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6 Ways to Improve eCommerce Services with Machine Learning

eCommerce is a highly competitive area and to win customer loyalty, platforms should constantly enhance the user experience and increase customers' satisfaction.

Contemporary buyers value speed of the service, personalized recommendations, comfortable and effective search, quick and safe payments, excellent customer support, among others.

However, it becomes more and more difficult to satisfy all these needs as the volumes of data are growing.

Here are the areas where using Machine Learning for eCommerce websites can improve the customer experience and boost sales.

1. Better recommendations. Now is the era of personalization 2.0 or hyper-personalization. Unlike simple personalization based on preferences in the customer account, hyper-personalization means more advanced recommendations that take into account location, reviews, seasoning, weather, purchase history, and many other factors related to the product. ML allows analyzing and segmenting the data by each customer group and showing them hyper-personalized messages and promotions to hit precisely their interests.

2. Smart search. Large marketplaces experience difficulties with inaccurate goods descriptions and large amounts of similar goods. So customers simply cannot find exactly what they need.

To improve the situation, ML can help sellers of marketplaces by suggesting the descriptions and categories for their products based on historical data.

Another good application of artificial intelligence visual search. Users can upload a picture or photo of the product they want, and the system searches for the similarities in the database.

3. Real-time price optimization. AI algorithms are able to predict the time of peaks or drops of purchase activity and slightly change the prices to spur up the buyers’ interest with discounts.

4. Optimized inventory. ML allows you to keep under control all the products in stock. For example, the system is able to forecast the periods of increased purchase activity (like winter holidays) to calculate the number of goods necessary to have in stock in advance to satisfy the demand when the time comes.

5. Fraud prevention. ML can effectively identify suspicious user behavior and prevent malicious actions like listing counterfeit goods, posting fake reviews, using stolen credit cards, copy-pasting listings, and many others. Ordinary rule-based programming algorithms are not able to discover complex and non-trivial scenarios, as well as ML, can.

6. Automating sales routine. Developing eCommerce website, create a web portal for sales managers. AI-driven CRM can automatically collect and categorize valuable information about buyers from emails, messages, and calls and add it to their profiles. Then, the system will be able to suggest to sales managers the further steps for interaction with customers.

All these and many other improvements to your eCommerce website or marketplace will lead to a visible increase in customer satisfaction and sales growth.

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