Machine Learning In E-Commerce

Introduction : E-commerce is one of the first industries that started using all the benefits of machine learning. Nowadays, there are machine learning applications for almost every area of e-commerce. It feels like ecommerce is in a constant state of reinvention. Machine learning is helping ecommerce development companies take the customer experience to a whole new level. It is also making them more agile. Machine learning is helping ecommerce businesses generate revenue in ways that they never could previously. There are a number of ways in which the power of machine learning can unleash the full potential of an ecommerce business. So, lets talk about the use cases of ML in e-commerce.

1. Personalization Of Services : When a customer walks into a brick-and-mortar store, a salesperson usually approaches the customer and asks them what they are looking for. Ecommerce websites do not have this luxury. Customers usually shop online for convenience rather than an experience. They usually have a specific product in mind. If they find it easily, they may purchase it. In order to provide an experience similar to that a customer would have in-store, ecommerce retailers need to collect huge amounts of data and make sense of it. This is where machine learning can help. It can help ecommerce retailers run targeted campaigns that can convert prospective buyers into actual ones.
2. Optimized Pricing : Online shoppers are usually very price-sensitive. If a product costs as much as it does in-store, customers may feel more comfortable going to the store and assessing it first-hand before purchasing it. It is also not uncommon for shoppers to compare the prices of products across various ecommerce platforms to find the best deal. Ecommerce businesses have found much success by implementing dynamic pricing. Machine learning can change and readjust prices by taking into account various factors all at once. These factors include competitor pricing, product demand, day of the week, time of the day, customer type, etc.
3. Fraud Protection : Chargebacks are every ecommerce retailer’s nightmare. Most buyers, especially first-time ones, have the impression that ecommerce companies are not secure enough. Ecommerce companies are vulnerable to fraudulent activities. ecommerce retailers must be very careful. It is not uncommon for businesses, especially online ones, to shut shop owing to a bad reputation. Businesses must therefore not cut corners when it comes to detecting and preventing any kind of fraud. Machine learning can eliminate the scope of fraudulent activities significantly. It can process reams of exhaustive, repetitive data speedily and can nip fraudulent activities in the bud, by proactively detecting any anomalies.
4. Product Recommendations : Shoppers may walk into a store knowing what they want. However, an excellent salesperson can anticipate customer needs and recommend products even before customers realize that they need them. Product recommendations can increase revenue substantially. This becomes tricky to achieve on an online platform as it requires identifying patterns in sales and shopping behavior. Many ecommerce retailers have leveraged machine learning to successfully create a product recommendation engine. They are able to identify trends in buying behavior to suggest suitable products to a shopper. McKinsey and Company found that 75% of what customers watched on Netflix were based on product recommendations. 35% of purchases made on Amazon were owing to product recommendations.
5. Customer Support : In this competitive business environment, customers do not just expect a good product. They also assess the quality of customer support. Most customers dread calling those toll-free helpline numbers, listening to endless menu options and struggling to connect to an actual person who can help them. Nobody looks forward to delayed and impersonal email responses received from customer support IDs. For most organizations, staying on top of customer service requests can be very challenging. Automating customer support and enabling self-service can help the retailer as well as the customer. Machine learning can be used in many ways to help customers and enhance customer satisfaction. A great example is the use of chatbots. Chatbots can identify and resolve issues by conversing with the customer in a natural manner. Machine learning can help businesses offer superior, personalized customer support on a large scale.
6. Managing Supply And Demand : All businesses resort to forecasting in order to match demand with supply. To forecast well, ecommerce retailers must base their decisions primarily on data, among other things. To make sound data-backed decisions, businesses must process as much data as possible. It is also important to ensure that the data is accurate and that it is being processed correctly. Machine learning can process exhaustive amounts of data accurately and much faster. Machine learning can also study data to provide as many insights as possible. This enables not just forecasting but also helps online businesses improve their products and services.
7. Predictions about customers : You can use machine learning to learn various things about viewers visiting your website and making purchases. In fact, artificial intelligence can help business owners find out how likely viewers are to purchase from you again or what catches their interest.

