4 Business Uses of Machine Learning

1. 4 Business Uses of 4 Business Uses of Machine Learning Machine Learning Technology has progressed, and most prominent companies use machine learning algos to understand their customers better and open new doors for revenue streams. There are many machine learning algorithms, and learning the basics can be highly challenging. However, here are the key business uses of neural network machine learning. CLV Modelling Most e-commerce companies rely on customer lifetime value models. It helps them to identify, understand, and retain the most valuable customers. It could mean the biggest spenders or the most loyal advocates of the brand. Using these models helps to predict the future revenue that the individual customer will bring to your business in a certain period. With this information, marketing efforts could be more focused. These customers can be offered incentives to interact with your brand more often and even target the acquisition spend to attract new customers who are similar to the most valuable ones.

2. Churn Predictions With the help of customer churn modelling, you can identify which of your customers will most likely stop engaging with the business and why. Using the model, you can assign risk scores to individual customers and rank them. These outputs are essential components of an algorithm for the retention strategy. Using these, you can plan the discount offers, email campaigns, and other similar initiatives to ensure that the high-value customers keep buying from you. Dynamic Price Setting It is also known as demand pricing. It is the practice of pricing the items flexibly based on the factors like the customer's level of interest, demand while making the purchase, and engagement of the customer in the marketing campaign. It requires a lot of data on how the willingness of the customers' changes. Companies like airlines and ride-sharing services have implemented these strategies to enhance revenue. Segmenting Customers Instead of relying on the marketer’s intuition to separate the customers into groups for the customers for campaigns, the data scientists can use clustering and classification algos to create customers' groups into personas based on individual variations amongst them. The personas factor the differences like the

3. demography, browsing behaviour, and affinity. By connecting these traits to the purchasing behaviours, the data-savvy companies can roll out highly personalized marketing campaigns that effectively increase sales. Finally, Besides these applications, neural network machine learning is also used for image classifications, giving recommendations. It’s wonderful tech that is only growing in usage.