Users enjoy it when companies can guess their thoughts. In an ideal world, our favorite food chain creates weekly shopping lists for us. The fashion brand sends us a list of new clothes that complement our current wardrobe. We don’t have to put in a lot of effort to get the necessary things. That’s the (near) future, but even today, machine learning technology has many uses in the e-commerce industry that extend far beyond analytics. Today, we will look at the most important use cases.
Machine learning in the service of e-commerce
Before you get to the bottom of it, you need to understand what machine learning is. At the basic level, it is the process in which machines can learn themselves. Machine learning algorithms and apps access and learn from data without additional human supervision or programing.
The machine learning algorithms are trained by analyzing as much training data as possible. Then they analyze the provided information and find trends and patterns within it. Ultimately, the algorithm is “smart” enough to apply what it has learned to new data sets and execute a task it was made for in the first place. Now is the time to explain the impact of this technology on online shopping. Here are six use cases of machine learning in e-commerce.
#1 PRICE OPTIMIZATION
Price is one of the most important criteria for online buyers, so it’s essential to ensure your offer is competitive and reflects the market. Price optimization is about finding the optimal price your customers are willing to pay for a specific product or service. While it may sound simple, it can be complicated at times, especially if you are running an international e-commerce business to find a price everyone is happy with, as it means estimating the value of items in other countries and currencies.
That is where AI comes to the rescue again with a dynamic pricing feature, which can be automated and set the fittest price for your goods. That can improve conversions and sales as customers typically check the product from different vendors before purchasing. Amazon uses this feature and logs over 2,5 million price changes every day.
#2 CONTENT PERSONALIZATION
Appropriately personalized content on a website or mobile application increases conversion and customer engagement. Machine learning algorithms can help you understand what content will drive the user to take action. You can further enhance your website experience by creating dynamic content recommendations for different types of audiences, just like Netflix does. Their machine learning platform changes the illustrations based on what you’ve watched in the past. In an e-shop, machine learning algorithms can find patterns in customer behavior, and based on them, they can adapt the content on the store’s website to them.
#3 OPTIMIZATION OF PRODUCT DESCRIPTIONS
Product descriptions provide customers with information about your products or services (logic), but they also work on your SEO and improve your ranking in search results. Therefore, your descriptions should be well-optimized as they can drive organic traffic to your website. In addition, well-presented product information has the power to persuade potential customers to buy. Finally, while you can do it manually, artificial intelligence allows you to automate the entire process and save yourself a lot of time.
#4 CHATBOTS AND VIRTUAL ASSISTANTS
A chatbot is a great way to start a conversation with a user. About 64% of users expect real-time interactions with online businesses. Thanks to the chatbot, you can offer immediate response and no longer ask users to fill out contact forms or send emails. A virtual or digital assistant is an application that understands natural language and can help users with their questions. With an efficient and automated chatbot, e-commerce websites and retailers can increase conversion rates by personalizing the online experience for the consumer without the need for additional work.
#5 SECURITY AND FRAUD DETECTION
The more significant the amount of data, the more difficult it is to spot anomalies. However, machine learning algorithms can spot changes in patterns and determine what “normal” behavior is, and alert administrators when suspicions arise. For example, customers who make purchases with stolen credit cards or withdraw payments shortly after receiving the goods are problematic. Detecting and preventing such fraud is almost impossible without machine learning, which quickly processes repetitive data patterns to detect fakes before they happen.
For example, PayPal uses tools to find fraudulent transactions and separate them from legitimate transactions. Machine learning explores the specific characteristics of a data set. It then builds models that analyze each transaction for signs of fraud. That makes it possible to stop an ongoing fraud before the transaction is completed.
#6 AUTOMATION OF MANUAL PROCESSES
Technology helps automate diverse processes from emailing to booking airline tickets. AI algorithms not only perform complex tasks faster than humans but are also able to operate 24/7. In the e-commerce world, we talk primarily about automating customer service and answering frequently asked questions, but also about automating online marketing campaigns and tracking their results.
Summarizing: Machine learning in e-commerce
Many people are concerned that the use of AI across industries will leave people unemployed. Fears aside! The goal of brilliant solutions is not to replace humans but to improve human work. Artificial intelligence and machine learning is an opportunity to delegate tedious and time-consuming tasks to robots and algorithms.
The points mentioned above prove that machine learning is a high-value investment in digital innovation and the latest technology trend. E-commerce is an industry in which machine learning solutions directly affect customer service and business development. There is no doubt that ML will continue to grow in the coming years, changing the face of sales.