In the never-ending pursuit of business innovation, artificial intelligence (AI) is increasingly becoming a hot topic. The prospect of computers progressing automation further through machine learning has even the market research industry abuzz.
If we’re being honest, every researcher could name a few tasks that they would gladly hand over to reliable AI technology to complete. Heck, while we’re at it, we’d gladly accept help with household chores and weekend errands.
The good news is artificial intelligence has countless applications within market research. A recent Quirk’s Media article touched on some 2019 AI trends in market research in regard to open-ended data, decision-making, and proactive community engagement.
Read below for a recap of these trends and why they are important to the future of the industry.
In the never-ending pursuit of business innovation, artificial intelligence (AI) is increasingly becoming a hot topic. The market research industry is no exception. Learn three ways AI will be advantageous for marketing research.
1. Open-Ended Text Analysis
Most market researchers understand the struggle of open-end coding. For the longest time, there was no better way to categorize your respondent’s text answers than by manually reviewing them. This process involves reading through up to thousands of open-ended responses and assigning the most relevant code or codes.
With the advancement of artificial intelligence, new approaches to text analysis in market research are emerging. Data scientists are developing statistical analysis techniques to make sense out of overwhelming amounts of qualitative data. Thankfully, the more data you feed an AI machine, the better the results you receive based on learning.
The latest technology, including Google Cloud’s Natural Language API, has achieved the ability to understand the category of text, syntactic structure, and text sentiment. Sentiment analysis, in particular, has become easy to integrate into online surveys to determine how positive or negative respondents’ answers may be.
This process makes it easier and less time consuming for a market research analyst to interpret data. In turn, with reduced hours for a market research firm to analyze data comes a reduced cost projection for a client’s market research project.
2. Complex Decision-Making
Natural language processing applications are also enabling market researchers to pull new insights from unstructured text data. AI technology such as IBM’s Watson can analyze social media posts from any number of users and identify intricate themes or traits.
Imagine sifting through thousands of Facebook, Instagram, Twitter, or LinkedIn posts and attempting to summarize the statements. Sure, it might be possible. However, it will probably also take your average researcher weeks to reach the best conclusion. Because few market research firms are willing to do this, AI is opening up a brand new service to offer value to clients.
Think beyond verbatim quotes or word clouds. What if you could automatically generate qualitative findings through AI? This may become a reality as machine learning and decision-making continues to evolve.
#3: Community Engagement
Artificial intelligence also has some tricks up its sleeve for researchers who manage a panel of participants. An online panel consists of people who have signed up to take part in a variety of market research studies should they be qualified.
Participants in these large online communities often receive research email invitations to qualify for a new survey or focus group. With the kind of volume some have, however, it’s easy to lose members from disengagement. In other words, if a panel member responds to many pre-screening surveys, but is never selected to participate in the research study, they may remove their contact information from the list or ignore the email invitations.
With the power of artificial intelligence, panels can predict when a panel member is on the verge of losing interest in participation through their behavior. This ability to proactively engage helps to prevent member turnover and improve the quality of participation in research.
For other online communities, market research firms can use this technology in a similar manner for clients to flag members at risk of becoming disengaged. For example, a social media platform or online subscription service may want to identify members whose usage has declined. In this instance, AI would learn the patterns associated with disengagement and observe other members for the same behaviors.
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