Five Ways AI Is Changing the Future of Marketing Research for the Better
The rise of artificial intelligence (AI) has dramatically changed the way businesses and brands understand and communicate with their target audiences. Nowhere is that truer than in the fields of experience management (XM) and marketing research.
AI and machine-learning can evoke polarized opinions in public discourse: For example, many welcome AI as a means to reduce repetitive and mindless work; others fear it for exactly the same reason, thinking AI coupled with robotics will replace human jobs.
But in the XM research world, the scales are massively tipped in favor of AI. In fact, our research at Qualtrics research shows that 93% of market researchers describe AI as an industry opportunity. As an example of interest in the intersection of AI and market research, the self-governing organization of market researchers in the US, the Insights Association, organized it conference in June on how AI and other new technologies “are powering better ways to collect data, discover insights, and communicate results.”
Researchers have reason to be optimistic about AI. Here are five ways AI is changing the future of XM research for the better.
1. Understanding What Customers Really Want
AI gives market researchers access to tools like powerful automated text analysis, which can analyze millions of comments, both voice and text, in minutes and emerge with a nuanced understanding of what customers think and want.
Powerful algorithms can learn from respondents and ask the right follow-up questions, micro-targeting the questions to be specific to a single respondent’s interests and needs.
With natural-language processing and sentiment analysis running alongside, market researchers can see trends and sentiment in real-time across multiple channels.
2. Finding Respondents Faster, With Higher Accuracy, in a Way That Maximizes Existing Data
Using AI, market researchers can review a wider pool of respondents and remove those who aren’t suitable, resulting in better, more tailored shortlists of potential candidates. Using techniques like tapping into social graphs and accessing blockchain information, will (ironically) catch “bots” who pretend to be humans.
