Machine Learning in eCommerce: 12 Ways to Boost the Online Shopping Experience

eCommerce is one of the most rapidly growing industries. The market generated 5.2 trillion U.S. dollars in revenue in 2021.

Along with growth come a lot of challenges. You must either innovate or risk losing your footing.

eCommerce machine learning can turn your business upside down (in a good way, don’t worry). Higher revenues, reduced losses, improved customer satisfaction, and lower returns are just a few benefits that AI and ML can bring.

According to Servion, AI will power 95% of customer interactions by 2025. Machine learning improves the performance of an eCommerce business through an impeccable customer experience.

For instance, implementing AI-powered sizing assistance helped Asos multiply revenue by 300% and cut losses in half caused by product returns.

In this article, we talk about how to build a successful eCommerce business by implementing machine learning.

You’ll discover:

• Future of eCommerce and machine learning
• Benefits of machine learning in eCommerce
• Use cases of machine learning for eCommerce
• Useful eCommerce platforms and tools
• 6-step process for incorporating machine learning algorithm

All in all, it’s time to revamp your eCommerce business with machine learning and artificial intelligence.

Future of eCommerce and Machine Learning

It is no surprise that the latest technologies are always finding ways into eCommerce and online stores. Machine learning is no exception.

So what AI/ML can bring to the retail industry?

Machine learning in eCommerce helps businesses become more agile while taking the customer experience to a new level.

In 57% of cases, companies use machine learning and artificial intelligence to improve their customer experience. Personalized design and product recommendations, customer demand prediction, and inventory and delivery management are three areas with the biggest potential for machine learning in the retail industry.

Why Companies Use Machine Learning for eCommerce

Thanks to machine learning implementation, customers can get timely predictions regarding their businesses, insight into customer behavior, and the ability to identify what influences their buying decisions, as well as how much inventory they need for the upcoming holiday season.

Machine learning in eCommerce brings many benefits, which is why companies are searching for the help of a software development company like MLSDev to implement the technology.

Business Benefits of eCommerce Machine Learning

eCommerce businesses accumulate a lot of data. Machine learning uses data to make business, demand, and growth predictions. The possible applications of the technology are truly limitless.

Personalization

Machine learning for eCommerce allows companies to tailor the customer experience to their preferences and expectations. As a result, customers see more relevant recommended products, targeted ads, and even search results when they type a query in the search bar on your website.

Interactivity

Deep learning can also be used to create chatbots. These chatbots provide 24/7 customer support, improve the recommendation engine, boost eCommerce sales, and save money for a business. Another possible implementation is contextual shipping features to let customers ship whatever they want anytime.

Logistics

Logistics and inventory management is another sphere where machine learning can make a huge difference. Implementation of the technology allows the data to be used to forecast demand. Real-time analytics allow for streamlined product delivery.

Another area is warehouse technology, where self-driving vehicles can speed up the picking process, reduce the strain on human workers, and automate error-prone operations.

Security

Machine learning in eCommerce can also strengthen the security of operations. Fraud and anomaly detection tools powered by machine learning can ensure the safety of transactions, the protection of user data, and compliance with regulations.

Increased conversions

Higher conversions are what all merchants want, and implementing machine learning in eCommerce can help you achieve this goal. There are a few ways the technology can do this, and we will review them in more detail further in the article. Now, let’s just say that more relevant search results, personalized product recommendations, and highly targeted ads all contribute to higher conversion rates. In the end, you get better revenue.

Better marketing campaigns

eCommerce stores have access to a lot of information. With the help of machine learning, businesses can make sense of customer data in order to create tailored marketing campaigns.

For example, machine learning knows that when a potential customer visits an online store, searches for certain products, and adds them to the shopping cart but doesn’t ultimately proceed with the purchase. The next time customers visit your store, they will be recommended products from the category they’ve browsed before.

You can also easily segment your customers and target them with relevant special offers, discounts, and promotions via email or other marketing channels.

Improved in-house operational efficiencies

The importance of machine learning for eCommerce doesn’t only lie in boosting sales; the technology is also effective at reducing expenses.

