11 Real-World Applications of Machine Learning in E-Commerce

The storm has blown over the e-commerce market following the COVID-19 pandemic, which caused a 55% surge in online spending .

Brands are turning to advanced technologies to stand out from the competition, with the development of machine learning for e-commerce leading the way: deciphering customers and predicting their next moves is key.

In this blog, we look at 11 key use cases of machine learning in ecommerce that are trending right now . If you are familiar with the underlying technologies, you can skip the next two sections and jump straight into these hot topics.

How machine learning works — the bare essentials

Machine learning (ML) is a subfield of artificial intelligence that enables computers to learn from data and improve their learning over time without being explicitly programmed .

The essence of ML is designing algorithms (instructions for a computer to follow) that can make informed predictions and decisions.

Think of machine learning as teaching a computer how to fish. First, you give it a fishing rod (the algorithm) and teach it how to fish (train the model with data). Once it has learned, it can fish (make predictions or decisions) on its own in any part of the ocean (new data).

This vast ocean of data comes in many forms, from structured data such as transaction records and demographics, to unstructured data such as emails, customer reviews, social media posts, clickstream data, images, and videos.

ML can use both historical and real-time data to predict future outcomes, and the more diverse and high-quality data you provide, the better the computer's predictions and decisions will be.

ML is being deployed across industries: it's used to power personalized content recommendations at Netflix, precise arrival times at Google Maps, detect suspicious transactions at JPMorgan Chase, forecast demand at Walmart, help Siri understand language, and enhance the safety of Tesla's self-driving cars.

Types of Machine Learning in Ecommerce

There are five main types of machine learning in e-commerce and various industries.

• Supervised learning : This type uses labeled data (data and corresponding answers). For example, to predict customer churn, you may need to train a model on customer purchase history (features) and whether the customer stayed or churned (labels). Common algorithms include linear regression, decision trees, and support vector machines.
• Unsupervised Learning : Unlike supervised learning, this approach relies on the machine to discover hidden patterns in unlabeled data on its own. For example, unsupervised learning can help e-commerce businesses classify customers into groups based on their purchasing behavior without pre-defining the groups. K-means clustering and principal component analysis are commonly used algorithms in this category.
• Reinforcement learning : This type is based on trial and error. The machine learns to interact with the environment and make decisions based on rewards and punishments. This can be used to optimize warehouse layouts, for example by reducing product retrieval times through learned placements. A common algorithm here is Q-learning.
• Generative AI : Generative AI is a type of unsupervised learning that stands out for its ability to create new data points similar to a training set. E-commerce sites might leverage this technology to create new product designs or realistic virtual model images. GANs (generative adversarial networks) are a popular model.
• Deep Learning : This form of ML is inspired by the structure of the human brain and is particularly good at processing large amounts of data. Deep learning models use "neural networks" with several layers (hence the "deep") to progressively extract higher level features from raw input. In e-commerce machine learning, this technique is used for image recognition (to identify products in an image) and natural language processing (to understand and respond to customer queries in human language). It is the technology behind chatbots and product recommendation systems.

Read more: What is digital conversion? 4.0 trend: Digital transformation

Real-World Applications of Machine Learning in E-Commerce

Before we dive into our list of 11 key use cases for ML in ecommerce, let’s take a look at how some of the industry’s leading companies are effectively combining ML with their custom ecommerce solutions .

• Amazon is revolutionizing e-commerce with its ML-powered recommendation engine, driving 35% of sales . Amazon is using the power of big data to adjust prices every 10 minutes,boosting profits by 25% .
• Alibaba uses ML in e-commerce to detect and filter out counterfeit products, increasing trust and reducing disputes.
• Pinterest employs computer vision technology to vet the content of each pin, which helps filter abusive and deceptive content, optimize ad placements, and organize nearly 300 billion pins every day.
• JD.com , one of China's largest online retailers , has used machine learning to create a hyper-efficient supply chain. The technology has increased procurement automation rates to 85% and reduced inventory turns to about one month.
• Asos has tripled its revenue and halved its losses on returns.
• Uniqlo is using voice recognition and ML to guide customers to nearby stores so they can quickly find the products they searched for on their smartphones .
• Dollar Shave Club uses the power of data and ML to predict what DSC products customers are likely to purchase.

The challenges and goals of e-commerce are the same regardless of size. Experts predict that the e-commerce market will exceed $8.1 trillion in just three years, despite the economic slowdown caused by the pandemic. The space is filling up.

