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July 12, 2022

Machine Learning in E-Commerce: The Future of Online Shopping

This blog post covers all kinds of common questions related to the use of machine learning in e-commerce.

Mitya Smusin

Chief Executive Officer

Since the Internet has become a large part of our everyday life, it has also affected our purchasing behavior. As a result, the majority of the population has experience with ordering items online. You might not have noticed it, but technology is working behind the scenes at each of the steps in your online purchasing transactions. It suggests items, collects customer data by analyzing the points where you lose interest and make purchases, and following your behavior along the way. 

In fact, the e-commerce industry and technology are like two best friends that are totally inseparable. For example, e-commerce platforms are constantly growing, so the question of "how to build a scalable web application" will be urgent for you.

Machine learning algorithms also can help you grow your e-commerce business, get to know your customers better, increase customer satisfaction and improve relationships with your internal team. 

The perks of Machine Learning for Ecommerce are numerous and the challenges are few. Let’s take a closer look.

The Role of Machine Learning in E-Commerce: A Market Scenario

The pandemic accelerated the shift to digital, including for shopping. Now, almost three years since the beginning of COVID-19, making purchases online seems totally ordinary, and this is a big change for many people. 

According to The Adobe Digital Economy Index, U.S. consumers spent $1.7 trillion in online stores from March 2020 through February 2022, which is $609 billion more than in 2018 and 2019 combined. OptinMonster reports that  93.5% of global internet users have purchased products online (and 58.4% of internet users buy something online every week). Nasdaq estimates that, by 2040, 95% of all purchases will be completed in online stores. With the growth of the e-commerce industry in general, we’re bound to see more technology utilized in this field and the growing number of e-commerce businesses.

Machine Learning in E-Commerce Stats

At this point, it’s worth mentioning that artificial intelligence (AI) and machine learning (ML) are often used as synonyms, and that’s a big mistake that could lead to misunderstandings between you and your software development team or external specialists. Here’s a simple explanation of how these systems are used in e-commerce.

AI is used for automated systems that can help shoppers find exactly what they need.

ML has more use in digital retail. It is used to collect and analyze customer data, find patterns and trends, and make accurate predictions.

A recent study found that 73% of retailers believe AI and ML can be valuable in demand forecasting. As we’ll see later, ML algorithms are already defining the present and shaping the future of e-commerce in many areas. 

Best Machine Learning Strategies for Ecommerce 

Machine Learning in E-Commerce Strategies

1. Personalized Search Suggestions

Think of the last time you bought something from, let’s say, Amazon. Before you even typed what you were looking for in the search bar, it already recommended products that you might be interested in. How does Amazon know? The secret is data mining, processing, and analysis! The algorithms used by Amazon (and other e-commerce retailers) analyze large quantities of data to identify the groups of items with common traits. For example, when you’re searching for a cocktail dress, you’re more likely to see high-heel shoes in recommendations than gardening tools. That’s convenient, isn’t it?

The impact of personalized product recommendations is huge, and a quick look at the numbers makes this abundantly clear. SalesForce says that shoppers that clicked a product recommendation spent an average of 12.9 minutes on sites vs. just 2.9 minutes for those who didn’t see any. The customer return rate also goes up: 56 percent of customers are more likely to return to sites that offer recommendations. 

And that’s not all. Keep reading for additional details about increased conversions later on.

2. Dynamic pricing

This is the practice of offering products at different prices depending on market conditions. You may have experienced it when you ordered an Uber from downtown on Friday night and saw the price double compared to what you usually pay for that trip. Or if you fly often, you certainly know that it’s better to buy tickets early because as the departure day nears, the prices gradually increase.

In e-commerce, price dynamically changes (Amazon adjusts prices on a minute-by-minute basis) depending on many factors, including supply and demand, market trends, competition, and industry standards, consumer expectations and behavior, and inventory levels. 

The benefits? More customers and sales growth. McKinsey reports that dynamic pricing helped their US clients improve sales by 2 to 5 percent and increase margins by 5 to 10 percent.

3. Fraud protection

Machine learning is able to detect whether a credit card was used from an unusual location or for a suspicious transaction and block fraudulent payments super fast and efficiently. Machine learning in e-commerce can even predict whether a person is going to retract the payment after the purchase. To do so, the software development team trains the machine learning engine, by flagging previous cases of fraud and non-fraud to avoid false positives and to improve your risk rules’ precision. 

The simple rule proves its efficiency: The longer the ML algorithms run, the more accurate the rule suggestions will be.

4. Competitors analysis

Watching what your competitors do and how their clients react is crucial for adjusting your strategy and learning from their mistakes and successes. Machine Learning is here for you. It can identify what items your competitors have that you don’t, as well as which competitors’ offers are more appealing to clients. 

5. A/B testing

Even if you’re happy with the revenue, conversion rates, and other performance indicators, there is always room for improvement. A/B testing—or split testing—can improve important metrics for your e-commerce business.

