Top 10 Machine Learning use cases in ecommerce

The role of machine learning (ML) in ecommerce is becoming increasingly important as the technology matures and more businesses gain advanced deep learning capabilities. Machine learning is transforming the way people shop online by providing retailers with the ability to offer more personalized shopping experiences, recommend products to customers, and automate tasks. Let’s take a look at some of the most powerful use cases of machine learning in ecommerce.

10 ways businesses are using ML to improve online customer experience 

Below are the 10 main ways of using machine learning to improve online customer experience.

1. Product recommendation engine.
2. Optimizing merchandizing.
3. Predictive search.
4. Price optimization with dynamic pricing. 
5. Improved ranking and search functionality. 
6. Better inventory management and sales forecasting.
7. Visual search.
8. Intelligent chat bots.
9. Enhanced fraud detection and threat management.
10. A/B testing using Artificial Intelligence.

Let's dive deep into these ways!

1. Product recommendation engine

An ML-powered product recommendation engine predicts what products a customer is likely to be interested in. This information is then used to recommend similar or complementary products to the customer or to provide personalized marketing messages. You can also cross-sell and upsell products using smart recommendations. 

Product recommendation engines are typically trained on data from past customer behavior, such as purchase history, browsing behavior, and search queries. This data is used to identify customer preferences and search behavior, and personalize recommendations accordingly.

The recommendations can be delivered through different channels – they can be displayed on a product detail page, or sent via email or push notification. There are many different types of machine learning algorithms that can be used for product recommendation, including collaborative filtering, content-based filtering, and matrix factorization. 

2. Optimizing merchandizing

Smart merchandising is one of the most promising use cases for machine learning in ecommerce. It refers to the practice of using data and machine learning algorithms to dynamically optimize the placement and presentation of products on an ecommerce website or app. For instance, an ML algorithm could be used to identify which products are most popular with certain customer groups. You can then ensure that those products are prominently displayed on the website for those customers. 

Further, machine learning in ecommerce can be helpful in optimizing stock levels for individual products. By analyzing customer data, businesses can predict demand for specific items and adjust inventory accordingly. This helps to ensure that customers can always find the products they need when they need them.

3. Predictive search

As ecommerce continues to grow and evolve, so does the need for more sophisticated search capabilities. This is where predictive search comes in – a type of search that uses machine learning algorithms to learn from past user behavior and make predictions about what a user is likely to search for next.

Machine learning in ecommerce is revolutionizing the predictive search experience in more ways than one - 

• Identifying trending products and topics: By analyzing past search data, predictive search can identify which products and topics are currently trending and display them prominently in the search results. This is especially useful for fashion and lifestyle retailers who want to make sure their customers always see the latest trends.

• Autocompleting search queries: Predictive search can autocomplete partial searches based on what it knows about the user’s preferences and past behavior. For example, if a customer starts typing “women’s shoes size 7” into the search bar, predictive search may autocomplete the rest of the query as “women’s shoes size 7 black” based on the user’s search or order history.

• Suggesting related searches: Related searches are another method to streamline site search performance with machine learning in ecommerce. Related searches can be a brilliant way to narrow down search possibilities and improve relevancy. For example, if a customer searches for “dresses”, predictive search may suggest related searches such as “cocktail dresses”, “maxi dresses” or “summer dresses”.

4. Price optimization with dynamic pricing 

By using machine learning, businesses can automatically and accurately adjust prices in real-time, based on a variety of factors such as stock levels, competitor prices, customer behavior, and more. This allows businesses to optimize their pricing to maximize profits, while still providing customers with the best possible price.

Dynamic pricing is becoming increasingly popular in the ecommerce world, as it provides a major advantage over traditional static pricing strategies. 

5. Improved ranking and search functionality 

Search is one of the most important functionalities on ecommerce websites. It allows customers to find the products they are looking for quickly and easily. However, search can be a big challenge for ecommerce businesses. There are millions of products on most ecommerce websites, so it can be difficult to rank all of them correctly. Additionally, customers’ search habits are constantly changing, so businesses need to be able to re-rank their products quickly and accurately.

By using techniques like auto-complete, autosuggest, typo tolerance and synonym detection, you can ensure that your site search is always providing the most relevant and accurate results for your customers. Autocomplete and auto suggest are methods that are used to suggest possible search terms to the user as they are typing in the search box. Typo tolerance is a feature that allows users to still find what they are looking for even if they make a typo in their search. This is important because sometimes users may not know how to spell a word correctly, but should still be able to find the product they are looking for.

6. Better inventory management and sales forecasting

Keeping track of inventory levels, anticipating customer demand, and managing stock to meet that demand can be a complex and time-consuming process.

Machine learning can be used to streamline and automate many aspects of inventory management, from demand forecasting to stock replenishment. By using historical data and trends, machine learning algorithms can predict future demand and help ecommerce businesses keep their shelves stocked with the right products at the right time. This not only saves businesses time and money, but can also improve customer satisfaction by ensuring that products are available when they're needed.

Forecasting isn't just about numbers, though. It's also about understanding customer behavior. Machine learning can help businesses better understand their customers' needs and wants, and can even predict when they're likely to make a purchase. This information can be used to tailor marketing campaigns and improve the overall customer experience.

7. Visual search

By understanding the content of an image, ecommerce companies can better target products and offers to customers. For example, if a customer uploads an image of a dress to an online retailer, an ML-powered search engine can analyze the image and suggest similar dresses that the customer might be interested in. This can be particularly useful when customers don't know the name of the product they are looking for, or when they are looking for a specific product but don't know how to describe it. Additionally, image recognition can be used to process customer photos for product reviews or for user-generated product recommendations.

8. Intelligent chat bots 

Chatbots can be used to answer questions, recommend products, provide customer support, or even upsell and cross-sell products. Machine learning enables chatbots to get smarter over time and provide more personalized and humanlike conversations.

In addition to providing a more personal touch, ML-powered chatbots can also help reduce customer service costs. By automating common tasks like answering product questions and providing shipping information, chat bots can free up customer service reps to handle more complex issues.

9. Enhanced fraud detection and threat management

The ecommerce world is no stranger to fraud. Machine learning can be used to detect and prevent fraud, by analyzing patterns in customer behavior and identifying unusual activity. This can include things like unusual purchase patterns, anomalous behavior, and unexpected changes in financial data. By identifying these patterns, businesses can take steps to prevent fraud before it happens.

10. A/B testing using Artificial Intelligence

A/B testing is a great way to test different changes on your website to see what has the biggest impact on your bottom line. But this testing can be time-consuming and expensive, especially if you're constantly changing multiple elements on your site. That's where machine learning comes in.

Machine learning can help automate the A/B testing process by constantly testing different variations of your site and determining which ones are most effective. This way, you can focus on other aspects of your business while still getting the insights you need to optimize your conversion rate.

Wrapping Up 

Thus, it should be clear that machine learning has revolutionized what technology can do and what people expect from online businesses. By offering online shoppers with the personalization and relevance they want, ML-based features ultimately help online businesses improve their revenues.

And this is why it’s important for ecommerce merchants to also explore ML- and AI-powered search engines like Zevi which offer contextual search powered by NLP and advanced ML algorithms. The result is an optimized search engine that understands user intent and delivers accurate, relevant and personalized search results. 

Interested in leveling up your site search? Reach out to us today to book a demo.