E-commerce – is one of the first industry that started using all the benefits of Machine Learning. Zalando, Asos – companies that have entire artificial intelligence (AI), deep learning departments. They invest a lot of money to have better knowledge on their clients, personalize offers for a particular customer, improve customer experience and automate manual processes. Recommendation engine and machine learning in ecommerce industry directly converts into profits and increase companies market share with better customer acquisition.
Addepto machine learning consultants has analyzed which solutions have the biggest potential today. You could find few machine learning use cases below. They can help monetize your data and improve customer experience like Asos and Zalando:
1. Recommendation engine (recommender system)
Machine Learning in ecommerce have few key use cases. Personalization and recommendation engine is the hottest trend in global ecommerce space. With the use of artificial intelligence and the processing of huge amounts of data, you can thoroughly analyze the online activity of hundreds of millions of users. On its basis you are able to create product recommendations, tailored to a specific customer or group (auto-segmentation).
Let’s see how recommendation engine in ecommerce works. By analyzing collected big data on the current traffic on websites, you can determine, which sub-pages the client used. You could identify what he was looking for and where he spent most of the time. Based on various information: profile of previous customer activity, its preferences (e.g. favorite color), social media data, location and weather – results will be displayed on a personalized page with suggested products that will most likely interest them.
2. Personalization of the content on the website
Properly personalized content on the website or mobile application increases conversion and customer engagement. The selection of the best content is possible thanks to machine learning algorithms. Thos algorithsm could find patterns in the data based on the processing of a large amount of structured and unstructured data (including images and text).
AI algorithms take into account various factors such as: favorite style and color, image intensity, activity history, preferences, etc.. The results on the website are adapted to the personal preferences of each individual person. In this way recommendation in ecommerce could help you to increase your revenues.
3. Machine Learning for dynamic pricing in ecommerce
Machine Learning 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. Those algorithms continuously learn from new information and detect new demands and trends.
This is why online retailers could use ML models for dynamic pricing. Instead of simple price markdown. E-commerce companies could benefit from predictive models that can allow them to determine the best price for each particular product.
You can choose the offer, the optimal price and display real-time discounts, which will also take into account the state of the warehouse.This is done to maximize sales and to optimize inventory.
4. A/B tests using AI
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 methods of artificial intelligence 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 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 ML models 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. It allows online retailers to shorten orders of magnitude from months to days.
5. Predictions using machine learning in ecommerce
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 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 solution will allow you to react quickly on the customers who are probably will stop buying from you. Such 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. It 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 that customers will be happy to return to the your online store where most of the goods are available and can be bought immediately.
6. 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 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 use cases 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).
7. Improving the quality of the search engine using machine learning in ecommerce
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 increasingly important role. It is based on machine learning models with short-term and long-term user preferences, history or previous queries. Such search engines are able to increase the user’s conversion better than non-personalized search engines based on traditional information retrieval (IR) techniques.
8. Smart chat-bots to improve customer service
An intelligent chat bot based on NLP and AI can interpret individual users’ questions and respond to them individually. The role of virtual assistants is to imitate the best consultants to be able to help the users of e-stores in the purchase process in the most effective way. For example help in getting to the products, suggest the best pricing solutions, carry out through the transaction process.
Check our case studies to see how ecommerce company use machine learning and deep learning. Ping us a message and we will explain to you how recommendation engine is used in ecommerce and for personalization.