Digital ethnography is a research method that provides us with deep insights into consumer motives and needs. Using digital technologies, it examines practices and cultures in both virtual and real spaces. Now, artificial intelligence is helping to further develop digital ethnography.
It combines efficiency with precision. For modern market research, it is indispensable because it enables companies to make data-driven and well-founded decisions based on real behaviors. Despite its advantages, digital ethnography faces several challenges, which can, however, be overcome through the use of artificial intelligence (AI) in market research.
In this blog, we will show how the combination of digital ethnography and artificial intelligence makes market research more efficient and effective.
The 5 biggest hurdles of digital ethnography in market research
Despite its advantages, digital ethnography presents the following difficulties in market research: On the internet, users are not only anonymous but also often operate under pseudonyms. There are also fake profiles and bots that manipulate data. This makes it difficult to verify whether the data is genuine, leading to lower validity of research results.
Data collection in online communities raises several data protection issues. Researchers must ensure that they respect participants’ privacy and anonymize data in accordance with legal regulations. At the same time, they must also adhere to ethical guidelines so that participants trust them.
Digital ethnography often produces vast amounts of data that need to be analyzed. This “Big Data” challenge requires advanced analytical tools and techniques that are not always easily accessible or manageable. Therefore, there is a risk that important patterns or trends may be lost in the mass of information.
Content on the internet is often dynamic and transient; it can be quickly changed or deleted. This volatility presents a major challenge as it makes long-term observation and analysis difficult. Researchers must therefore react quickly and capture relevant data before it disappears.
Digital ethnography requires the use of specific software and platforms to collect and analyze data. This makes research susceptible to issues, for example, when technology fails or the platforms on which communities are active change. Therefore, analysts must always stay up to date with the latest technology to effectively apply their methods.
How artificial intelligence can overcome the challenges of digital ethnography in market research
These challenges show that digital ethnography in market research requires extensive planning, technical adjustments, and new methods to achieve reliable results. Artificial intelligence could be quite a good solution for the problems that arise in digital ethnography.
AI-powered tools can analyze large amounts of data quickly and efficiently, identifying patterns and trends that human analysts might overlook. Additionally, AI technologies can help verify the authenticity of online data by detecting unusual behavioral patterns and identifying potentially fake or manipulated information.
AI in market research can also help ensure compliance with data protection regulations by providing automated tools for anonymization and the protection of personal data.
Another benefit with artificial intelligence for digital ethnography is its ability to facilitate continuous monitoring and real-time analysis. This allows researchers to stay up to date and respond quickly to current developments.
Are there any AI tools in the previous paragraphs that we can specifically mention by name?
Did you know that artificial intelligence is already being successfully used in market research for digital ethnography?
In the 2024 study by digital ethnography pioneer Robert Kozinets, artificial intelligence played a crucial role by analyzing vast amounts of data from the forum r/wallstreetbets on reddit.com over a period of two and a half years. This forum has about 16.8 million followers, and researchers interpreted discussions, memes, and jokes, drawing connections between users’ financial strategies and their daily lives. Using AI-powered analysis, the study demonstrated how digital activism and consumer movements are linked to everyday politics, populist narratives, and cultural references.
Marketing professionals use artificial intelligence to analyze competitors’ activities, assess consumer sentiment, and test new product opportunities. The rapid creation of responsive product concepts can enhance the efficiency of successful products, improve testing accuracy, and accelerate market entry.
Rewriting this sentence in the active voice: With the help of artificial intelligence, they efficiently create product concepts that are precisely tailored to the target audience. This makes them more promising and allows for faster market entry. – Is this what you meant?
Here are two examples: The brand Kellogg’s scans trending recipes (where do they collect them?) that include (or could include) breakfast cereals and uses the resulting data to launch social campaigns centered around creative and relevant recipes. Likewise, L’Oréal analyzes millions of online comments, images, and videos to identify potential opportunities for product innovations. Is this an ongoing process, or did they conduct it in a specific year?
Conclusion
In conclusion, artificial intelligence enhances the accuracy and efficiency of digital ethnography as a market research method by solving technical problems and challenges. This allows digital ethnography to provide deep and efficiently obtained insights into consumer motives, needs, and usage habits with higher validity and secured data protection. This enables analysts to better overcome the associated challenges and optimize their results.
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Sources:
- Harkness, L., Robinson, K., Stein, E., & Wu, W. (2023). How generative AI can boost consumer marketing. McKinsey & Company, Hämtad 2024-05-28 https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/how-generative-ai-can-boost-consumer-marketing.
- Kozinets, R. V., & Seraj-Aksit, M. (2024). Everyday activism: an AI-assisted netnography of a digital consumer movement. Journal of Marketing Management, 40(3-4), 347-370.
- Mantelero, A. (2018). AI and Big Data: A blueprint for a human rights, social and ethical impact assessment. Computer Law & Security Review, 34(4), 754-772.
- Peukert, C., Qahri-Saremi, H., Schultze, U., Thatcher, J. B., Cheung, C. M., Frenzel-Piasentin, A., … & Turel, O. (2024). Metaverse: A real change or just another research area?. Electronic Markets, 34(1), 1-10.