Here’s what former Amazon CEO Jeff Bezos says about testing: “Experiments are key to innovation because they rarely turn out as you expect and you learn so much…If you can increase the number of experiments you try from a hundred to a thousand, you dramatically increase the number of innovations you produce.”

The ultimate goal of testing is to find the ways to make your customers happy. Happy customers buy more, complain less, produce more conversions, and much more. A/B testing is a great tool for understanding what your customers prefer and what works best for them, helping you achieve that ultimate goal. Machine learning can be used to run these experiments and provide you with insights.

6. Inventory management 

Inventory plays a key role in the e-commerce industry. Artificial Intelligence and Machine learning help e-commerce companies avoid order mix-ups, dead stock issues, insufficient stock levels, and warehouse disorder and eliminate human-caused problems in inventory management.

Case Studies of Machine Learning in E-Commerce

Let’s take a look at how machine learning is used by the three biggest e-commerce companies in the world. These case studies cover machine learning at Amazon (41% share of the US e-commerce market), Walmart (6.6%), and eBay (4.2%, according to Influencer Marketing Hub.)

Machine Learning in E-Commerce Cases

1. Amazon: You’ve surely heard of Amazon for sure. It’s a company with over a trillion dollars in net worth that provides a wide range of services such as shipping products, services, online streaming, and cloud management. 

In its e-commerce business, Amazon uses machine learning algorithms to identify patterns from purchasing history and then create specific targeted ads and recommendations for users based on tailored promotions of different types of electronic brands.

An unusual but interesting case is how machine learning contributes to Amazon’s commitment to reducing its carbon footprint. Justine Mahler, Packaging Senior Manager at Amazon explains: “Our goal is to minimize the amount of packaging customers have to dispose of, and to increase recyclability of our packaging. Carbon is the primary metric we hold ourselves accountable to when we think about sustainability for the customer—and our corporate responsibility is to be a leader in this space.”

Some of the most impactful ML models identify products that don’t need any packaging at all—like diapers. Mahler continues, “Instead of having someone inspect these products individually for their fragility or their volume, we use machine learning.”

2. Walmart: The second largest online retailer and investor in retail tech. Walmart is different from Amazon because it also operates brick-and-mortar stores. The company uses ML to create a seamless customer experience. Lauren Desegur, VP of customer experience engineering at WalmartLabs, explains: “We’re essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores.” 

3. eBay: eBay facilitates consumer-to-consumer and business-to-consumer sales through its website, which is best known for its auctions. The company uses machine learning for automatic machine translation to provide clients with a localized shopping experience. They also use it to enhance the search function.

This is what Selcuk Kopru, a Research Scientist at eBay says about it: “Searching has moved well beyond simple keyword matching. We have seen that extracting semantics from item titles and descriptions using machine learning algorithms has helped to improve relevance in customer search experiences.”

The CEO of eBay, Devin Wenig, continues: “We already use machine learning algorithms to recognize objects in listings, find similar products, and rank recommendations. In the next few years, we’ll witness an unprecedented convergence of technology, commerce, and consumer expectations.”

Business Benefits of E-Commerce Machine Learning 

You may be thinking something like this “You don’t understand. I don’t run Amazon or Walmart. We’re not that big but we want to use machine learning or artificial intelligence. How can it benefit my business?” Well, here are some major business benefits you may be interested in. 

Machine Learning in E-Commerce Benefits

1. Data-Driven Decisions

Want to predict your customers’ behavior? Wondering whether or not the product line will be in demand? Want to know future purchasing trends? No more guesses. Machine learning is here to help you to make more informed decisions. Here are some other things you can get from incorporating ML into your e-commerce business.

  • Gathering competitive business intelligence

  • Understanding the buyer journey

  • Getting realistic forecasts for future demands

Machine learning algorithms analyze large amounts of data and identify patterns to get the broader picture and understand correlations between existing parts of the whole business. As a result, you’ll get insights on how to improve your processes. 

2. Streamlined Workflow 

Machine learning is able to improve not only client-side operations but also internal processes. It can provide insights for the Marketing Department and help accountants with tax implications, plus it’s an absolute must for warehouse management.  

3. Marketing Campaigns that Hit the Bullseye

Successful marketing campaigns are always relevant to users. With the permeation of technology, the amount of user data collected makes it possible to launch and run effective campaigns. Machine learning algorithms improve the quality of data analysis, enabling more data to be analyzed in less time, and they also adapt to changes and new data. Moreover, these algorithms allow companies to automate marketing processes and avoid routine work. 

In addition to personalized recommendation systems, ML can also improve targeting at every stage. For processes ranging from the launch of campaigns based on user segments to the analysis of their effectiveness, machine learning algorithms can be used to collect data and make adjustments.

