“The ability to analyze data in-depth and contextual ways, combined with advanced algorithms, allows us to uncover correlations and trends that might have remained hidden in traditional research practices.”
THE EXPONENTIAL WAVE OF AI IN MARKET RESEARCH
The exponential evolution of Artificial Intelligence over the past five years—particularly since 2022—has marked a significant milestone in the way we conceive and conduct market research. From its beginnings to its current complexities, AI has evolved from a mere promise to a transformative reality , enhancing the human capacity to explore and understand the world around us.
In its early applications, AI focused much more on automating processes, relieving researchers of repetitive and time-consuming tasks. However, its role has evolved into an intelligent agent capable of analyzing large volumes of 
, and offering insights that might otherwise go unnoticed by the human eye.
Today, it’s already possible to capture audience perceptions and responses to your products or services in real time: artificial intelligence-based technologies are breaking new ground in human behavior research and enabling the creation of innovative options designed to enhance customer relationships.
Let’s look at the different ways AI can be used in market research processes:
As for applications related to the emotional and behavioral analysis of the consumer , we find:
- Sentiment analysis can help researchers identify the sentiment behind written responses, allowing them to understand the emotional impact that campaigns, products, or services have on consumers and users. One type of sentiment analysis is confidence metrics: another technology that measures the level of certainty or conviction expressed by respondents in their answers, allowing researchers to gain a deeper understanding of consumer behavior.
: This can help researchers analyze tonality, inflection, and other vocal cues in spoken responses, providing additional insight into consumer attitudes and behaviors.- Facial coding: Allows for the analysis of micro expressions and emotional responses. It can provide valuable insights into consumer behavior and preferences.
In relation to efficiency and automation in data analysis , AI can have -among others- the following applications:
- Efficient processing of large volumes of data in a short time: This simplifies analysis management by automatically identifying relevant information in large amounts of data and making it easier to obtain relevant insights. Thus, the concept of machine learning has gained relevance, helping to design systems capable of making decisions automatically when analyzing these large data sets.
- Prediction and modeling: Allowing us to predict future social behaviors or outcomes. This allows researchers to anticipate trends and events, which can be valuable for planning and decision-making.
- Automation of repetitive tasks (such as data collection and sorting): allowing researchers to spend more time analyzing and interpreting results.
- Improved accuracy and consistency of analysis: By using algorithms and models, consistent criteria and standards can be applied to data analysis, helping to minimize subjectivity in the results.
And finally, regarding market understanding:
- Identifying patterns and trends: which can reveal significant relationships, connections, and social phenomena that may go unnoticed.
- Segmenting and personalizing data based on individual or group characteristics makes it easier to understand the specific needs, preferences, and behaviors of different segments of the population.
- In-depth market analysis: enabling real-time understanding of the competitive landscape and its impact on consumers, enabling rapid and effective decision-making. Deep learning has gained relevance in this field. It detects both customer intentions and behavior and provides essential data for gaining a deeper understanding of user behavior related to products or services, their market demand, and how they meet consumer needs.
CONCLUSION
In short, the evolution of AI has propelled market research to new heights, transforming insight gathering into a dynamic, efficient, and deeply enriching process. This shift does not imply the replacement of human intelligence, but rather a unique collaboration between humans and algorithms that empowers informed decision-making and fuels a cycle of continuous improvement in knowledge discovery.
As we have seen, the path toward this promising future is not without challenges. AI, although proven, faces obstacles ranging from technical issues to moral questions. Consciously and these challenges is essential to realizing the promise of more efficient, accurate, and ethical research. The ability to navigate these obstacles with caution and responsibility will not only ensure the continued success of integrating AI into research, but will also enable this technological advancement to benefit society in a sustainable and
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[1] Marín, JA (2023, September 14). “This is how AI can help in market research.” La Razo. Retrieved from https://www.larazon.es/emergente/asi-como-puede-ayudar-investigacion-mercados_20230914650308cc1fb4a6000132285c.html
[2] Retold Marketing. (2022, June 6). “How to benefit from artificial intelligence in market research.” Retold. Retrieved from https://letsrebold.com/es/blog/inteligencia-artificial-investigacion-mercados/
[3]Editorial Team. (May 23, 2023). “How Artificial Intelligence is Transforming Market Research.”. Retrieved from https://cepymenews.es/inteligencia-artificial-investigacion-mercado/
[4] its’d Foundation. (2023, May 16). “Advantages of artificial intelligence in social and market research.” its’d Foundation. Retrieved from https://isdfundacion.org/2023/05/16/ventajas-de-la-inteligencia-artificial-en-la-investigacion-social-y-de-mercados/
[5] Diligences Digital Editorial Team. (2023, September 6). “Artificial Intelligence, a Boost for Market Research.” Diligences Digital. Retrieved from https://dirigentesdigital.com/tecnologia/inteligencia-artificial-impulso-para-la-investigacion-de-mercados/
