Predictive Marketing: Anticipating Market Demand with Proactive Action
George Cosmin Tanase
Romanian Distribution Committee Magazine, 2022, vol. 13, issue 2, 34-39
Traditionally, marketers rely on descriptive statistics that explain past behavior and use their intuition to make smart guesses on what will happen next. In predictive analytics, most of the analysis is carried out by artificial intelligence (AI). Past data are loaded into a machine learning engine to reveal specific patterns, which is called a predictive model. By entering new data into the model, marketers can predict future outcomes, such as who is likely to buy, which product will sell, or what campaign will work. Since predictive marketing relies heavily on data, companies usually build the capability upon the data ecosystem they have previously established. With foresight, companies can be more proactive with forward- looking investments. For instance, companies can predict whether new clients with currently small transaction amounts will turn out to be major accounts. That way, the decision to invest resources to grow the specific clients can be optimal. Before allocating too many resources into new product development, companies can also use predictive analytics to help with the filtering of ideas. All in all, predictive analytics leads to a better return on marketing investment. Predictive modeling is not a new subject.
Keywords: Analytics; Neural Network; Customer Lifetime Value; Retention Strategies; Machine Learning; Artificial Intelligence (search for similar items in EconPapers)
JEL-codes: L81 M31 O33 O35 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:rdc:journl:v:13:y:2022:i:2:p:34-39
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