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Machine learning use cases in marketing

Machine learning use cases in marketing

Machine learning in marketing for consumers, businesses, and practitioners

Marketing is an interesting place to start exploring the applications of machine learning because it impacts all of us as consumers. On the other hand, businesses will find that marketing is quite a fertile ground for machine learning. As a practitioner, it is fascinating to find how one can find insights from the data to help a business  identify, anticipate, and satisfy customer needs and wants. What are some examples of machine learning use cases in marketing?

Machine learning use cases in marketing

Churn or attrition modelling can help predict the chances of a customer leaving. It can also help determine the key drivers of churn and serve as a metric in daily business operations to minimise chances of churn. Meanwhile, cross-sell and up-sell models can find opportunities to provide a comprehensive purchase experience for the customer.

A customer lifetime value model determines the length of customer engagement with survival models. Classification models can help estimate the probability of customers leaving. Another avenue of interest is projected revenue based on knowledge of revenue made from a customer thus far. Meanwhile, a loyalty model can determine key drivers of customer loyalty.

The marketing mix model can help target the right audience at the right time with the right frequency through the right channel at the right price. This is a very powerful combination of insights for the business to meet or even exceed customer expectations. It benefits the consumer because they would receive highly relevant offerings tailored to their needs and wants.

Sales forecast models helps estimate short-term and long-term sales. It uses time series, multiple regression, and many other types of machine learning models. On the other hand, price elasticity models can measure the change in demand with respect to the change in price. This way, revenue may be optimised. Another way to optimise marketing decisions is through A/B testing models.

Market segmentation models can help find the optimal number of market and customer segments. Through these models, we also discover different types of segments. From there, we can design marketing strategies that fit each respective segment well.

Machine learning in marketing and our daily lives

Machine learning leverages data to make predictions, find interesting patterns, and perform particular tasks without explicit instructions. It may sound boring to most folks, but there are in fact many exciting applications of machine learning in business and our daily lives. There’s a gamut of machine learning use cases in marketing alone. Marketing can use machine learning as an optimisation engine to enhance strategic and tactical decisions, and keep customers happy.