AI has officially arrived in the market research industry. Thousands of reports have been automatically generated, AI specialists have been hired, and countless hours have been saved. However, as AI continues to evolve and market researchers have more time to experiment with the technology, more and more use cases are emerging.
Displayr recently published AI Essentials for Market Researchers as a resource to help market researchers navigate this ever-emerging landscape. Here, we'll take you through some of the main opportunities identified in the ebook and show you how you can start implementing AI to save time today.
1) Human-supervised text categorization
Text categorization—the process of sorting open-ended and textual responses into clear and meaningful groups—has been one of the most obvious and valuable uses of AI in market research. It combines machine learning and Natural Language Processing (NLP) to automate the entire process of reading through troves of data and assigning text to categories. This eliminates the manual grunt work and saves market researchers hours upon hours.
However, the scale in which AI-powered text categorization can can be used varies. Most market researchers would love to automate the entire process. However, potential inaccuracy from the AI makes this unrealistic. Instead, it is recommended that market researchers work collaboratively with the AI, checking for potential errors and ensuring each category is relevant to the context of the research. This would involve asking the AI to generate the desired amount of themes, and then manually going through each one to optimize categorization by merging, removing or adjusting each theme.
2) Data Cleaning
We explained how text categorization uses NLP to help us understand the meaning behind unstructured data. The same technology can be used to prepare data for analysis. Data cleaning/tidying is another AI use case for market researchers, whether that means summarizing overly verbose verbatims, translating variables into different languages, or tidying variable labels in large data files (PS You can do this in Displayr).
AI can also help translate data across languages or even detect anomalies that could indicate data entry mistakes. This not only speeds up the analysis process but also improves the overall quality of the insights derived from the data, giving market researchers more time to focus on strategic decision-making rather than spending hours on manual data cleaning.
Although it isn't the sexiest use of AI, being able to quickly and accurately clean this data drastically improves the accuracy of the findings and helps market researchers deliver better results faster.
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3) Writing assistance
As anyone who has used ChatGPT would know, AI can generate fast, accurate, and well-written reports in no time. However, accuracy issues mean it's best to combine the magic of AI with the guidance of human logic.
For example, rather than simply asking ChatGPT to generate the entire report, a more effective use of AI might be using AI to create an executive summary, or any other summary needed in the report.
Researchers can use AI to draft initial versions, but their expertise is crucial in ensuring the final output is accurate, contextually appropriate, and aligned with the research objectives. By combining the speed and efficiency of AI with human judgment, market researchers can produce high-quality reports in less time, enabling them to focus more on interpreting results and making actionable recommendations.
4) Writing code
It is not uncommon for quantitative researchers to have to write a small amount of code when working with data. Previously, this could have been a very time-consuming process. It would either involve researchers saving snippets from project to project or asking for assistance from coworkers.
Now, they can ask the AI to generate the snippet of code in the language they need. For example, someone working in R who wants to convert age in years into categories would ask ChatGPT, "In the R language, write a snipped of code that takes a variable called age, measured in years, and converts it to a categorical variable with categories: Under 18 18 to 30 31 to 50 50 or more Just return the code. No additional information please."
Many data scientists might have some experience with writing code in order to customize
By leveraging AI for coding, market researchers can less time on the technicalities and more time finding insights.
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