Real-time insight into a business allows employees to operate better while spending less time on tasks. For example, deep learning can be used to forecast demand. Thus, you’ll be able to replenish your inventory so that you won’t run out of products but, at the same time, won’t have too many products in stock either, especially if we’re talking about products with a limited shelf life.

More informed decisions

• Will customers be interested in a new product?
• Should you increase prices for some products?
• Is there something wrong with your checkout process?

Whatever questions you have and decisions you need to make, eCommerce machine learning will provide you with the necessary data to make data-driven decisions.

As you can see, the benefits of machine learning for eCommerce are numerous, and there are quite a few areas where it can be used.

12 Use Cases of Machine Learning in eCommerce

Now that you know about the benefits of deep learning, let’s talk about some areas where it can be implemented.

Usage of AI/ML in eCommerce

Today, you can use machine learning to provide customers with a personalized shopping experience, provide more relevant site search results, detect fraud, predict customer churn, and improve customer service. However, possible uses of machine learning for eCommerce don’t end there.

We’ve collected twelve use cases to illustrate the capabilities of this technology. Let’s take a close look at them.

1. Personalized shopping

There is no secret that a personalized customer experience boosts eCommerce sales.

According to research by Google, existing customers are 40% more likely to spend more than planned when merchants provide them with a highly personalized shopping experience.

What does it take to create a personalized shopping experience?

Personalized Product Matching in eCommerce

We usually mean personalized product recommendations when we talk about machine learning in eCommerce. eCommerce stores make recommendations based on historical data, preferences, interests, purchase history, and behavior.

However, you can take personalization even further, for example, by using customers’ location data and real-time weather forecasts, you can show products and banners that suit their current weather conditions.

You can also show a different message for different segments to target new customers. For example, for first-time visitors, you can offer 10% off of their first purchase. For return customers, you can offer free shipping or use the personal information you gathered during the first purchase to send them an exclusive discount for their birthday.

As you can see, there is no limit to what machine learning eCommerce personalization can help you do and achieve.

2. Omnichannel marketing & ML

Omnichannel commerce is focused on providing a seamless experience across all sales channels, such as a website, social media, and brick-and-mortar stores. Through omnichannel marketing, online businesses increase customer retention, purchase rate, and engagement.

Omnichannel vs. Multichannel eCommerce

eCommerce machine learning can help you gather information across all sales channels and analyze how your customers behave over time. This will help you form a more unified image of your existing customers and choose a better communication strategy.

The data can be used to provide customers with a personalized customer experience at every channel and touchpoint.

3. Wise recommendations

As mentioned, product recommendations are integral to creating a personalized shopping experience. eCommerce recommendation algorithms can process past sales data, recognize recurring purchase patterns, and predict what products might grab customers’ attention.

Machine Learning eCommerce Personalization

Recommendation engines usually use content-based and collaborative filtering approaches:

• Content-based recommendations analyze a customer’s purchase history and recommend other products with similar characteristics to those purchased before such as price, product type, etc.
• Collaborative filtering recommendations consider a customer’s behavior and compare it to the behavior of similar customers. Recommendations are then provided based on the products that have already been ordered and positively rated by other customers with similar buying patterns

4. AI-powered assistants & сhatbots

The benefits of machine learning for eCommerce don’t only hide in a streamlined shopping experience. The technology can also help businesses with customer support services.

AI-powered assistants and chatbots are software development solutions that can help you build great relationships with customers. In fact, 68% of customers like chatbots because they provide quick answers. In addition, 41.3% of consumers use conversational marketing tools, such as chatbots, for purchases.

Thanks to natural language processing, customers won’t be able to tell human and chatbot conversations apart. These virtual assistants are available 24/7, know answers to all the questions, and never get tired of talking with customers.

Aside from providing answers to basic questions, they can also recommend the available shipping options, offer special coupons based on the course of conversation to increase sales, and learn from conversations in order to become even better.

5. Optimized pricing

Pricing is one of the key factors that influences the buying decisions of 47% of customers. This is the main reason eCommerce companies optimize product prices, taking into account internal trends such as demand, and external trends, such as competitors’ prices.

As a result, businesses can increase customer interest by offering more attractive prices for low-demand products or increase prices for high-demand products that competitors have run out of stock.

