Artificial Intelligence In eLearning Market Research
Market research (MR) can demand significant effort across data source selection and surveys. However, Artificial Intelligence (AI) must empower you to automate several tasks like those. So, conducting market research becomes more manageable for smaller teams. This article discusses how Artificial Intelligence plays a major role in revolutionizing market research.
What Is AI For Market Research?
AI technology enables computers, machines, and cloud computing systems to eliminate the need for human intervention from repetitive tasks. Therefore, embracing it to upgrade market research services helps reduce your employees’ workload.
Market researchers specialize in forecasting consumer responses based on historical survey data and public information sources. While focus groups and face-to-face interactions are integral to MR methods, web scraping and social listening are gaining momentum. As a result, an enterprise can determine how it wants to customize market research tools to acquire specific intelligence. For example, tracking competitors’ online profiles and press releases helps estimate which new products they have planned. On the other hand, customer behavior insights are essential to developing an ideal client persona and categorizing the consumers accordingly.
The Role Of AI In Revolutionizing Market Research
Sam Altman, CEO of OpenAI, claims the company is engaged in developing more sophisticated generative pre-trained transformers (GPTs). Although the news revolving around GPT-5 keeps catching the world’s attention, similar generative AI solutions have helped innovate MR strategies. Conventional market research surveys used to be complicated. In contrast, modern MR studies prioritize respondents’ comfort. If an AI chatbot can increase consumer interactivity during these surveys, more individuals will be likely to complete them.
Besides, AI and Machine Learning (ML) can process unstructured data, like descriptive texts or call recordings. So, market researchers can expand their data analytics and reporting scope by using such media as raw data.
Applications Of AI In eLearning Market Analytics
- Real-time competitor tracking will alert companies of rival brands’ tactics
If a competitor slashes its product prices, launches a new service, or proceeds with a business merger, you want to know this as soon as possible. After all, this intelligence lets you prepare counterstrategies to be resilient against competitors’ ever-changing strengths and weaknesses. AI technology can streamline real-time competition monitoring that works 24/7. - Sentiment analytics evaluates a sample of media to estimate what the originator wanted to express
Business analysis and market research professionals use it to categorize consumer responses based on appreciative, critical, and neutral emotions. This use case relies upon Machine Learning and huge training datasets. It identifies similarities between the training data’s emotion-based samples and customer feedback. - AI-assisted predictive reporting bridges the gaps
It helps bridge gapes between the geometric extension of historical trend curves and more dynamic future scenarios, and therefore, it increases revenue forecasting’s reliability. Consider using it in the prelaunch and post-purchase phases to predict a customer’s lifetime value to your business.
Precautions You Need To Take
Automated MR is promising but raises questions concerning the ethics of customer data gathering. On one hand, you can restrict the scope of online or in-person surveys to objective descriptions.
Many corporations achieve this through a five-to-ten star scale. This approach also respects the surveyed clients’ convenience. Indeed, all the descriptive terms are predetermined, letting customers choose the most appealing one. While it provides structured data demanding less computing power for analytics, the results of surveys are unhelpful for personalization.
Effective customer journey personalization must combine objective reviews like star ratings with subjective ones. Given the governance controversy risks related to personally identifiable information (PII) processing, you must discourage consumers from oversharing private information. Additionally, it would help if you informed them how your team and data partners will use their elaborative feedback.
Finally, your data storage and extract-transform-load (ETL) pipelines must conform to the most robust encryption standards. Otherwise, you will expose your market research survey participants’ details to cybercriminals during a data breach. Identity theft, fraudulent transactions, and online harassment are some consequences of customers’ PII ending up on the dark web because of corporate data leaks.
Conclusion
Market researchers tap into public information platforms like social networking sites (SNSs), news portals, and peer-reviewed research journals. This secondary data gathering lets them understand the changes in customer preferences and brand perception. Simultaneously, MR professionals leverage focus groups and online survey tools to get first-hand data.
However, sorting the acquired data, cleansing it, and extracting competitively advantageous insights are complex tasks. Therefore, Artificial Intelligence has a crucial role in automating and revolutionizing market research practices. It will liberate your workforce from mundane data operations, enabling them to focus on more creative projects.
AI for market research offers real-time competitor data, sentiment-based response sorting, and predictive insights. Still, you must use it without infringing upon surveyed individuals’ privacy rights. If required, seek expert guidance regarding ETL security, cloud migration, and automated data aggregation. Remember, while AI and MR are potent aids for business development, using them ethically is nonnegotiable.
