Market Research Analysts’ New Job: How AI is Changing the Role, Not Replacing It

Market Research Analysts' New Job: How AI is Changing the Role, Not Replacing It

In a sense, generative AI in market research is not doing anything that researchers weren’t doing already. They have always generated, collected, analyzed, and utilized data. What AI has done is empower them to do all that at deeper, more robust, game-changing levels and do it faster.

Astute researchers know that, depending on the quality of the technology employed to utilize generative AI tools for research, what they receive is essentially regurgitative AI. Facts, stats, and figures that have been collected, organized, and presented but do not provide anything original – merely raw material for crafting ever more robust plans.

By automating report generation and simplifying data comprehension, AI in market research streamlines processes while saving time and resources for more complex decision-making. It is especially useful in market research for processing and learning from unstructured data, which is often underused because of its complexity. However, unstructured data, such as rich media, surveillance, audio, and past purchasing data is abundant and valuable. The latest report from Gartner estimates that up to 90% of all enterprise data is either unstructured or semi-structured, and most are not used to their full potential. Maximizing automation to capitalize on deeper insights is the only way forward. In fact, considering the estimated 65% yearly growth rate of unstructured data in most organizations, the importance of generative AI tools for research will only grow.

But that is all about the quicker processing of things we know. 

What are humans to do when we need to discover the unknown? 

While textual data continues to expand in volume and importance, a true understanding still relies on written or spoken words gathered through focus groups, social media, or review platforms. Natural language processing is an obvious boon to market research, and it sits at the very crossroads of AI, ML, and computational linguistics. Advancements in word embedding comprehension enable automatic sentiment analysis and instant recognition of semantic patterns, and this has presented remarkable opportunities for researchers to assess opinions, preferences, and trends. Accelerating the segmentation process means more time for more insightful analysis, which in turn drives more effective marketing strategies, elevating campaigns and enhancing user experience.

However, what NLP does not do, and probably never will, is replace a real, live human, whether that be a focus group moderator or market research professional. And that gets to the very heart of how AI is changing – not replacing – that person’s role.

Great research explores the unknown and provides significant competitive advantages through genuine discoveries rather than synthesized ones. The development of generative AI tools for research has progressed to the point where it can compete with human thinking with astonishing accuracy, quality, and speed. However, it cannot match human intuition, emotion, or even common sense. It also cannot fully account for cultural nuance, body language, or proper use of local slang or localized translations. 

In fact, three key areas in which humans cannot be replaced are:

Validating AI’s Validity

Validating AI’s Validity
With the emergence of AI, the landscape of Market Research Analysts is undergoing a profound transformation, yet the pivotal role they play remains irreplaceable. AI’s rise has ushered in a paradigm shift, redefining the contours of the profession. While AI algorithms exhibit remarkable capabilities, they often cloak themselves in opaqueness, drawing exclusively from the data upon which they were trained. This phenomenon has given rise to concerns that echo through the corridors of transparency and accountability.

Here’s the crucial realization: AI is not an autonomous entity, but rather a creation of human ingenuity that necessitates initial training and perpetual human validation. The metaphor of a copy spawning another copy is apropos here. Just as unchecked replication can distort the essence of the original, AI outputs that venture deep into feedback loops might eventually resonate with an echo of the truth, yet sever their connection with the comprehensive reality. 

This potent understanding underscores the role of the market research analyst as a synergistic partner with AI. The analytical prowess of AI can effectively undertake the labor-intensive tasks of sifting through vast troves of data performing intricate operations like conjoint analyses and cross-tabulations. This, in turn, empowers these analysts to ascend the intellectual ladder, engaging in profound critical thinking exercises that validate the very validation process itself.

In essence, the integration of AI amplifies the market research analyst’s capacity to extract insights from the avalanche of data, thus liberating their cognitive faculties to delve into the nuanced nuances of validation. This synergy heralds a new era of market research, wherein analysts harness AI as a potent tool for information distillation, enabling them to discern not just the echoes, but the harmonious symphony of truths that underlie the ever-evolving consumer landscape. As the evolution continues, one thing remains steadfast: the human touch, with its capacity for discernment, creativity, and contextual understanding, is the compass that navigates this transformational journey.

Understanding Human Understanding

Understanding Human Understanding
AI cannot comprehend or respond to distinctly human intangibles, such as life experiences or emotions, that go into real-life decision-making. Even semiotics are in between worlds betwixt tangibles and intangibles. On one hand, they are symbols our brain rapidly translates, but the complexity of doing so is layered with culture and past experiences some of which humans struggle to put into words themselves. 

The semiotics of color alone are a great example of something even a local child would know subconsciously, but outside of the culture, the color references could be dangerously interpreted. For example, the color yellow in China is incredibly tied to pornography and salacious media. But in Japan, the color symbolizes bravery, wealth and refinement. 

Contextualizing the Context

Contextualizing the Context
In the realm of cultural insights and market research, context is key. AI is great at summarizing large data sets and helping us overcome assumptions. A good example is the assumption brands often make that the Japanese youth are committed to environmental sustainability. But the record clearly shows perhaps an unexpected truth: it is the older generation that displays a greater level of environmental concern and willingness to make sacrifices for the cause. 

Where AI is not particularly flexible in applying its predetermined knowledge sets, so it cannot consider social, ethical, or moral conventions. To understand the intricate interplay of cultural nuances that shape behaviors humans are needed, specifically experience in exploratory ethnography. Much like a skilled detective, exploratory ethnography involves delving beyond surface-level data. It requires engaging with individuals, listening to their narratives, cultural immersion, and deciphering the contextual layers that influence their actions. This human touch is essential in decoding the “why” behind the statistics—uncovering the hidden and deeper meaning of stories that numbers alone cannot convey.

This need for context also highlights the need for adaptive thinking and innovation during the research process – especially during highly iterative human-centric design research or UX studies. The assumption that a particular approach will resonate with a target audience may not hold true when cultural intricacies are considered. This is where the true power of ethnographic research shines, as it opens up new avenues for engagement that were previously unexplored. By acknowledging the cultural drivers and historical influences, we can craft messages and strategies that deeply resonate with the audience, fostering a more meaningful connection.

In essence, the fusion of AI-driven analysis and human-driven qualitative exploration creates a dynamic synergy. The former provides a foundation of insights, while the latter adds depth, empathy, and context. For companies seeking novel opportunities and superior outcomes, embracing this holistic approach offers the potential to uncover hidden potentials and reshape strategies for success.

In the end, what AI can do is serve as a powerful tool to help bring the market research pro to the starting line, quickly and based on solid data and analytics. However, it still takes an actual, flesh and blood, thinking and feeling human to grasp and interpret the profound complexities in any research endeavor and take the project across the finish line. When delivering into the unknown, a human is needed to explore what’s next for humans.

Seeking human expertise for your research? Look no further. Our team provides quality research with the human touch needed to interpret meaning and direction.

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