Traditions in market research are brief and often taken down by advancements in technology and technique. The balance between man and machine is crucial to handling endless streams of data.
In order to keep up with the changing industry and the increasing demand for faster insights, we need to use technology to help us gather and analyze data more quickly.
The whole arena surrounding computational text analysis has shifted to be more reliant on AI-based approaches. In the past, human-based analysis was the only way to go. However, this approach had a lot of limitations, such as being limited to only skimming the text and being biased. Computational approaches that incorporate all the text while throwing away what the computer views as non-essential information are now replacing this methodology. Although these machine-based approaches can have trouble understanding all of the text's meaning, they are much faster at analyzing large amounts of data.
We can see that AI-based solutions can help us to shoulder a lot of the work so that humans can do what we do best. Machines are good at finding data points and patterns, which allows us to tell better stories. Humans can then come in and add color and intelligence to the data. This human-machine tension is something that will continue to exist as we move forward. Our goal is to make machines as smart as possible so that we can use our limited time in the best way possible.
We are moving away from using crosstabs to analyze data and are instead using machines to find the differences that matter. This changes how we view classical statistics because false positives become statistically inevitable.
Bayesian statistics can be used to test a hypothesis by incorporating the data into the analysis. This allows researchers to see if the hypothesis is supported by the data, rather than relying on tests for significance which may not be accurate.
There are many different models that are changing the way we do our jobs in market research. New tools, based on different principles, are giving us new opportunities. With all these opportunities, we are challenged to work in a new way and learn new skills.
We need to change the way we do things, because the industry and our clients' industries are moving too quickly for us to hesitate. Researchers should embrace this new paradigm so that they will have the skill advantage. They should try new tools, even if they don't understand how they work, because many of them can help them do what they do better. Doing things in new ways can lead to better, faster insights.