For ecommerce business owners, tracking trends isn't an option -- it's a requirement.

So, here’s the ultimate guide to implementing machine learning in ecommerce today.

1. Intelligent search solutions - we give you what you want

When customers fire up the search bar, they're likely ready to buy - a detailed query like "limited edition rose gold iPhone 13" indicates clear purchase intent - but imagine their frustration when unrelated rose gold watches and earrings clutter up the results.

Or consider a scenario where a customer sees a unique lamp in a friend's home and wants a similar lamp, but how will they search for "Industrial Loft Style Iron Cage Desk Lamp" without even knowing its exact name?

Smart search, powered by ecommerce machine learning, is a game changer: it returns relevant results, intuitively corrects typos, and translates "Nkie" as "Nike" so customers don't miss out on the perfect running shoes.

ML Supercharge searches in a variety of ways.

• Uses product details and image recognition to automatically suggest product categories and descriptions
• Facilitate autocomplete when users start typing in the search bar
• Fix spelling mistakes on the fly
• It enhances visual search, allowing customers to upload a photo and the system will find the closest available item.
• Detecting individual elements in an image and using them as independent search terms
• Facilitating voice-activated search

2. Personalized product recommendations - tailor-made shopping

For example, think back to your last purchase on eBay. Before your finger even touched the search bar, you were greeted with customized suggestions. How did eBay know what you were thinking? The secret is smart data interpretation.

Using various ML algorithms, e-commerce platforms can analyze customers' browsing history, past purchases, shopping cart contents, and even the behavior of similar users. This analysis leads to predictive product suggestions. So, a search for vintage records is more likely to surface related items like record players and record cleaning kits than random kitchen appliances.

The mechanics behind such a recommendation engine are as follows:

• Learning from the Crowd – Collaborative Filtering : This technique looks at a user’s past shopping habits and the choices made by other shoppers with similar tastes. For example, if shopper A buys books by Hemingway, Fitzgerald, and Salinger, and shopper B chooses Hemingway and Fitzgerald, it stands to reason that B might also enjoy a bit of Salinger.
• Content Knows Best – Content-based filtering : This method suggests items similar to those that the user has previously shown interest in, based on an analysis of the product's features. If a customer is considering a high-megapixel camera, the system can suggest other high-resolution cameras.
• Best of both worlds – hybrid systems : By combining content and collaborative filtering, hybrid systems can generate even more accurate suggestions. For example, Netflix employs a hybrid approach that takes into account both user behavior and movie characteristics.
• Deep Dive – Deep Learning Techniques : More complex techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) dig deeper into the data and find patterns that traditional techniques might miss. These are the “hunches” that suggest a customer looking for camping gear might also need hiking shoes.

SalesForce highlights that when shoppers click on a recommended product, their time on site jumps from 2.9 minutes to an average of 12.9 minutes , and offering product suggestions also increases a site's repeat customer rate by 56%.

McKinsey highlighted this, revealing that algorithm-driven recommendations influence 75% of viewing choices on streaming platforms and drive 35% of purchases on Amazon.

3. Smart Pricing - Setting the price to maximize profits

Setting prices is no easy task. You have to keep an eye on your competitors, the seasons, market changes, local demand, and even the weather.

When shipping internationally, the task becomes a puzzle that involves local regulations, shipping costs, local market rates, and other factors.

After all, price is crucial: even a slight increase over your competitors can cause customers to abandon their cart.

Rather than sticking to fixed prices or rushing to reduce them when sales slow, the solution is machine learning-driven pricing adjustments that can help predict prime prices, identify when discounts are needed, and encourage upsells when the opportunity is ripe.

Machine learning for ecommerce allows you to instantly evaluate all influencing factors, allowing for dynamic pricing on your site.

4. Customer segmentation - creating unique experiences for unique customers

Take a step back and imagine a store full of customers with different shopping habits, preferences, and budgets. Dealing with this diversity may seem daunting. But machine learning in e-commerce makes it simpler by segmenting customers and grouping them by common characteristics for personalized marketing.

Take the example of Emily, a loyal customer who loves books. Machine learning, powered by predictive analytics and other techniques, calculates her Customer Lifetime Value (CLV), which predicts that Emily would respond positively to a custom-made loyalty program. This prediction proves correct, doubling Emily's purchases and making marketing efforts more cost-effective.

