Surveys and focus groups are the go-to methods for gathering customer insights to drive marketing strategy. However, they have major flaws such as inherent biases, low predictive power, high costs and stakeholder fatigue. It’s time to move beyond these outdated tactics.
Today, AI-powered tools like data mining and sentiment analysis offer a powerful way to augment and improve customer research. By leveraging customer data and feedback, AI can provide deeper and more accurate insights, with less bias and better predictive capabilities than surveys alone.
This article explores two key use cases on how AI can improve customer understanding more effectively and efficiently.
Using AI to increase predictive value and reduce the size of customer surveys
Two major problems associated with surveys are questionable predictive value and responder fatigue due to their size. Surveys have low predictive value because they often present choices to respondents or ask them to identify pain points independent of the broader context of their lives. As a result, survey results often do not match actual customer behavior and preferences. Additionally, the credibility of answers decreases as the number of questions increases.
Fortunately, customer interaction histories can be leveraged to better understand real-world behaviors and preferences. Traditionally, marketing analysts have used data mining techniques on structured customer data to identify behavioral patterns and create predictive models. AI reduces the need to structure customer data and improves the speed at which information can be delivered.
Although our experience tells us that AI still requires significant human oversight and direction, with AI we can evaluate a wider range of behaviors and scenarios in a shorter time frame. As a result, the information generated has both predictive and explanatory power.
However, a survey will help identify underlying factors, needs and motivations. Segmentation and insights based on customer data can help focus survey questions on observed behaviors, customer profitability, key demographics, and other valuable dimensions. Additionally, the survey can be shortened to address issues or opportunities identified specifically during the customer data exploration phase.
Remove bias inherent in surveys
Surveys are considerably susceptible to bias. The very design of a study and the survey questions often reflect the company’s agenda.
Consider the scenario of an innovative, engineering-driven consumer products company seeking to develop a new brand proposition for the market. Considering itself innovative, the company would likely poll its customers’ thoughts on innovation, and most would respond, “That’s great.” If you further ask them if innovation is essential to them, they will probably answer “Of course”.
However, when the customer makes a purchasing decision, they are unlikely to consider the innovation because it is neither transparent nor obvious. Instead, they can evaluate a product or service based on features and benefits that reflect a sense of innovation and relevance to their lifestyle.
This is just one example of a bias introduced into market research projects, based on what a company may consider important to them rather than what is essential to customers. Although it seems obvious in hindsight, in my experience these biases (and others) are very difficult to detect and prevent.
An alternative, less biased way to understand what customers value is to evaluate minimally solicited feedback. This could be information on social networks, chats or simple free responses to open-ended questions such as “How do you like the product?” »
This information has been difficult to extract because text mining and sentiment analysis capabilities have been limited. Using AI, we can evaluate large volumes of open-ended responses and identify critical perceptions, attitudes and needs. Once these AI-driven needs are revealed, a more focused and less biased market research project can be designed to provide deeper insights and support market strategies.
Unleashing the Power of AI in Customer Insights
The two use cases above are limited examples of using AI to create powerful insights at lower cost, with less bias and better predictive powers. There are many other use cases for AI in market research. The challenge for marketing science is to understand how AI can augment and improve research methods that are in desperate need of an overhaul.
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The opinions expressed in this article are those of the guest author and not necessarily of MarTech. Staff authors are listed here.
