AI-Driven Market Research Is a Gamechanger for Marketing
Jul 11 2019 | 07:30 PM | 4 Mins Read | Level - Intermediate | Read ModeAnil Kaul Co-Founder and CEO, Absolutdata
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Anil has over 22 years of experience in advanced analytics, market research, and management consulting. He is very passionate about analytics and leveraging technology to improve business decision-making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. He is also on the board of Edutopia, an innovative start-up in the language learning space.
An in-demand writer and speaker, Anil has published articles in McKinsey Quarterly, Marketing Science, Journal of Marketing Research and International Journal of Research. He was recently listed among the ‘10 Most Influential Analytics Leaders in India’ by Analytics Magazine India and has been quoted as a “Game Changer” in Research World. Anil has spoken at many industry conferences and top business schools, including Dartmouth, Berkeley, Cornell, Yale, Columbia and New York University. Anil holds a Ph.D. and a Master of Marketing degree, both from Cornell University.
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Unlike the traditional approach, the output from AI-driven market research can be combined with other sources, such as sales, text, social media, behavioral and passive data. This will yield new recommendations that will have 10x the impact traditional market research does and reflect a changing marketplace. Absolutdata's CEO, Anil Kaul, discusses how AI solutions can provide marketers with real-time data and recommendations that optimize decisions for the future.
The martech space has exploded over the past decade; The number of available solutions has grown from about 150 in 2011 to more than 7,000 in 2019. At the same time, the market research sector has experienced a decline, with global revenue down from a high of $46.1 billion in 2014 to $45.8 billion in 2017. But AI is about to disrupt and revitalize market research — that’s great news for marketers.
Until now, market research has been viewed as an expensive tool that’s used only for high-stakes marketing projects. While automated surveys have made data collection easier, most market research initiatives still require expensive consultants and take several weeks, if not months, to complete. That makes it too costly to use in everyday marketing decisions. That’s not the case with AI.
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AI-driven market research can now bring together a range of research methodologies, data types and machine learning techniques to automate the research process from design to delivery. By putting AI at the core of a platform and layering methodologies and frameworks around it, AI can not only drive research design to generate insights, but also can provide recommendations that inform business decisions.
Moreover, instead of market research being a one-and-done proposition, in which the knowledge gained informs a single decision and then gathers dust, AI-driven research project data and knowledge persist, joining the company’s entire body of research to be leveraged to provide answers to business questions. With AI, users can access a portal to ask questions in natural language, receiving answers (and reports) via natural language processing.
The practical applications of this capability are virtually limitless, and they’re not confined to just the marketing department. Another advantage of AI-driven market research is that AI and machine learning functions get smarter over time and create scalable business impact across the organization. But here are three use cases for AI-driven market research that are especially useful for marketing:
Segmentation: Marketers take on segmentation projects to more efficiently target customers and refine and optimize marketing strategies. AI-driven market research can assist by making it simple to build dynamic micro-segmentation models, create archetype segmentation for messaging design purposes, develop attitudinal segmentation to understand the customer mindset and optimize global marketing investment with country clustering.
Brand optimization: AI-driven market research tools can deliver a brand measurement framework centered on the customer, accessing diverse information sources to provide a comprehensive picture of brand health. This gives marketers insight about brand equity and the actions they can take to capitalize on it most effectively. Practical applications can include a brand health dashboard and identification of key brand loyalty drivers.
Media strategy: The right AI-driven market research tool can assist with media planning and execution by defining campaign success and identifying impact. It can bring diverse data together, including survey information, social media and in-house sales data to provide recommendations that improve customer interactions. AI-driven research can also measure ROI from media spend and promotions and recommend channels to optimize reach.
With a platform-based, AI-driven market research solution, the data, results and insights generated from research projects can be leveraged across the enterprise, training self-learning algorithms that get more intelligent and align with unique company dynamics. Continuous learning and data generate benefits beyond marketing, delivering a positive impact on sales, product development, supply chain and more.
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It’s now possible for marketing to add an AI-driven marketing research tool to the solution stack. Since market research projects no longer require weeks and months to complete or a team of experts to interpret, companies can leverage market research in daily operations. The democratization of market research will be a gamechanger for the industry.