ECommerce is an industry that relies heavily on technological and digital advances. But how does machine learning fit into this picture?
There’s no better way to conceptualize the growing power of the eCommerce sector than by referring to the figures that back up the trend. For example, Statista predicts that the number of users in the eCommerce market will reach 284.6 million by 2025. In addition, user penetration is expected to be 83.6% by 2025.
On the sales side, eMarketer research shows that in 2022 global retail eCommerce sales will surpass $5 trillion for the first time, accounting for more than a fifth of overall retail sales. And by 2025 total spending will exceed $7 trillion.
This exponential growth can be explained by the introduction of numerous technologies to the sector, such as digital payments, platforms’ adjustments to various devices, omnichannel shopping, and of course machine learning.
Let’s take a closer look at what machine learning can do in the world of eCommerce and how it can improve the way it functions.
Top 7 machine learning use cases in eCommerce
We have so far touched on the business advantages of machine learning for the online shopping experience in general terms. Let’s now take a look at specific machine learning use cases in eCommerce.
1. Personalized customer experience
Applications of machine learning in eCommerce utilize customer data and behavior to tailor the user experience to each site visitor. How exactly does it work?
An algorithm processes a large amount of structured and unstructured data, including images and text, and finds patterns in the data while also taking into account a range of factors such as favorite style and color, image intensity, activity history, and preferences.
Properly personalized website or mobile application content increases conversion and customer engagement. So it is no surprise that tech giants like Amazon and Netflix rely on machine learning to create personalized content strategies.
Beauty start-ups are also leading the way. For example, HelloAva is a skincare platform that helps users find the best products for their skin type. First, customers must create a user profile and complete a skin quiz. Then, the ML-powered system analyzes the data and provides recommendations from a bank of 2,000 products chosen by aestheticians.
2. Supply chain management
Like many other businesses, eCommerce is all about supply and demand. As an online retailer, you must guarantee that you have the right stock in the right quantities to meet your customers’ needs.
Customer requirements, however, shift over time. As such, the more proactive your inventory and supply chain management, the better. In addition, machine learning enables businesses to make accurate real-time forecasts. The ability to predict fluctuating customer needs keeps them ahead of the competition while helping them to improve their products.
Leading the way is Microsoft. Using machine learning, it has built an integrated supply chain system to capture and analyze data in real-time. The company’s robust supply chain uses proactive and early warning systems to mitigate risk and resolve queries quickly.
Alphabet also leverages ML, alongside AI and robotics, in order to become completely automated. It relies on flexible and responsive supply chain management that can seamlessly collaborate across regions.
Machine learning models for eCommerce, used to make predictions, reduce a company’s costs and increase customer satisfaction.
Machine learning can be used in eCommerce to create churn prediction models. These models consider factors like customer purchase history, demographic information, and social media data. Systems such as these will identify customers they are at risk of losing, and thus increase the retention rate and bring in a stable revenue stream.
Facebook is a good example here. By assessing the user’s likes, their friends’ likes, and location data, Facebook deploys this information to determine the content it feels its user will enjoy through features like Facebook Watch.
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4. Targeted advertising
In physical stores, segmenting is done primarily by sales associates. For example, through observation and questions they can gather more than enough information about demographics, or about the customer’s needs or their doubts over products.
When the customer is engaged in an online experience, segmentation has to be based on behavior that can be measured. And on-site search is a great place to start.
Part of the demographic information is readily available as soon as someone signs up — most of the time, you will know their age, location, and whatever data you ask for. You can also observe their site behavior, such as content preferences, what materials they download, how frequently they return, and how often they make a purchase.
These interactions generate massive amounts of data, and machine learning allows us to assess behaviors and identify trends. Moreover, these patterns help us to make more effective marketing decisions. We can, for example, build ultra-targeted campaigns with the most relevant messaging and enhance our conversion rate by giving customers exactly what they want.
The recommendation engine is the most widely used application of machine learning in eCommerce. An effective product recommendation engine can help contribute substantially to your revenue and average order value (AOV).
