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Surveys and focus groups are the go-to methods for gathering customer insights to drive your marketing strategy. However, they have major flaws such as inherent bias, poor predictive power, high cost, and responder fatigue. It’s time to move on from these outdated tactics.
Today, AI-powered tools such as data mining and sentiment analysis offer powerful ways to enhance and improve customer research. By leveraging customer data and feedback, AI can provide deeper, more accurate insights with less bias and greater predictive power than surveys alone.
In this article, we explore two key use cases where AI can help you understand your customers more efficiently and effectively.
Use AI to increase predictive value and reduce the size of customer research
Two major problems associated with surveys are questionable predictive value and respondent fatigue due to size. Surveys have low predictive value because they often present respondents with choices or ask them to identify problems outside of the larger context of their lives. As a result, survey results often do not match actual customer behavior and preferences. Also, as the number of questions increases, the reliability of the answers decreases.
Fortunately, you can mine the history of customer interactions to better understand their actual behavior and preferences. Traditionally, marketing analysts have used data mining techniques on structured customer data to identify behavioral patterns and build predictive models. AI reduces the requirements for structuring customer data and increases the speed at which insights are delivered.
Our experience shows that while AI still requires significant human oversight and direction, it can be used to evaluate a wider range of behaviors and scenarios in a shorter amount of time. As a result, the insights generated have both predictive and explanatory power.
Research helps identify underlying drivers, needs, and motivations. Segmentation and insights based on customer data can help you focus your research questions on observed behaviors, customer profitability, key demographics, and other valuable aspects. Additionally, research can be shortened to address issues and opportunities specifically identified during the customer data mining stage.
Remove inherent bias from research
Research is highly susceptible to bias. The survey design itself and the survey questions often reflect the company’s challenges.
Consider the scenario of a consumer products company focused on innovative engineering that is trying to develop a new brand proposition for the market. A company that considers itself innovative will likely survey its customers to find out what they think about innovation, and most will say, “That’s great.” And if you ask them if innovation is essential to them, they’ll likely answer “of course.”
However, when customers make purchasing decisions, they are unlikely to consider innovation because it is not transparent or obvious. Instead, we may evaluate products and services based on features and benefits that reflect a sense of innovation and relevance to our lifestyles.
This is just one example of bias being injected into market research projects based on what companies might think is important to them, rather than what is important to their customers. Although it seems obvious in hindsight, in my experience these biases (and others) are very difficult to detect and prevent.
Another less biased way to understand customer value is to assess minimal feedback. This could be information on social media, a chat, or a simple open-ended answer to an open-ended question such as “What do you think of your product?”
This information was difficult to mine due to limited text mining and sentiment analysis capabilities. AI can be used to evaluate large volumes of open-ended responses and identify key perceptions, attitudes, and needs. Once these AI-driven needs are identified, you can design more targeted and unbiased market research projects to generate deeper insights and support your market strategy.
Unleashing the power of AI in customer insights
The two use cases above are limited examples of using AI to create powerful insights with less bias and better predictive power at a lower cost. There are many other use cases for AI in market research. The challenge for marketing science is to understand how AI can enhance and improve research methods that are in dire need of innovation.
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The opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
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