professionals will be enhanced by AI but not be replaced. At the end of the day, humans still have to ask questions, so AI steps in to make answering those questions easier. In this context, AI helps by cutting through the noise to make the evidence-driven decisions effortless.