Next, you meet John, a sporadic buyer who was identified by your ML churn prediction algorithm as on the verge of churn. By offering him a timely discount on his favorite outdoor gear, you can rekindle his interest and prevent a potential customer loss.

Machine learning in ecommerce adds a personalized touch to stores by creating a clearer picture of the customer, moving from a one-size-fits-all model to a “made-for-you” destination, ensuring everyone, from loyal Emily to upset John, can find what they need.

Read more: Artificial Intelligence Is The Future Of E-commerce

5. Chatbots - seamless, out-of-the-box customer service

Managing customer support is not a clear cut job. Relying too much on human staff can require large, costly teams to handle inquiries that could be addressed through an FAQ page. But fully automated systems lack the human touch and can frustrate customers.

ML-powered chatbots emerge as the ideal solution. They are cost-effective and provide round-the-clock support without the need for round-the-clock payroll. And they outperform the average respondent: by learning from the user's profile and past behavior, they tailor their answers and increase the chances of conversion.

Smart chatbots, powered by deep learning and natural language processing, act as customer service soldiers: they answer questions, address complaints, suggest products, process payments, track deliveries, and more. And they're good at what they do.

Plus, chatbots are getting better too. They're learning to understand not just what customers say, but how they say it. Sentiment analysis and emotion AI make a chatbot more than just a tool. It becomes a listener, an empathizer. It makes customer service even more amazing.

6. Sentiment Analysis – Understand emotions to improve customer engagement

Customers speak. In reviews and on social media, they spill their thoughts, often laced with emotion. "Page turner," they say, or "lifesaver in the winter." They express more than just words, they express their satisfaction, or lack thereof. Now imagine a business listening and responding to this.

So what about that lone complaint buried in the mountains of data? A product malfunction aired in frustration. How can companies catch this signal amid the noise?

This is where ecommerce machine learning-powered sentiment analysis comes in handy.

Sentiment analysis identifies the underlying emotional tone of words, interpreting "not bad" as a thumbs up, ensuring businesses understand customer sentiment.

Using NLP, deep learning, and some ML algorithms, sentiment analysis can help ecommerce businesses in a variety of ways: deciphering product reviews and comments to gain insights to improve your offerings, monitoring social media buzz to gauge public reaction to marketing campaigns, and uncovering customer service bottlenecks to improve satisfaction.

But that's not all. Sentiment analysis works even better when you incorporate it into your chatbot, which gives your bot the ability to feel. Here's what you get from an emotionally intelligent chatbot:

• Customized customer experiences : These bots read the tone, sentiment, and emotion of customer chats and tailor their responses accordingly, resulting in a more empathetic, personalized customer experience that increases loyalty and satisfaction.
• Proactive Conversation : They're not one to wait and see. These bots offer a smart way to engage, upsell, and cross-sell customers based on their browsing behavior and past interactions.
• Engaging feedback : They are good listeners and collect customer opinions in an engaging way, clearly indicating what customers like and dislike.
• Cart recovery : An emotionally intelligent bot notifies customers of abandoned carts and offers a hand or reason to complete the purchase.
• Trend Spotting : These bots are great trend spotters, finding patterns in customer interactions and providing useful inputs to improve your products, services, or customer support.
• Customer relationship managers : They also keep an eye on dissatisfaction and use sentiment analysis to catch unhappy customers and send them offers and messages at the right time to prevent them from churn.

Read more: Leveraging AI to “listen” to consumers in e-commerce

7. Omnichannel strategy: reach your customers where they are

Omnichannel is taking the reins in the marketing space. Done right, it can skyrocket retention, conversions, and revenue. But the secret isn't more headcount, it's machine learning.

For example, consider a customer who switches devices to browse shirts online and then finally buys one in-store. ML tracks this journey like a shadow and gets a holistic view across platforms. It creates a single unified customer profile and breaks down device silos.

Imagine another person abandoning a cart full of dresses - ML doesn't miss this opportunity, triggering personalized email reminders and custom offers to nudge the buyer towards completion.

Machine learning for ecommerce keeps track of customer behavior. It records which ads were clicked, which content was engaged, which emails were opened, and it all factors into the equation. And it doesn't just analyze: it learns, predicts, and personalizes.

8. Social Commerce - Harness the power of social media to capture sales opportunities

Social commerce is the new big thing. It combines online shopping with the social chatter we all love. Statista predicts that social commerce sales could reach a staggering US$2.9 trillion by 2026.

