Surveys and focus groups are popular approaches for gaining client insights to inform marketing strategy. However, they have significant limitations, including intrinsic biases, low predictive power, high costs, and responder fatigue. It’s time to abandon these antiquated methods.
Today, AI-powered techniques such as data mining and sentiment analysis provide a great approach to supplement and improve customer research. By leveraging customer data and comments, AI can give more profound, more accurate insights with less bias and superior predictive skills than surveys alone. This post looks at two significant use cases for how AI might improve customer understanding more efficiently and effectively. Using AI to improve the predictive value and reduce the size of consumer surveys Two key concerns with surveys are questionable predictive value and respondent weariness due to scale.
Surveys have low predictive value because they frequently give respondents options or ask them to identify pain areas apart from the greater context of their lives. As a result, survey results frequently disagree with actual customer behavior and preferences. Furthermore, responder credibility reduces as the number of questions grows. Fortunately, consumer interaction histories may be analyzed to understand actual actions and preferences better. Historically, marketing analysts have utilized data mining techniques on structured customer data to detect behavioral trends and create prediction models. AI reduces the need to structure customer data and increases the pace at which insights can be supplied. While our experience has shown that AI still requires significant human supervision and direction, it allows us to examine a wider range of behaviors and scenarios in less time. As a result, the insights gained have predictive and explanatory value.
A survey can nevertheless assist discover underlying causes, needs, and motives. Customer data-driven segmentation and insights can assist in focusing survey questions on observed behaviors, customer profitability, key demographics, and other important factors. Furthermore, the survey can be tailored to address specific problems or opportunities uncovered during the customer data mining step. Removing biases inherent in surveys. Surveys are highly subject to prejudice. The design of a research and the questions on the survey frequently reflect the company’s agenda. Consider the case of an innovative engineering-focused consumer products company trying to create a fresh brand proposition for the marketplace. Seeing themselves as innovative, the corporation will most likely assess customers’ opinions on innovation, with the majority responding, “It’s great.” If you ask them if innovation is important to them, they are likely to say, “Of course.” However, when it comes time to make a purchasing decision, customers are reluctant to consider innovation because it is neither transparent nor obvious. Instead, consumers may assess a product or service based on its features and benefits, which demonstrate innovation and relevance to their lifestyle. This is just one example of a bias introduced into market research initiatives based on what a firm believes to be significant rather than what customers require. While it may appear evident in retrospect, in my experience, these biases (and others) are extremely difficult to detect and prevent.
A less biased approach to understanding what customers value is to analyze minimally prompted input. This could include information from social media, chats, or simply free-form responses to open-ended queries like, “How do you like the product?” This data has been difficult to extract since text mining and sentiment analysis capabilities are limited. AI allows us to examine massive volumes of open-ended responses and uncover crucial perspectives, attitudes, and needs. Once these AI-driven requirements are identified, a more targeted and less biased market research project may be developed to provide deeper insights and support market strategies. Unleashing the power of AI for customer insights. The two use cases presented above are just a few instances of how AI may be used to generate powerful insights at a reduced cost, with less bias and better predictive potential. There are numerous applications for AI in market research. The issue for marketing science is understanding how AI might supplement and improve research techniques that urgently require reform.
