6 Factors about Machine Learning in E-Commerce
Machine learning is a branch of artificial intelligence that improves performance over a period of time through experience. In many ways that may not be apparent at first glance, machine learning has become integrated into our daily lives. For anyone that is involved in E-Commerce, it would be natural to ask how machine learning would affect them.
1. Product Recommendations
Probably the most obvious examples of machine learning for E-Commerce are product recommendations, where products are suggested to the customer based on what they have shown interest in. Traditionally, recommendations have been manually entered based on the product categories, but it is extremely time-consuming to do it in this way.
Modern methods use algorithms based on machine learning, separated into the categories of Collaborative filtering, where recommendations are made based on what similar customers have bought in the past and Content-based filtering, where recommendations are made to match the customer’s profile.
2. E-Commerce Targeting
In physical retail stores, we can talk to our customers face to face to find out what they want. In order to achieve the same, online stores need to make use of the vast amounts of customer data coming in from each visitor.
Therefore it is important to segment customer data as it allows companies to adapt their sales strategies for every customer. Machine learning helps us understand the customer’s needs and create a rich customer experience.
3. Machine Learning and Search
No matter how good or cheap a product is, it will not generate sales if customers are not able to find it.
For websites with large product catalogs such as eBay, this is even more important to provide efficient search algorithms.
Machine learning can help with search ranking feature, which sorts results based on their estimated relevance. This estimation is based on the frequency of the searches as well as each customer profile.
Another feature is query expansion, where potential options are shown while the customers are still typing.
4. Fraud Detection
As we gather more data, it becomes easier to spot anomalies. Machine learning algorithms can be used to identify patterns in data and learn what is normal and notify us when something seems wrong.
In E-Commerce, this is used for fraud detection, to find those people who use stolen credit cards or chargeback payments after an item is delivered.
Anomaly detection can also be used to maintain a high level of quality for product information in a database. Large databases in E-Commerce usually contain errors, such as misspelled product names, missing images or products sorted in the wrong categories.
5. Price Optimization
Machine learning algorithms can help you find information regarding price trends, including your competitor’s prices, as well as combine this information with customer behavior to determine the best prices for each product.
6. Enriching your Customer Experience
Machine learning allows us as service providers to create a more personalized experience for each and every customer. We can eliminate customer service issues before they even occur. As a result, the cart abandonment rate can be reduced and sales should be higher. Unlike humans, AI can provide customer service solutions around the clock.
The above applications are only a small peek into the advances that machine learning can drive in the E-Commerce field and E-Commerce development. There is a broad range of possible options which are currently being worked on and we can be excited about what is to come in the future.