Many of today’s most prominent companies rely on machine learning algorithms to help better understand their customers and revenue opportunities. There are hundreds of different machine learning algorithms, so even learning the basics can feel like a daunting task.

To help you get started, this post introduces six of the most common machine learning applications for business: customer lifetime value modeling, churn modeling, dynamic pricing, customer segmentation, image classification, and recommendation engines.

1. Customer Lifetime Value Modeling

Customer lifetime value models are among the most important for eCommerce business to employ. That’s because they can be used to identify, understand, and retain your company's most valuable customers, whether that means the biggest spenders, the most loyal advocates of your brand, or both. These models predict the future revenue that an individual customer will bring to your business in a given period. With this information, you can focus your marketing efforts to encourage these customers to interact with your brand more often and even target your acquisition spend to attract new customers that are similar to your existing MVPs.

2. Churn Modeling

Customer churn modeling can help you identify which of your customers are likely to stop engaging with your business and why. The results of a churn model can range from churn risk scores for individual customers to drivers of churn ranked by importance. These outputs are essential components of an algorithmic retention strategy because they help optimize discount offers, email campaigns, or other targeted marketing initiatives that keep your high-value customers buying.

3. Dynamic Pricing

Dynamic pricing, also known as demand pricing, is the practice of flexibly pricing items based on factors like the level of interest of the target customer, demand at the time of purchase, or whether the customer has engaged with a marketing campaign. This requires a lot of data about how different customers’ willingness to pay for a good or service changes across a variety of situations, but companies like airlines and ride-share services have successfully implemented dynamic price optimization strategies to maximize revenue.

4. Customer Segmentation

Rather than relying on a marketer’s intuition to separate customers into groups for campaigns, data scientists can use clustering and classification algorithms to group customers into personas based on specific variations among them. These personas account for customer differences across multiple dimensions such as demographics, browsing behavior, and affinity. Connecting these traits to patterns of purchasing behavior allows data-savvy companies to roll out highly personalized marketing campaigns that are more effective at boosting sales than generalized campaigns.

5. Image Classification

Image classification uses machine learning algorithms to assign a label from a fixed set of categories to any image that’s inputted. It has a wide range of business applications including modeling 3D construction plans based on 2D designs, social media photo tagging, informing medical diagnoses, and more. Deep learning methods such as neural networks are often used for image classification because they can most effectively identify relevant features of an image in the presence of potential complications like variation in the point of view, illumination, scale, or volume of clutter in the image.

6. Recommendation Engines

Recommendation engines are another major way machine learning proves its business value. In fact, Netflix values the recommendation engine powering its content suggestions at $1 billion per year and Amazon says its system drives a 20-35% lift in sales annually. That’s because recommendation engines sift through large quantities of data to predict how likely any given customer is to purchase an item or enjoy a piece of content and then suggest those things to the user. The result is a customer experience that encourages better engagement and reduces churn.

Want to learn how to effectively implement these or other data science techniques to generate value in your business? Check out our data science resources to discover how machine learning can help you unlock the value of your data.