If it seems like every tech company is slinging around buzzwords like “big data,” “artificial intelligence,” and “machine learning,” well, you’re not wrong. The thing is e-commerce companies have a lot of data at their fingertips. But making use of that data is a challenge.

Machine learning is able to make sense of digital data at a much faster rate than any human is capable of. Choosing the application of machine learning tends to be a decision of priorities. Sure, you could use machine learning to do a lot of things, but what will make the largest impact?

It’s safe to say that the priority would be the tech that makes the biggest impact. With this, let’s review the most powerful applications of machine learning technology in e-commerce.

1. Segmentation, Personalization, & Targeting

E-commerce websites suffer a degree of separation from their customers. In person, a salesperson interacting with a customer quickly takes in what they are saying, their body language, behavior, and many other factors in order to help the customer. In effect, the salesperson segments and targets, and personalizes the customer’s experience to get them to buy.

hen offline shoppers have a question, concern, or hesitation, a salesperson is there to give them the right information to nudge them closer to purchase. Online, we have trouble understanding the vast amounts of data needed to be able to provide the same tailored experience; which means it’s very difficult to nudge an on-the-fence shopper closer to purchase. Sale lost.

This is where machine learning makes an impact. Machine learning technology makes it possible to provide optimized experiences that drive sales and increase revenue.

2. Pricing Optimization

Pricing is important. Online pricing is critically important. You can’t just rely on a set markup rate or even the local market price to win the sale. It’s easier than ever to compare prices from one competitor to another with just a few clicks. And shoppers aren’t afraid to get a better deal.

Machine learning technology can change prices to account for many factors at once. Your competitors’ prices, demand, time of day, and type of customer could all influence your price. Machine learning technology makes it possible to adjust prices accordingly.

3. Fraud Protection

E-commerce companies are susceptible to fraud. Chargebacks are just the beginning of the negative consequences of fraud. In some cases, a damaged reputation can permanently tarnish a company’s reputation.

Detecting and preventing this at scale is nearly impossible without the help of machine learning. Machine learning can process the repetitive, tedious data at rapid speed and prevent fraudulent transactions before they happen.

4. Search Ranking

If your shoppers can’t find what they’re looking for, they won’t be able to buy it. We may be too spoiled by Google’s search engine to consider that not all search is intelligent. But often, product searches fall short of delivering results that truly answer the query.

Factors like content, preferences, and similar items all play into providing the optimal search results. Machine learning is able to pull information from deep within the patterns of search and purchases—rather than just keywords.

5. Product Recommendations

Amazon has proven that product recommendations work. Their Recommendation Engine is responsible for 35% of its sales. But it takes a lot of computing power to find the right patterns in product sales and shopping behavior.

Machine learning can do it. It is possible for an intelligent employee to write “if this, then that” rules, but this limits recommendations to only reflect the employee’s knowledge. Machine learning can effortlessly quantify buying behavior over and over again, each time digging deeper into trends.

6. Customer Support & Self Service

Providing quality customer service in e-commerce is challenging. Doing so at scale is daunting. But one answer is to use machine learning technology like chatbots. Intelligent chatbots are able to use natural language to communicate with a customer, identify an issue, and resolve the issue.

Automating customer support and enabling self-service makes it easier for you and your customer to have higher satisfaction. There’s a lot of creativity to how machine learning can be used to help customers, chatbots being just one example. But the intent remains the same: higher customer satisfaction.

7. Supply & Demand Prediction

Forecasting is common enough. But today, with more data than ever before, e-commerce companies are choosing to leave this task to the machines. Not only is machine learning able to process data faster, it’s also able to find unique insights hidden where people weren’t thinking to look.

And forecasting is just the tip of the iceberg when it comes to business intelligence. Machine learning can be applied to a number of analytical goals. With deeper, more accurate information, companies can make data-backed decisions that ultimately lead to better products and services.