Dynamic pricing is an effective way to beat the competition and ensure customers will return to your online store for repeat purchases.

6. Optimized search results

Around 30% of eCommerce visitors use the internal site search to find the products they’re interested in. Those visitors who use site search are 2-3 times more likely to convert into customers.

Machine learning and artificial intelligence have made these searches even smarter. AI-powered search solutions like Algolia are typo-tolerant, have an autocomplete feature, and have a language-based option for tweaking the configuration of plural handling, stop words, optional words, etc. eCommerce search machine learning can also analyze customers’ behavior and tweak search results to include products that have the highest chance of grabbing their attention.

7. Visual search

Image recognition technology based on machine learning allows visitors to search for a product using an image. After the customer feeds in the photo, the image recognition engine matches the input information with the product database, then selects and displays the closest matches.

Visual Search

Visual search is a must-have for eCommerce businesses targeting Millennials and Gen Z. According to statistics, 62% of these generations use this feature over all other search methods. Visual search is also extremely effective for the clothing and make-up segments specifically.

8. Supply and demand management

Product inventory management is one of the most challenging tasks in eCommerce. Businesses need to understand how many products they need to meet demand. The task becomes even more challenging during the holiday season when sales can increase by up to 10%.

When you make the wrong predictions, you lose money.

Retailers can lose nearly half of intended purchases when customers encounter stock outages, which equals 4% of sales. Due to inventory shortages, a billion-dollar retailer could lose $40 million in sales during a single year.

Machine learning in eCommerce can help solve this challenge. Predictive machine learning algorithms analyze the data from past purchases and customer purchase patterns to predict future demand based on different factors.

As mentioned, there is seasonal demand and incidental demand. The first one is easier to predict, as it depends on the seasonal changes in consumer behavior. Incidental demand is caused by third-party factors such as changes in the economy or even in the epidemiologic situation on the other side of the world. This demand is much harder to predict, as many factors are involved.

9. Fraud detection & prevention

Fraud is the bane of the eCommerce industry. In the first half of 2022, the industry lost $3.56 billion to online fraud, which is an almost 50% increase compared to the same time period a year ago.

eCommerce fraud detection machine learning has the power to detect and prevent fraud. Machine learning algorithms spot recurring patterns among the datasets they process. They can also take note when something breaks the rules. For example, an unusual frequency of transactions can be a sign of fraudulent activity.

Aside from credit card fraud, machine learning can also prevent other types of fraudulent activity, such as Return to Origin fraud when criminals abuse the refund policy or account takeover, which is considered the most sophisticated type of eCommerce fraud.

10. Delivery optimization and autonomous vehicles

Machine learning can also streamline the processes of product shipping and delivery. For example, by analyzing information about traffic, weather conditions, and driver performance, the technology can accurately calculate the time of delivery and recommend the best route.

Amazon is taking this approach a hundred steps further with anticipatory shipping. The company uses machine learning to track customers’ purchase habits and predict future orders in order to transfer products to the closest warehouses. As a result, Amazon can provide one-day delivery with inexpensive shipping costs.

11. Better inventory management

As discussed, machine learning helps online stores better control product inventory; but what should you do if the system predicts a significant drop in sales and you already have these products in stock? Machine learning algorithms can help you decide how to boost sales with upselling, cross-selling, special offers, discounts, etc.

12. Predictions via ML

The technology can be used to understand your audience better and predict their future behaviors.

Here are a few things that ML can help you with:

• Predict Customer Lifetime Value (CLV) to better understand the value of customers.
• Predict the possibility of a purchase based on previous purchases and behavior. Based on the data, you can better understand when to send an incentive in the form of a discount, special offer, or related products to increase average order value
• Predict customer returns and purchases to better craft marketing messaging. For example, customers that are more likely to return might need less attention from your side, while visitors less likely to become repeat customers might require your special attention and additional incentives.

Useful eCommerce Machine Learning Tools

There are thousands of solutions with machine learning for eCommerce. Businesses can use the power of ML and AI for data-driven decisions, high-accuracy predictions, and a better customer experience.

