Ajith Sankaran, Senior Vice President, Course5 Intelligence.
The field of market research has seen paradigm shifts over the years due to forces such as the internet, mobile phones, neural sciences and process automation. Today, generative artificial intelligence (generative AI) is poised to have a truly transformative impact on the market research landscape. This article will explore the profound impact of generative AI on market research, shedding light on the key areas of impact and how significant this will be for the industry.
Impact Of Generative AI On The Market Research Value Chain
Data Collection
Generative AI can aid in improving the agility and efficiency of data collection in different ways. There are already vendors such as Qualtrics and QuestionPro adding generative AI capabilities into their survey tools, enabling rapid survey development. The rich repository of past studies and questionnaires can be used to create custom survey-build solutions that can be fine-tuned to the specific needs of an organization. While it is still early days, there is a potential for a paradigm shift in the data collection by leveraging of generative AI chat-based surveys and metaverse/virtual worlds.
The enhanced search and summarization capabilities of generative AI can be invaluable in desk research and information mining.
Data Management
Generative AI can be used for automated data cleaning and removal of PII data from survey response files. These are relatively easy-to-adopt use cases. Furthermore, generative AI can be invaluable in monitoring data quality in an agile manner. Moreover, with the increased need for bringing in non-survey data streams into market research, the need for more complex data cleaning, data transformation, data classification, etc., is increasing significantly. This is an area where generative AI can make a big impact in driving automation and efficiency.
Data Analysis
One of the most important use cases of generative AI will be for unstructured text and video/audio analytics. Existing approaches of applying AI/ML to unstructured data require training data sets for training the algorithms. Such training data sets are not available for new studies, and some of these studies can have huge amounts of unstructured data. Generative AI can be readily used to mine such unstructured data and generate insights and summaries.
Specifically in qualitative research, generative AI can be a huge help to drive scalability and efficiency, which has been an industry challenge forever. Generative AI enables researchers to tap into vast volumes of customer reviews and customer feedback data in the form of text, audio and video, and run analysis/sentiment analysis at scale to generate insights.
Generative AI can also be used to drive agility in model selection and deployment. These are yet to be perfected for large-scale adoption.
Data Visualization And Reporting
Generative AI can help with insight summarisation, aiding rapid reporting and better data-based storytelling. Leveraging the power of GPT-based image generation tools such as Dall-E, visualization and visual-based reporting can be significantly enhanced. Specifically, when it comes to insight, reporting tools can significantly speed up reporting and visualization.
Meta-Analysis Or Information Synthesis And Insight Democratization
Perhaps the most obvious and arguably the biggest impact of generative AI in market research will be in the realm of information synthesis. The inherent ability of generative AI models to sift through large amounts of data, identify the right sources and stitch together a summarised synthesis is truly revolutionary. Even the best enterprise knowledge management systems today can, at best, throw out a curated list of reports and documents or some “extracts” from these reports when a user searches for insights. With generative AI, all types of users will be able to query the internal market research assets and generate well-triangulated and summarized insights. With the latest developments in generative AI tools, this capability has only increased with the ability of these tools to connect with real-time search results. Generative AI truly drives real insight democratization through self-service and persona-based insight delivery.
As described in a recent HBR article, generative AI can drive a significant increase in question velocity, question variety and question novelty. The article indicates that their research found that 79% of respondents asked more questions when they used generative AI. Specifically, on “novelty,” the article reports: “AI-led respondents to ask unique questions that changed the direction of their team, organization or industry 75% of the time.”
Recommendations
• Take the time to understand generative AI and be an early adopter by identifying specific use cases. The initial wins may be small; however, the longer-term value creation from the adoption of generative AI will be immense.
• Train your teams in the use of generative AI as part of larger data literacy and AI training programs. Give specific focus on training on prompts.
• Use a mix of build and buy, but I recommend not trying to build all the generative AI applications in-house. There is a wide range of specialized vendors and platforms that can be adopted by market research teams.
• Generative AI offers immense potential in market research; however, as with any other field of application, there will be challenges and ethical considerations to be mindful of. Aspects such as data privacy, data security, AI bias, etc., are important and need to be considered and addressed. Recognize and prioritize responsible and ethical AI practices, ensuring transparency, fairness and accountability.
Conclusion
Generative AI is transforming how market researchers gather insights and make informed decisions. A recent report by BCG showcasing a survey of CMOs on generative AI showed that while 70% of CMOs reported that they are using generative AI, the top two applications/use cases were personalization (67%) and insight generation (51%). It is to be noted that insight generation came in second place, even above content creation. I believe organizations that embrace generative AI will be better equipped to navigate the complexities of consumer behavior, innovate rapidly and respond to competition and market trends effectively.
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