Kids in a candy store

Deep learning and artificial intelligence are disrupting as well as enriching research and analytics. We go to tech-pioneering North America and ask some of the industry’s frontrunners how will this affect research and what is needed to create the next paradigm of knowledge based consumer insights?

Earlier this year, Forbes identified deep learning (DL), a sub-set of artificial intelligence (AI), as the hottest tech trend. The American business magazine predicts that huge AI investments by the likes of Google, Facebook and Amazon will lead to the emergence of new tools for collecting and analysing data. This will have a pervasive impact on the intelligence industry, thinks Rex Briggs, founder and CEO of New York’s Marketing Evolution, a marketing measurement and optimisation solutions specialist. “There are so many applications of AI that can be helpful for the market research industry. Analysing open-ended text in the form of sentiment analysis is already here. Image recognition of advertisements in the wild is already here.”

Dmitry Gaiduk, CEO of CoolTool, a San Francisco based online platform for survey research, believes that AI will lead us to Insights Intelligence (II) and perhaps even to a completely different industry landscape within ten years or maybe even sooner. “During the last few decades we have collected a lot of data. AI has amazing potential to help us systemise it, turning big data into great insights. Moreover, we will discover a lot of superb opportunities on every particular step of our journey; from smart analysis of the data, to getting truly actionable insights while on the go. AI will be at the heart of the research/analytics automation.”

Proto-insights

The biggest shift, according to Michael Alioto, VP global methods at Michigan-based decision intelligence company Gongos, is in how AI and DL enhance the way the insights industry views and understands consumers. “Previous rational based methods and analytics viewed and evaluated data on a linear, logical, single point in time framework. AI has the ability to incorporate dynamic assessment, longitudinal data, machine learning, and human based self-correction. This allows the marketing researcher to view the human decision making structure as a process freed from time based constraints. It can power our predictive capabilities by allowing data to correct itself and machine learn.”

Although David Krajicek, CCO, GfK Consumer Experiences, and member of the GfK management board, agrees that the effects will be large, he doesn’t expect a seismic shift or big bang. He expects the shorter term impact to be on the traditional research delivery chain, where there are still a lot of human touches on the production chain. “Here, AI is mainly an efficiency play – and will continue to drive speed and quality.” In the longer term Krajicek thinks AI will develop more robust capabilities around insight generation, enabling it to identify connections that historically were not easy to surface.

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