In recent times, adaptive machine learning has grown in demand among the marketing fraternity. Also, the availability of vast amounts of data when churned through a machine learning algorithm creates meaningful and proactive marketing decisions and communications relevant to the customer’s wants. Our 4 part of Marketing Week Series speaks on the ability of Machine learning in solving problems of marketing.
Machine learning is a discipline combining science, statistics and computer coding that aims to make predictions based on patterns discovered in data. It is a great way to automate marketing activities to make them less time consuming and to provide better experiences for your customers.
With 89% of marketers saying that their customer experience is going to become their key differentiator this year, it plays a crucial role in the marketer’s best strategy to win. You want to take real advantage of data to convert visitors and increased sales. That’s where machine learning comes in.
How does Machine learning help marketers?
Machine learning-algorithms have also become indispensable in e-commerce. They enable retailers to gain more user knowledge in order to treat them individually. Therefore, retailers first track their visitors’ behavior, save it in a data warehouse, deploy a machine learning-algorithm to model this data and then, accurate next best action predictions can be made.
The key thing to remember is that as you supply machine learning software with more data, it keeps on learning and adapting. Other areas in which a machine learning application can help marketers include:
Customer segmentation – Machine learning customer segmentation models are very effective at extracting small, homogeneous groups of customers with similar behaviors and preferences.
Customer churn prediction – By discovering patterns in the data generated by many customers who churned in the past, churn prediction machine learning forecasting can accurately predict which current customers are at a high risk of churning. This allows proactive churn prevention, an important way to increase revenues.
Customer lifetime value forecasting – CRM machine learning systems are an excellent way to predict the customer lifetime value (LTV) of existing customers, both new and veteran.
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