Replicating actual market decisions in a survey choice task is an important design goal. However, given that survey respondents have moved to devices with smaller screens, it has become increasingly difficult to accomplish the goal of realism.

Market segmentation is a very powerful tool. When possible, leverage that power by applying the segmentation to your company's customer database(s)

With all the hype and hoopla over generative AI, we decided to do some experimental work to see how well ChatGPT-4 performed versus Decision Analyst’s Deep Learning Model, a multi-layer neural network classification model.

Writing a research report requires planning and attention to detail to ensure that the reporting experience goes smoothly and that it results in a deliverable with actionable insights.

What steps would one take to build such a branding model based on both structured data and unstructured textual feedback from customers? What kind of modeling techniques could be used?

A brief summary of the different types of errors that can occur in marketing research surveys and how to avoid them.

You don’t have to look too hard these days to witness the transformation that artificial intelligence (AI) is having on society. The topic is all the rage at marketing research conferences, too.

Outliers can play havoc with the data. At best, these pesky data values can make individual metrics look a bit “off.” At worst, outliers can lead to erroneous business decisions.

In John Colias' previous blog, he investigated the viability of using AI to segment a market using textual data from survey open-end responses. In this follow-up analysis, he asks the question: If we were to market to the individuals in a text-based segment, would we be able to target them and find them geographically?