"For most marketing applications, data analysts typically utilize three basic approaches:"

"Descriptive — applied to data from past events"

"Predictive — used for forecasting and planning;"

"Prescriptive — used to determine optimal courses of action."

"Of the three, predictive and prescriptive are most commonly used to build ML algorithms while descriptive analytics applies mostly to reports and dashboards."

"Product recommendation"

"When incorporated into a prescription analytics and personalization model, product recommendations are intended to boost conversion rates, average order value and other key metrics."

"Churn rate prediction"

"To do this effectively, however, the ML model requires access to some highly specific predictive data, such as recent purchase history or average order value."

"Uplift"

"ML is also highly adept at gauging the incremental effect of a marketing campaign at the user level, as well as revenues, sales and other data, and then making predictions as to how this uplift will play out into the future."

"Recurring purchases"

"Customer analysis"

"ML brings equally powerful tools to the popular RFM (Recency, Frequency, Monetary Value) analyses that drive many marketing strategies these days. At both speed and scale, ML vastly improves the ability to quantitatively rank and group customers to develop targeted marketing campaigns."

"Dynamic pricing"

"Of these, the most effective is predicting supply and demand."

"In this way, pricing models are built on data, not hunches, although marketing executives can always establish limits as they see fit, including not reducing prices at all."

"One thing to keep in mind is that ML won’t provide significant benefits if it only has limited data to learn from."