4. Increased Conversions   

Following from the previous point, machine learning can improve conversion rates. Consider the cart abandonment rate, a metric that can be frustrating for e-commerce managers and owners. The average cart abandonment rate across all industries is nearly 70 percent, accounting for $18 billion in lost potential sales revenue for e-commerce brands each year. That’s huge! 

By analyzing customers’ data and identifying patterns in their purchasing behaviors, ML can help to create an enhanced and more pleasant user experience, which could potentially improve this cart abandonment rate.

Guide for Adopting Machine Learning in Your E-Commerce Business

4 Steps to Incorporate Machine Learning

Machine Learning in E-Commerce Steps

Now that you understand what machine learning is used for in e-commerce, you may be wondering how to adopt this powerful technology for your business. The following four steps will describe the process and help you to get started.

1. Identifying growth points

Knowing exactly what you want to achieve or improve is fundamental for the whole process. It will ensure that you get there fast and don’t waste resources on unnecessary iterations. 

Do you want to streamline internal workflow, improve the customer journey, launch more relevant marketing campaigns, or find out more about your competitors? Think about this before taking the second step. 

2. Observing your technology gap

While most machine learning solutions are relatively easy to incorporate, it’s better to understand what your existing infrastructure lacks and prepare in advance for future changes.

3. Finding a dedicated team

These people will keep things on track and give your project the attention it needs. They will be responsible for setting up systems for future data collection and collation, choosing the best machine learning tools or coding unique solutions, and implementing pilot programs. 

4. Measuring and scaling

Start small, then observe how it works, adjust if necessary, and scale. That might sound obvious but it’s essential to the process. This is how development works and how your team will be able to manage the project, use machine learning to its fullest potential, and give your e-commerce business priceless insights and game-changing tips. 

Low-Code Tools to Get Started with Machine Learning in your E-Commerce Business

These are tools that enable your team to skip coding and proceed to production. They rely on modeling and graphical interfaces. 

Machine Learning in E-Commerce Low-Code Tools

1. GA360: Provides the tools and support that enterprise teams need to get actionable insights from their data. Paired with BigQuery, GA360 can help you build a recommendation engine driven by insights drawn from user actions such as their number of clicks, time spent on a page, and products clicked and bought. This is a paid tool but it’s also customizable, so you can adjust the user experience even without having historical user data.

2. BigQueryML: Used to build complex machine learning models. Unlike GA360, BigQueryML requires you to have historic user data to build a good recommendation engine. But it’s not complicated since it’s a fully managed service that not only selects the appropriate model for you but also takes care of deploying it and providing you with appropriate inferences. 

3. Granify: In previous sections, we mentioned user segments and their importance for creating personalized user experiences. Granify is here to streamline this process. It is able to identify future customers and entice them to buy a product. How does it do this? By mapping out a customer journey and identifying the most optimal point to achieve buyer conversion. Granify not only automates the implementation of Machine Learning but also provides nifty ways to analyze the traffic reaching your website, all without the need for a data science team

Low-code approaches are great except that they can be expensive. If you have a data scientist on your team and aren’t in a rush for implementation, code-intensive tools that are more cost-effective can be a good option. 

Get Started with Machine Learning Using Code-Intensive Tools

Python: Staistictimes claims that Python is the most popular programming language in the world. And no wonder. It has a simplified syntax, is free to use, and is supported by an extremely large ecosystem of libraries and packages. 

A strong data science team can build custom machine learning models that will address and solve your specific problem. 

It’s a great option for e-commerce website development too because it’s simple, secure, and scalable. 

TensorFlow Garden NeuMF is an open-source Python library used to create deep learning models. It’s the fastest way to create and implement a recommendation engine. This tool is free to implement and integrates quite readily with most infrastructures, but you’ll need a strong Python developer and an ML engineer on your team.

Wrapping Up

Machine learning in e-commerce has changed the way we make purchases both online and offline, too. Its influence is only expected to grow, bringing more advantages to e-commerce managers and clients.

Implementing machine learning algorithms is not simply a luxury for industry giants but actually a must-have for click-and-mortar retailers. Without it, they are sure to be left behind. The advantages are numerous and there are many ways to start implementing ML in your e-commerce business. Now that you have a solid knowledge base, why not take the first step now?

🛒 What’s machine learning for e-commerce?

Machine learning (ML) is a system that collects and analyzes customers’ data, finds patterns and trends, and makes accurate predictions.

🛒 Who has been implementing it?

Machine learning is used by a large number of online retailers, including the biggest companies like Amazon, Walmart, and eBay.

🛒 What is ML used for in e-commerce?

It has many use cases. For example, ML algorithms can be used to launch highly relevant marketing campaigns, build recommendation systems for customers, and create hyper-personalized purchasing experiences.

🛒 How do I start implementing ML in my business?

First, you should identify what problem you’re trying to solve with it. Second, understand the capabilities of your infrastructure. Third, hire a dedicated team.

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