Published Jan 24, 2025
Gene Saykin, VP/Head of Quality Strategy at Toluna
AI is transforming the data game, enhancing the speed, accuracy, and depth of insights that drive today’s business decisions. However, the value of AI-driven insights hinges on the quality of data.
In this article, we’re exploring the dual nature of AI in market research—how it can enhance data quality and insight generation while also introducing risks that need to be carefully managed.
Just as a high-performance car requires quality fuel, AI needs high-quality data to produce reliable insights. Poor data quality (from incomplete, outdated, or biased data) can lead to flawed outcomes and misguided business decisions.
AI’s reliance on data also means that it can exacerbate existing data quality issues if not carefully managed. For instance, biased data fed into an AI model can result in skewed insights that perpetuate and even amplify these biases, leading to detrimental decision-making. Overreliance on AI without putting in the effort into all the “boring” stuff that goes into making AI great can lead to major issues for researchers.
While AI offers tremendous benefits, it also opens the door to new risks that threaten data quality. One growing concern is AI-enhanced fraud. Malicious actors can exploit AI to manipulate data, creating fake survey responses, skewing sentiment analysis, spamming your company’s emails, scraping your whole entire company’s directory via Linkedin, training AI LLMs to pretend to be specific B2B participants based on their Linkedin profiles, reverse engineering the algorithms you’re using for some of your quality checks, or generating synthetic data to alter research outcomes. These are just to name a few, but the list is much longer. As AI tools become more accessible and sophisticated, these threats are only growing.
Biased data is another growing risk: AI models trained on biased data often produce skewed insights that reflect these underlying biases. For example, if a dataset overrepresents a particular demographic group, the insights generated are likely to disproportionately favor that group’s preferences and behaviors. This can lead to decisions that do not accurately reflect the broader market, potentially alienating key customer segments and leading to ineffective strategies.
Biases can stem from the researchers themselves; for example, by determining which respondents to clean out of their data sets. The amplification of biases through AI can have far-reaching consequences, which is why it’s crucial for market researchers to ensure that their data is as unbiased and representative as possible.
Adding to the mix is the rise of AI-generated survey responses, which mimic human behavior closely, providing varied answers, completing surveys at different times, and even imitating the thought patterns that real participants might exhibit. Unfortunately, the anti-bot checks of yesteryear are not equipped to address this new wave of fraud. This makes it increasingly difficult for researchers to detect fraudulent data, particularly in large datasets where manual review of each response is impractical. This level of sophistication poses a significant challenge to market researchers, who must find ways to filter out these fraudulent responses to maintain data integrity.
The impact of these issues on data integrity can’t be overstated. What’s even more concerning is that the credibility of the entire industry is at stake. If clients and stakeholders begin to doubt the reliability of the data produced by market research firms, they may become hesitant to invest in these services, leading to a potential decline in the industry’s value and reputation.
To combat these risks, market researchers must adopt advanced detection techniques that go beyond traditional methods. Techniques such as digital fingerprinting – which tracks the digital traces left by respondents – and the use of CAPTCHAs and trap questions to filter out bots, for example, are becoming increasingly necessary.
Furthermore, real-time monitoring of survey responses, combined with periodic audits and cross-referencing of data sources, can help identify and mitigate the impact of fraudulent activities. The key is to stay one step ahead of fraudsters by continuously evolving these techniques in response to new and emerging threats.
By adopting advanced tools and techniques, the industry can mitigate the risks posed by AI-enhanced fraud and continue to deliver reliable, actionable insights to businesses. Let’s look at a few ways we can stay vigilant:
1. Rigorous data validation
The first line of defense in managing AI-related risks is rigorous data validation. Given that AI tools are both powerful data processors and potential vectors for introducing errors or biases, it is crucial to use advanced AI techniques to detect and correct anomalies in data. These tools can help identify inconsistencies that might otherwise go unnoticed, such as unusual patterns in survey responses or outliers in datasets that could indicate fraudulent activity.
It’s also crucial to make sure that the models are free of bias from the model developers and feedback sources. When we were developing gibberish detection models at Toluna, we adjusted the models for Gen Z speak and other types of communication used by different demographic groups.