The designs of recommendation engines are based on a collaborative filtering algorithm that analyzes historical purchase data from customers who bought similar products, or looks at the user’s buying patterns.
Moreover, ML-powered engines analyze profiles of previous customer activity, preferences (such as favorite color), social media data, location, and weather. In the end, the results will be displayed on a personalized page with suggestions for products that will most likely interest them.
Companies like Netflix, YouTube, and Amazon use recommendation engines based on Python that interact smoothly with their microservices.
6. Fraud prevention and protection
In this age of cybersecurity awareness, you might think eCommerce fraud is a thing of the past. Unfortunately, you would be mistaken. The size of the eCommerce fraud detection and prevention market continues to grow steadily. Recent reports estimate that this figure will reach over $69 billion by 2025.
Fraud detection and fraud protection are essential processes for all online stores. For these purposes, machine learning models can be trained to identify common patterns associated with fraudulent activities, such as abnormally high order values or customers placing orders from new IP addresses.
In addition, some eCommerce platforms like Alibaba and Amazon possess built-in fraud detection systems that use machine learning algorithms.
7. Pricing optimization
Machine learning can be beneficial when it comes to dynamic pricing and can help enhance KPIs.
Businesses are well aware that before making a purchase, consumers like to compare the pricing of the same product across several eCommerce platforms. These days, retailers and eCommerce platforms use machine learning algorithms to offer dynamic pricing.
Machine learning enables retailers to calibrate the prices based on supply, demand, minimum price, operating costs, and competition. All of this would be impossible to achieve manually.
What’s more, machine learning automates vast amounts of data collection and offers better dynamic pricing in real-time. It also helps businesses anticipate early trends and predict demand for products that do not have transaction data.
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What are the benefits of machine learning for eCommerce businesses?
Machine learning (ML) offers many benefits for eCommerce businesses. These include better conversion rates, the ability to create personalized marketing campaigns, and higher overall operational efficiency.
Machine learning in eCommerce helps increase customer conversion rates in two ways: through on-site search engines and through product recommendations.
On-site search engines
Via natural language processing, search engines can recognize what is written into the search bar. ML algorithms use what they’ve learned from past searches to show the users exactly what they’re looking for, even if they don’t type in the name of a specific product or a detailed description.
Algorithms can assess customers’ activity by recognizing the products they browsed or purchased, as well as the information with which they interacted. When users return, they receive recommendations for products similar to those in which they first expressed their interest.
As a result, an eCommerce business is capable of providing a hyper-personalized customer experience, which leads to increased sales. Happy customers = better business, right?
Tailor-made marketing campaigns
In the era of big data, applications of machine learning in eCommerce can redefine marketing campaigns entirely, making them highly relevant to the target audience.
In this process, the patterns identified by machine learning algorithms are critical because they enable more accurate customer segmentation. This allows businesses to split up clients depending on their interests and target them with far more relevant marketing materials.
Additionally, algorithms can learn client behavior and provide highly relevant retargeting advertising.
Not all benefits of eCommerce machine learning are related to customer-facing procedures. Algorithms can also deliver real-time insights to help improve overall operational efficiency.
Consider stock management and inventory accounting, for instance. Analyzing client data is the most important angle to take when it comes to achieving optimal results.
Machine learning makes such an analysis swifter and more accurate, from calculating eCommerce sales, storage expenses and tax implications, to predicting future demand. As a result, the eCommerce business has all the key information it needs to implement the most effective operations imaginable.
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Machine learning technology directly impacts business growth in the eCommerce industry, and its influence is only going to grow. From customer experience to inventory management and efficiency, machine learning will continue benefiting the sector as the technology evolves.
So, whether you market B2B or B2C, internationally or locally, here at PixelPlex we can help you create a machine learning model for the needs of your specific business. It will increase customer engagement and satisfaction and, in due course, boost your revenue.
Reach out to our machine learning specialists and start transforming your eCommerce business today!