One of the first industries start utilising all the advantages of machine learning is e-commerce. Almost every part of e-commerce is now controlled by machine learning algorithms . Its application can be seen from inventory management to customer service. Most of the modern E-commerce companies has separate departments for deep learning and artificial intelligence(AI).These companies have invested a lot of money to understand better about their clients, so that they can provide personalize offers for a particular customer, improve customer experience and automate manual processes.
machine learning and Recommendation engine in the e-commerce industry directly convert into profits and increases the company’s market share with better customer acquisition.
1. Recommendation engine (recommender system)
Personalization and recommendation engine is the hottest trend in the global e-commerce space. Now by using machine learning algorithms we can thoroughly analyze the online activity of hundreds of millions of users. By using this data we are able to create product recommendations, tailored to a specific customer or group (auto-segmentation).
Let’s understand how the recommendation engine in e-commerce works. By analyzing collected big data from the website traffic, we can understand which sub-pages each customer has visited, what he is looking for, how much money he spend on each item etc. By using all these information we can easily built a recommendation system which will suggest the items to each customer they are interested in.
2. Machine Learning for dynamic pricing in e-commerce
Dynamic pricing is the practice of adjusting prices according to market and customer data. e-Commerce business leaders, such as eBay and Amazon, leverage different types of dynamic pricing to attract more customers and increase profitability.
Machine Learning in e-commerce can be very helpful in case of dynamic pricing and can improve your KPI’s. This helpfulness comes from ML algorithm ability of learning new patterns from data. As a result, those algorithms continuously learn from new information and detect new demands and trends. By identifying this varying demands and trends the algorithms can adjust the price accordingly. That’s why in most of the big e-commerce companies like amazon and ebay prices are varying with time.
3. Image processing
Companies invest in AI and image recognition systems to influence customers behavior and also for process automation. Computer vision technology can help us to find products by using photos of similar products .
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).
4. Improving the quality of the search engine using Machine Learning in E-commerce
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.
5. Fraud detection
Fraud is inseparable from commerce as a whole, and eCommerce is especially vulnerable.
Online shopping offers huge opportunities for those who want to take advantage of automated systems, which is why it is important to have algorithms in place that can detect fraudulent activity.
Integrating a CAPTCHA is not enough. We also have to monitor behavior and look at how certain people use our site.With machine learning, you can identify repetitive patterns that don’t match with human behavior.
6. Automated E-Commerce chatbots
Chat is one of the most popular ways to engage with eCommerce customers and this use is expected to grow by 30% over the next few years. Machine learning can be used in eCommerce chatbot systems for a number of purposes such as personalizing the shopping experience, answering customer queries, generating reports & alerts based on user behavior, etc. There are cloud tools such as Amazon Lex, Amazon Kendra, Google Dialogflow, Azure bot engine, etc., which can be used to build your custom chatbots.
There are lots of other applications of Machine learning in E-commerce are there, which we will cover in another article. Hope this worth reading.Happy learning.