2. Continuous monitoring and adaptive detection
AI-enhanced fraud evolves as malicious actors develop new tactics to circumvent defenses. Continuous monitoring and adaptive detection techniques are essential for staying ahead of these threats, as they can detect evolving patterns of fraudulent behavior, allowing businesses to respond more effectively. Regularly updating detection systems protects the accuracy and credibility of their research.
Adaptive detection techniques use machine learning to recognize new fraud behaviors, such as subtle changes in response patterns that suggest bot activity or unusual survey spikes (indicating manipulation). With continuous monitoring, these systems learn and adapt over time, becoming increasingly effective at identifying and mitigating risks.
3. Ethical AI practices
Upholding ethical AI practices is key to ensuring the long-term reliability and fairness of AI-driven insights. This involves being transparent about the development and use of AI models and conducting regular audits to check for biases and other ethical concerns. Ensuring that AI systems are designed to avoid reinforcing existing biases or introducing new ones into the decision-making process is also important.
4. Multi-layered verification and real-time response
Given the increasing sophistication of AI-enhanced fraud, a multi-layered approach to verification is essential. By employing a combination of automated and manual processes – like digital fingerprinting, CAPTCHA challenges, and manual reviews of high-risk responses – businesses can effectively identify and eliminate fraudulent data before it skews research outcomes.
Real-time response capabilities are also crucial. If an unusual pattern of responses is detected, immediate intervention can prevent those responses from being included in the final dataset, thereby preserving the integrity of the research.
We’ve spoken a lot about the risks associated with AI in market research, but it’s not all doom and gloom! AI brings incredible opportunities to enhance data quality and drive better business outcomes. AI isn’t just a tool for automation; it is a transformative technology that can elevate the quality, efficiency, and depth of market research in unprecedented ways.
Let’s look at a few ways how:
1. Enhanced accuracy
One of the most compelling benefits of AI is its ability to enhance the accuracy of data collection and analysis. AI can swiftly process vast datasets and identify errors that human researchers might miss, for example, anomalies in survey responses. This level of precision ensures accurate, reliable data collection, leading to more informed and effective decision making. And with AI’s capacity for continuous learning, these systems can improve their accuracy over time, enabling businesses to consistently rely on high-quality data for their market research needs.
2. Efficiency gains
AI offers significant efficiency gains by automating time-consuming tasks like data cleaning and analysis via agentic workflows. For example, AI-powered tools can automate the categorization of open-ended survey responses, quickly identifying key themes and trends without the need for manual coding. This not only speeds up the analysis process but also reduces the likelihood of errors, ensuring that the final analysis is both accurate and comprehensive. Not to mention the time saved through automation allows researchers to delve deeper into the data, exploring complex patterns and correlations that might otherwise go unnoticed.
3. Advanced insights
Traditional analytical methods are often limited by the volume and complexity of data they can handle. In contrast, AI can analyze large, complex datasets at scale. AI-driven predictive analytics can forecast future market trends based on historical data, enabling businesses to anticipate changes in consumer behavior, optimize product offerings, and tailor marketing strategies to better meet customer needs.
4. Improved communication
AI tools can enhance the way researchers interact with data, providing more intuitive ways to explore and visualize information. One example is large language models (LLMs), which can process vast amounts of data to understand human language and generate text – like ChatGPT. LLMs can simplify complex information, break language barriers, and personalize communication.
AI can also facilitate better communication between businesses and their customers. Think about sentiment analysis tools, which analyze customer feedback in real-time to provide insights into how products or services are being perceived. It can also be a conversation, or a text with the person in real time, which can lead to all sorts of agentic workflows.
The opportunities presented by AI go beyond improving existing processes—they also open the door to entirely new ways of conducting market research. By driving innovation, AI empowers businesses to be more agile, responsive, and ultimately successful in their market research efforts. The future of market research is bright – and AI is undoubtedly at the forefront of its evolution.
That said, simply incorporating AI into your business process without taking data quality into consideration can cause more harm than good. Managing the risks associated with AI in market research is key. By focusing on rigorous data validation, continuous monitoring, and ethical AI practices, businesses can mitigate the risks and fully harness the power of AI to drive more effective and insightful research.
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