People on social media aren't fans of traditional ads; many find them intrusive. Influencer Marketing Hub says the key is to incorporate ads into your social media posts. Make them useful and interesting, not just salesy.

How? Machine learning for ecommerce has the answer.

ML quietly processes reams of data (likes, shares, pins, retweets, comments) to derive meaningful insights. What artisanal coffee did you not know your customers wanted? ML will surface it in your feed. No guesswork required.

Draw links between user preferences: if you like handmade soaps, you might also like organic face oils; if you're into rustic decor, how about a hand-carved wooden clock?

With social media, ML can guide customers to their perfect fit. Isn't that impressive?

9. Just the right stock - intelligent inventory to achieve the ideal product mix

Inventory management is a chess game where foresight is key – it requires a strategic understanding of data and market conditions.

Excessive inventory in your warehouse ties up funds that could drive your business forward. Perishable or quickly depreciating items lose value as they fluctuate daily. The ultimate downfall? Dry cash flow from empty shelves.

To run a successful online store, you need to manage your products intelligently: monitor your inventory, reorder products, forecast demand trends, coordinate contractors, work with manufacturers, suppliers, and mail services, and manage your revenue.

This is where machine learning in e-commerce comes into play.

It monitors every item in your inventory and forecasts supply, demand, and cash flow dynamics based on a vast database of historical data.

Support multiple aspects of inventory management decision making.

• Offer upsells if certain products go out of stock
• Read the lunar patterns of seasonal and trend-driven product demand to suggest larger orders
• From streamlining delivery routes to scheduling, optimize your supply chain.
• Implement dynamic pricing to adjust prices based on supply, demand, and market conditions
• Automate replenishment to maintain ideal stock levels
• Identify slow movers to prevent excess inventory and free up storage space

Plus, as we mentioned earlier, advanced ML platforms can analyze data from social media; sifting through trends, viral moments, and celebrity influence to alert companies to the next "it" product. A popular fashion item ignites the scene? Machine learning can spot it, predict spikes in demand, and advise inventory adjustments.

No more out of stock, no more missed opportunities, businesses capitalize on trending items to seize the moment.

10. Fraud Prevention – Protecting Business Transactions

Fraud is hugely damaging to ecommerce. From stolen credit cards to compromised customer databases to manipulated returns, ecommerce fraud drains funds, erodes trust, and alienates customers.

Machine learning doesn't just solve fraud detection, it reinvents it.

We use "anomaly detection" where algorithms analyze millions of transactions to spot unusual transactions -- a feat beyond human capabilities in terms of speed and scale, but routine for ML. ML flags inconsistencies in everything from device type and location to time zones, such as overspending, address mismatches, repeat orders on different cards, unexpected international orders, and suspicious returns or reviews.

Through cluster analysis, ML identifies at-risk customer segments, products, and time periods, allowing businesses to proactively combat fraud, while social network analysis uncovers organized fraud rings by mapping and examining links between accounts, devices, and emails.

Moreover, ecommerce ML algorithms root out fake reviews: language, IP address, frequency of reviews, and even time since purchase are all factors that keep them from escaping their scrutiny.

11. Smart returns strategy - Making returns count

A quarter of customers purposefully pack their carts to the edge, knowing that some will return to the shelves. This indecision, the fear of clothes that don't fit, and poor quality all cost merchants dearly. Unbeknownst to the consumer, each return starts a chain of tasks: cleaning, repackaging and preparing for resale. What to do if the product comes back damaged? That's a terrible loss.

Machine learning algorithms for e-commerce can combat excess profits through accurate product suggestions. Quality control becomes stricter, predicting and blocking potential failures from past data and feedback. Product portrayals are closer to the truth, curbing dissatisfaction arising from misleading descriptions.

Moreover, ML predictions return probabilities from various factors such as customer history, product type, price, etc. In the fashion domain, ML can become a virtual tailor, providing custom-fitted size recommendations based on individual measurements.

ML reduces returns, protects merchant revenue, and increases customer satisfaction.

Read more: EDUTO AI’s Library

Summary

So there you have it. These are the 11 ways machine learning is making waves right now. Machine learning adoption in ecommerce:

• Gain a better understanding of customer preferences
• Increase sales and average order value
• Remove unnecessary processes
• Providing insights beyond human capabilities

Accumulating customer data without analyzing it? It's like having the key but never unlocking the door. Integrating machine learning in e-commerce is not about keeping up with the times, it's about setting the pace and leading the competition.