Five ways AI is changing the future of market research

The rise of artificial intelligence (AI) has dramatically changed the way businesses and brands understand and communicate with their target audiences. AI is enhancing the fields of experience management (XM) and market research.

AI and machine-learning can evoke polarised opinions in public discourse. Some welcome AI as a means to reduce repetitive and mindless work; others fear it for exactly the same reason, thinking AI coupled with robotics may replace human jobs.

But in the XM and market research world, the scales are massively tipped in favour of AI. AI presents an industry opportunity to power better ways to collect data, discover insights, and communicate results.

Here are five ways AI is changing the future of market research for the better.

1. Understanding What Customers Really Want

AI gives market researchers access to tools like powerful automated text analysis, which can analyse 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 Maximises 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.

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 analyse 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 analyse 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 optimise the collection of future customer feedback based on previous data.

As artificial intelligence evolves and its use becomes more mainstream, the potential benefits for marketing professionals and market researchers will only increase. By incorporating these technologies, marketing professionals and market researchers can develop timelier, more accurate, and, ultimately, more insightful data into their patients, customers and brands.

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