European Business Review

Purpose – The purpose of this paper is to provide an overview of predictive analytics, summarize how it is impacting knowledge creation in marketing, and suggest future developments in marketing and predictive analytics for both organizations and researchers.

Design/methodology/approach – Survival in a knowledge-based economy is derived from the ability to convert information to knowledge. To do so, researchers and managers increasingly are relying on the field of predictive analytics. Data mining identifies and confirms relationships between explanatory and criterion variables. Predictive analytics uses confirmed relationships between variables to predict future outcomes. The predictions are most often values suggesting the likelihood a particular behavior or event will take place in the future.

Findings – Data mining and predictive analytics are increasingly popular because of the substantial contributions they can make in converting information to knowledge. Marketing is among the most frequent applications of the techniques, and whether you think about product development, advertising, distribution and retailing, or marketing research and business intelligence, data mining and predictive analytics increasingly are being applied.

Originality/value – In the future, we can expect predictive analytics to increasingly be applied to databases in all fields and revolutionize the ability to identify, understand and predict future developments, data analysts will increasingly rely on mixed-data models that examine both structured (numbers)and unstructured (text and images) data, statistical tools will be more powerful and easier to use, future applications will be global and real time, demand for data analysts will increase as will the need for students to learn data analysis methods, and scholarly researchers will need to improve their quantitative skills so the large amounts of information available can be used to create knowledge instead of information overload.