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.
(Editor's note: This article is part of an occasional series from leading voices about key issues facing marketing today.)
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. In these ways, AI helps speed up the process of finding the best possible respondents—and, with other techniques, almost replicates probability-based sampling
Likewise, when finding respondents from customer databases, it is easy to overlook the huge amount of extant data that might, for example, come in the form of previous corporate data collection. AI can unlock and process operational data that might have been ignored and combine it with more recent experience data to ultimately mine for clarity and insights.
In addition, researchers are collaborating with companies to collect first-party information from customers who opt in to share their data to help shape products and brands for the future. This data may include income, segmentation, and purchase, for example, from rewards programs—organized and mined for insights by AI.
3. Removing Bias From Customer Feedback
Bias is a huge risk: It has the potential to skew to the integrity of data. However, AI is able to remove subconscious human bias both from respondents and from study design. For example, running data through a question design review tool that uses AI will analyze questions and make real-time suggestions that address potential bias and improve data gathering.
4. Conducting Extensive Secondary Research
Secondary research, such as syndicated studies or meta-analysis, and the advice of management consultants are often essential for small and large companies alike to make financially sound decisions. But this type of research and consultant support can be time-consuming and expensive.
Primary research, with the help of AI, can be both faster and less expensive. And using AI means companies can analyze secondary research quickly and identify key trends and themes in the data—without needing the help of management consultants to do the heavy lifting of analysis for them.
5. Continuously Improving the Quality of Studies
The quality assurance aspects of AI cannot be overstated. AI can pinpoint areas where questions need improving or where there is bias. Machine-learning can also be used to optimize the collection of future customer feedback based on previous data.
As artificial intelligence evolves and its use becomes more mainstream, the potential benefits for XM researchers—and businesses as a whole—will only increase. By incorporating these technologies, marketing researchers can develop timelier, more accurate, and, ultimately, more insightful data into their customers, employees, products, and brands.
You may like these other MarketingProfs articles related to Marketing Technology:
- Why PR (And Marketing) Pros Need to Embrace Imperfect AI Writing Technology Now
- Seven Ways Businesses Can Harness the Speed of Technology to Reduce Customer Churn
- How AI-Powered Marketing Tools Will Help SMBs
- Empowering B2B Marketing Teams With an AI Content Process: May Habib on Marketing Smarts [Podcast]
- Six Lead Generation Tools to Improve ROI on Your B2B Marketing Funnel
- Worse Than a Black Hole? The Complexity of B2B Martech Stacks