AI-Powered Market Research: Navigating Real-Time Customer Feedback

MX Bites / January 5, 2024

Presently, the global business landscape is in need of a more sophisticated approach to market research. This is because the traditional methods of market research are now obsolete as they consume time and energy and do not give accurate results. In recent times, the rise of AI tools has salvaged the process, radically enhancing market research and introducing transformative approaches to speed up the process and also provide accurate results.

However, this process also poses some challenges to market research, such that, if it is not managed properly, it may affect the outcome of product sales and customer relationships. Therefore, business leaders must learn how to implement strategies to overcome these challenges.

Let’s explore, in the rest of this article, the challenges posed by AI in market research and how to solve them.

The Power of AI-driven Market Research

For many companies, using AI-based tools can form the basis for transformative market research when implemented strategically. AI uses sentiment analysis and natural language processing to dissect the volume of data that comes in from customer feedback and further analyze the sentiments embedded in their feedback, which includes survey responses, social media comments, and reviews. Ordinarily, it is difficult to achieve this by conventional or traditional means, which involve human mental capacity; however, by using AI technologies, this process is achieved quickly and accurately.

Also, AI has the capacity to unveil hidden patterns and trends that give specific information about customer behaviors, preferences, and future needs. Access to this information helps business leaders pattern their production around it. Besides all these, AI-powered tools can be used to create interactive questionnaires that are specific to a category of customers based on their personal data. Not only does this increase customer satisfaction, but it also enhances customer retention.

Let’s explore a few challenges that are encountered when using AI technology for market research.

Challenges in Implementing AI for Feedback Analysis

The following are various challenges that are likely to be encountered when using AI for feedback analysis:

1. Over-automation: While AI-based tools are capable of automating tasks and providing a nuanced perspective on analysis, industry leaders must not lose sight of the fact that the process also needs human supervision. If AI is assigned to solely handle tasks, it may come to conclusions that are logically correct but lack the necessary human touch that makes them more relatable. Likewise, since AI models provide results based on the trained algorithm, they may be subject to limitations and biases that were introduced into the system during the process. Therefore, to solve this problem, industry leaders should work closely with AI tools during feedback analysis and processes to provide checks and balances to the process.
2. Biases in trained models: Some experts have concluded that there will always be biases in AI models, and the best thing we can do is reduce them as much as we can. These AI biases can lead to serious societal problems that cause prejudice and discrimination if not properly managed and dealt with.
3. Data Security Vulnerabilities: As more customer data flows into the system for analysis, it becomes attractive to cyber criminals who may want to breach the system to gain access to customer data if it is not properly secured.
4. Shortage of in-house AI experts: Generally, most AI-based companies encounter this issue. The absence of an in-house expert to control, monitor, and update the AI system causes a huge problem in feedback analysis. There’s a limit to which marketing experts can uncover hidden patterns and discover problems in analysis results, hence the need for an in-house expert to closely monitor the results.

Solutions to Overcome the Challenges

As progressive business leaders, the challenges mentioned above should be for your awareness, while the solutions to these challenges should be your focus. The following are proposed solutions that can be used to overcome the challenges stated above:

1. Invest in industry experts that are skilled at observing patterns in feedback analysis and interpreting the results. Likewise, it is important to invest in a solid IT infrastructure that can process massive amounts of data quickly. Data volume and velocity provide difficulties that can be addressed via scalable systems and cloud-based solutions.
2. AI models should be trained continuously to attain accuracy. Maintaining accuracy in AI models requires regular training and updating. By putting continuous learning processes in place, models are guaranteed to remain relevant in terms of capturing changing customer sentiment.
3. Invest in data security systems to safeguard customer data and prevent data theft or manipulation by cybercriminals.
4. Before deciding on a tool to use, painstakingly explore many options to find the most suitable tool that meets your demands. Also, explore different approaches to implementing these tools, all to attain accuracy in result processing.

Conclusion

As we experience technological advancement, these developments also bring about challenges of which we must not be unaware. Being exposed to the limitations of AI technology helps to relate to it in a more nuanced way, as does implementing strategies in order not to fall short of expectations. Having seen the various challenges highlighted above, industry leaders must endeavor to also pay close attention to the solution when addressing issues that have to do with market research.