Tools for Using Machine Learning in eCommerce

Here are some of the tools you might be interested in exploring:

• Deloitte Trellis – ML-powered platform designed to solve retail’s challenges around demand forecasting, replenishment, procurement, pricing, and promotions.
• Choice AI – this solution employs AI and ML algorithms that analyze customer data to predict fraud probability.
• Granify – analyzes customer behavior in real-time based on numerous attributes to provide the right information at the right moment for each shopper.
• Wiser – a dynamic pricing tool for eCommerce that keeps track of competitors’ prices to provide customers with the best offers.
• Syte – an eCommerce visual search platform that helps shoppers find the products they love.
• Algolia – one of the most popular platforms with powerful eCommerce search algorithms that help businesses maximize the speed of searches and the relevance of search results.

6-Step Process for Utilizing Machine Learning in eCommerce

You already know how ML can help solve business problems. You probably have some ideas about how these solutions can boost your eCommerce business.

Step-by-Step Process to Adopt Machine Learning for eCommerce

So, how can you get started with adopting this technology?

You can either choose one of the ready-to-go solutions or opt for custom development.

Let’s explore a 6-step process to implement machine learning in eCommerce.

Software development outsourcing is a challenging process. Read the guide Outsource Web Development Wisely: Useful Tips to learn about the best outsourcing locations, how to find the best team, and why development cost shouldn’t be the main deciding factor.

1. Identify problems you want to resolve

Let’s be honest, you can’t adopt technology just for the sake of technology. You need to understand your problems and what you want to achieve with an eCommerce machine learning solution.

The first thing to do is to find identifiable goals. For example, you may find that you have a high checkout abandonment rate. Your aim could be to reduce the bounce rate by offering highly personalized special offers and discounts. This is just one example of a specific goal you can achieve with machine learning.

You can either do the research yourself or hire a company that provides business analysis services to help you better understand how to strengthen your business.

2. Select the right tool & process

A few options are available if you want to adopt machine learning for eCommerce. For instance, you can choose one of the ready-to-use solutions, some of which were mentioned earlier in this article. As well, you can invest in developing a fully customized ML-based eCommerce solution from scratch.

The first option is more useful for businesses with straightforward requirements. Big and enterprise eCommerce companies with specific business requirements might benefit more from custom development.

3. Define technology and capability gap

Machine learning solutions have different barriers to adoption. While some of them are straightforward and easy to use, others require an in-depth knowledge of machine learning and software development technologies.

Before making a decision, consider your business’s capabilities and tech resources. You might need to hire a development company to implement a ready-to-use technology in your operations.

4. Find and allocate a dedicated team

If you build an eCommerce machine learning solution from scratch, you’ll need to find a technology partner to help you with this task. You can hire in-house developers, an outsource team to work on your project part-time, or a dedicated development team with the ultimate focus on your project.

5. Comply with data legislation

Over the past few years, data legislation has increased significantly. For example, in the European Union, you must comply with GDPR (General Data Protection Regulation) and there is a CCPA (California Consumer Privacy Act) in the USA.

Any ML solution should be developed in compliance with these regulations. You’ll be charged huge fines if you’re guilty of breaching them.

6. Implement, measure the AI algorithm and scale

When you’re just starting out with adopting a new machine learning solution, you should start small in order to analyze a specific eCommerce data-set first. This way, you will be able to better test the insights, predictions, and results delivered. You can also find a company providing software testing services and have them test your solution before rolling it out.

When the performance of your new solution satisfies your requirements, you can further scale and expand said solution. Since machine learning deals with a lot of data, you need to ensure your servers can withstand a growing load. So, finding someone who provides DevOps services is the next step to take.

Final Takeaways: Implementing Machine Learning for eCommerce

eCommerce machine learning solutions help companies make data-driven decisions, provide a better customer experience, increase conversion levels, and boost revenue.

Machine learning in eCommerce can help you achieve several goals:

• Reduce abandonment rate
• Improve conversions
• Fix product inventory issues
• Improve demand prediction
• Add dynamic pricing capabilities
• Get better insight into customer behavior
• Provide a personalized shopping experience
• Reduce customer support expenses
• Provide better website searches
• Implement visual
• and much more…

Want to adopt an ML-based solution for your eCommerce business and need help with the task? Drop us a line and we’ll be glad to discuss your project and provide you with a rough project estimate.

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