Companies have typically used market research in order to gain consumer insights. But in the age of social media, consumer preferences change rapidly and data gathered from months-long research projects is static and one-dimensional. AI can overcome this.
With the application of artificial intelligence, many companies can invest in research that grows with them and has no shelf life. Absolutdata CEO Anil Kaul tells Digital Journal that it will soon become necessary for brands to take a more holistic approach to research by taking ownership of it, rather than renting solutions that do not last.
The emerging field of AI-driven market research is putting marketing’s holy grail within reach. With market research available more broadly at a fraction of its former cost and time commitment, marketing teams will be able to conduct dynamic segmentation and brand optimization projects and develop more effective media strategies. Best of all, AI-driven market research will increase impact by a factor of 10, scaling across the organization.
To discover more, we spoke with Anil Kaul about AI’s capabilities in the market research space.
Digital Journal: How has market research changed in the digital age?
Anil Kaul: The market research sector is being transformed by the same advances in AI and technology that are changing the customer experience and/or business models across every industry. I think it’s fair to say the transformation of market research lagged behind until recently — the industry adopted online surveys, etc., years ago, but aside from tactical adaptations like that, the core processes hadn’t changed much in decades.
Now, the industry is undergoing two major changes. The first is a shift in industry emphasis from people skills to algorithms. This will change the nature of how we do market research as the focus shifts from assembling experts for research projects to developing and maintaining algorithms. As a result, research is becoming faster, cheaper and better, driving wider application to increase impact by a factor of 10. That’s an enormous change.
DJ: What are the limitations with the traditional model of companies purchasing market research reports?
Kaul: Traditional market research is expensive because it involves significant expertise on the design and administration side. It requires further expertise for data interpretation to yield insights, and projects are typically measured in weeks or months. AI-driven market research returns a better work product — actionable recommendations instead of insights — and produces it much faster for a fraction of the cost. This constitutes a significant leap forward in market research.
Traditional market research focuses on answering high-stakes questions; it’s too costly and time-consuming for day-to-day decision making. It uses research data only and typically has a short shelf life since it’s designed to answer specific questions. In contrast, AI-driven research combines research data with other information, such as operations reports, sales numbers, etc., to generate deeper insights on customer behavior. Data used in AI-driven market research can be leveraged for future projects and applied more broadly, creating scalable business impact across the enterprise.
DJ: How important is AI becoming in the area of market research?
Kaul: AI is the critical enabling technology that is driving the complete transformation of marketing research. Traditional market research won’t continue to exist as we know it because of AI advances. In modern market research, research methodologies and frameworks are layered around an AI core, generating recommendations to inform business decisions. By putting AI at the core, modern market research platforms can automate the research process from initial design to recommendation delivery.
In that way, AI is making the transformation of market research possible. With AI, enterprises can gain a much deeper understanding of customer needs, market opportunities, brand perceptions, product innovations, pricing impact and more. And because AI makes market research faster, cheaper and better, market research can be applied more broadly and inform daily decision-making.
DJ: Which are the leading AI technologies?
Kaul: Leading AI-driven market research technologies include everything from natural language processing (NLP) to machine learning (ML) to video and image recognition technologies and more. Together, these technologies will continue to change the way research is conducted. To cite just one example, instead of “diary data” that relies on participants to create an accurate account of their activities for research purposes, AI and video/image recognition technologies can collect and analyze it instead.
AI is also improving the business users’ experience. For example, it’s now possible to use NLP to develop an interactive model that allows users to ask business questions in plain language and receive research-based answers, reports and actionable recommendations. AI technologies are essential in making modern market research faster, cheaper and better than traditional market research.
DJ: What types of data are most important for businesses?
Kaul: Ultimately, customers are the source of business revenue, so data about customers is critical for businesses. The market research industry has always existed primarily to generate customer data, including information on why people behave as they do, what factors are driving changes within markets, where business opportunities lie, etc.
AI-driven market research can provide data to answer these questions. It can enable market segmentation for efficient targeting and optimal portfolio strategies. It can also help companies keep tabs on overall brand health, monitor media effectiveness and optimize the customer journey at every stage. These capabilities are increasingly necessary to compete and win in the modern economy.
DJ: What can businesses do with real-time data?
Kaul: The utility of real-time data depends entirely on the business decision that needs to be made. If the user can make a change quickly to take advantage of an opportunity or avoid losses, real-time data is extremely useful. Use cases can include restaurant pricing that can be adjusted in response to factors like foot traffic and/or weather, dynamic pricing on an ecommerce platform, customer satisfaction anomalies that point to problems that can be quickly addressed, etc.
The bottom line is that businesses today run on many types of data, including real-time data. AI is driving a huge change in the market research world, delivering the information businesses need to operate more efficiently, keep customers happy and innovate. AI is utterly transforming market research, and in the process, it’s improving the way businesses make decisions.