The same goes for the e-commerce industry; no one would have thought it would become what it is today. Machine Learning has many applications in the e-commerce industry that go far beyond analytics.

Machine Learning has many applications in the e-commerce industry that go far beyond analytics.

Artificial Intelligence, and in particular the subset of Machine Learning technology, is having a profound impact on e-commerce businesses.

Before we get into the nitty-gritty, it's essential to understand what Machine Learning is. At a basic level, it is a process by which a machine can learn. It is an application of the broader technology field of artificial intelligence. It involves creating algorithms or programs that have the ability to access and learn from data. All this without having to be programmed by a human.

The way these algorithms "learn" primarily patterns recognition. The learning algorithm is trained by inputting as much data as possible. It then analyzes the information and finds patterns in it. Finally, the algorithm is "smart" enough to apply what it has learned to new data sets.

Machine Learning algorithms are generally classified into one of three categories:

• Supervised: They apply what has been learned in the past to new data using specific labeled examples. They can predict future events and compare their results to expected results. This helps the algorithms improve with "practice".
• Unsupervised: These algorithms analyze unlabeled and unclassified data. There are no specific examples on which to base predictions. Such programs, therefore, draw conclusions.
• Reinforcement: Reinforcement algorithms interact with their environment to test results. Through trial and error, the programs discover the correct behavior. They then adapt their future responses accordingly.

Differences between Machine Learning and Artificial Intelligence

Machine Learning is a subset of Artificial Intelligence. Machine learning technology uses data to predict or execute actions. The more data the technology is exposed to, the more accurate its results become. Thus, algorithms in this field can be described as being able to "learn". 

Deep Learning is also another subset of AI and, in many ways, machine learning. This is where complex networks analyze and learn from massive data sets. This is the volume of information that only becomes available in the era of Big Data.

Now it's time to elaborate on the impact of technology on the online shopping experience. Here are six use cases for Machine Learning in e-commerce:

1. Personalization

Today's consumers don't want to be treated like just another customer. They prefer a highly personalized customer experience. It is this type of personalization that builds brand loyalty.

AI, specifically Machine Learning, is the only way to deliver high-level personalization online. Algorithms analyze customer data and behavior to tailor the user experience to each site visitor.

2. Improved Search and Display

Machine Learning can help users find exactly what they want based on their queries. Currently, users find products on an e-commerce site using keywords, so the site owner needs to make sure they have assigned those keywords to the products users are looking for.

 3. Better inventory management

One of the persistent problems of e-commerce is inventory management. Sellers sometimes oversell, deliveries can take too long or forecasts have to be wrong. This affects the standard e-commerce motto of delivering the right products to the right place in the right quantity. Inventory management can be laborious if done manually - which can impact the accuracy of sales forecasts and lead to cash flow issues.

Machine Learning can make forecasting future demand much more accurate. Not only will this make supply chain management easier, but it will also allow the company to better understand its customers and their behaviors.

Using Machine Learning algorithms, inventory replenishment can be automated based on historical and current sales analysis.

4. Churn Management

Customer churn is often discussed in the B2B niche. It is the rate at which customers abandon one brand - potentially in favor of another.

Predicting churn involves using data about existing and previous customers to find patterns. What behaviors, for example, do customers engage in when they are about to switch? These are the insights that Machine Learning algorithms can provide.

With this knowledge in hand, the company will be able to identify those who are about to leave. Then, it can adapt its marketing campaigns in order to keep them on board. 

5. Fraud Detection

The larger the amount of data, the easier it is to detect anomalies. Machine Learning can identify patterns in the data, determine what is "normal" behavior, and alert administrators when something is not "normal."

In this era of cybersecurity awareness, the most common application is fraud detection. The problem of customers purchasing with stolen credit cards, or retracting after the item is delivered, is ubiquitous in e-commerce. It is nearly impossible to detect and prevent this type of fraud without Machine Learning that quickly processes repetitive data to detect fraud before it occurs.

6. Improving customer service: Chatbots

Chatbots can enable a more "human" conversation with users by understanding structured data. Using Machine Learning, chatbots can be programmed with general information to respond to customer requests.

The more the robot interacts with people, the more it will be able to understand an eCommerce site and its products/services. The more complex the learning, the more things chatbots can do, such as identity upsell opportunities, deliver personalized coupons by asking questions, and address long-term customer needs.