• Predicting whether a given user will make a purchase in a specific product category in real time – so that the seller can react accordingly (eg, call that person or send email with engaging content). It gives you the opportunity to increase conversions while the customer, for example, is considering buying.
• Predicting whether the user will be returning and what purchases he will make at certain times. This will help in matching the right marketing message to that person to increase the conversion of the future purchase and to encourage the person to return.
• Customer lifetime value prediction (CLTV or LTV) – to predict how much money a particular user will spend in your shop. Accurate estimation of the future customer value allows effectively allocate marketing expenses, identificate and care for high-value customers and reduce exposure to losses.
• Customer churn prediction will discover customers who are risky to leave. The implemented machine learning solution in e-commerce will allow you to react quickly to the customers who will probably stop buying from you. Such a system will increase retention rate and will bring you a stable stream of revenue.
• Prediction of client’s size – personalized size recommendations reduce the chargebacks for both the company and customers. Predictions using machine learning in e-commerce reduces company’s or customers’ costs and definitely increases customer satisfaction.
• Prediction of demand for specific product categories – this will help to meet all customer needs and trends in the future. This will cause customers to be happy to return to your online store where most of the goods are available and can be bought immediately.

8. A/B testing : A/B tests enable the product (eg website) to be adapted to consumers. Almost 80% of the A / B tests variants do not yield positive results. Conducting this process is very hard and laborious, which is why the algorithms of machine learning for e-commerce will definitely help you with:

• Process automation of selecting platform`s (product`s) features that should be changed through the use of a genetic algorithm. That is based on the best suggested changes to the product that an algorithm may offer. For example, noticing that the bigger “BUY” button on the page increased sales by 1% so we can check whether its further enlargement may improve the results.
• Automatic customer segmentation into groups using unsupervised Machine learning models for e-commerce depending on their characteristics (age, gender, expenses, preferences, etc.) and personalization of the content (product for their needs). For example for women over 40, the main color of the page will be burgundy while for men under 20 years old it will be blue.
• Faster finding optimal options of pages / products through the use of self-learning AI algorithms instead of repetitive and tedious work. Machine learning in e-commerce allows online retailers to shorten orders of magnitude from months to days.

9. Image Processing : Retailers invest in AI and image recognition systems to influence customers (buyers) behavior and also for process automatization. Investment into computer vision technology with visual search possibilities could help you to match customers photos e.g. with similar clothes sold online. This could be defined by user’s preferences based on the category of products the person usually buys (what color, what brand) and based on the data from social media (eg Instagram, twitter, facebook, vkontakte). Another machine learning application in e-commerce could be automatic completion of information about the subject on the basis of the photo (what is the article, what category to add it, what color it has). 

 10. Improving The search engine : Users use search engines to quickly find what they need. They have less and less time and patience to formulate queries, wait for results and analyze them. That is why there is a need for personalized results of search queries. A personalized search engine could play an increasingly important role. It is based on machine learning models with short-term and long-term user preferences, history or previous queries. In addition, such search engines are able to increase the user’s conversion better than non-personalized search engines based on traditional information retrieval (IR) techniques. This is especially important for giants like eBay. With over 800 million items on its website, eBay uses artificial intelligence and data to predict and represent the most relevant search results.

Conclusion : E-commerce is an industry where machine learning applications directly affect customer service and business growth. With machine learning applications in e-commerce, you can create business benefits for each department of your e-commerce business. Moreover, improve customer service, increase efficiency and productivity, improve customer support, and make more informed HR decisions. As machine learning algorithms for e-commerce continue to develop, they will continue to be of great benefit to the e-commerce industry.

References :

• https://addepto.com/best-machine-learning-use-cases-ecommerce/
• https://www.omnisend.com/blog/machine-learning/