Machine Learning and eCommerce: Your Ultimate Guide to Innovative Use Cases and Strategies
The world changes, and B2B ecommerce changes as well. We have already written about how Artificial intelligence affects ecommerce. Today, let’s dive deeper into the world of Machine Learning technology — an inevitable and essential part of AI.
According to Statista, 17% of all B2B sales will be online this year. As digital commerce takes pace, Machine learning (ML) helps it to build and develop new AI features in the best B2B ecommerce platforms to overrun its competitors. Let’s see how it works.
- HOW IS MACHINE LEARNING USED IN ECOMMERCE?
- THE IMPACT OF MACHINE LEARNING ON ECOMMERCE
- AI AND ML: A POWERFUL SET FOR ECOMMERCE
- BOOSTING ECOMMERCE SALES WITH MACHINE LEARNING
- ESSENTIAL MACHINE LEARNING ALGORITHMS FOR ECOMMERCE
- KEY MACHINE LEARNING ECOMMERCE TECHNIQUES
- THE TANGIBLE BENEFITS: CAN ML IMPROVE ECOMMERCE PERFORMANCE?
- ADOPTING MACHINE LEARNING IN YOUR ECOMMERCE BUSINESS
How is Machine Learning Used in eCommerce?
The main idea of AI and machine learning in ecommerce is to make artificial intelligence able to develop. Just like any intelligence, AI needs new info to grow. While automatization is based on the outer rules, AI needs learning: it needs many data and cannot advance without machine learning. Businesses have to adapt and choose their way to implement machine learning to help their AI tools grow:
- The first way to go is personalized product recommendations. How does this work? Companies store many data about past purchasing and browsing behavior. Feeding these data to AIs will lead to the capabilities of forecasting and recognizing what the buyers need. This is a perfect way to guess what to sell, when to sell, and who will buy it.
- Flexible pricing. No, we do not mean changing the price in accordance with rules (e.g., Black Friday is coming). We're talking about “teaching” your AI to recognize customer behavior, analyze what they look at, and what they need more. At the most perfect moment, AI reduces prices right for those products customer needs now.
- Inventory and logistics management. Forecasting inventory needs and optimizing storage also works for AI. Unlike automated processes, AI can learn from past experience you provide (or it learns automatically) and predict potential product shortages or logistical problems. Once you get it, you will always know when it’s time to refill your warehouse.
- Customer service and support. A wide range of capabilities AIs offer includes personalized communication and chatbots. Instead of using rules based on your knowledge, let the AI learn how the client communicates, what they usually ask, and what they need first. You will see how UX grows along with how much data you feed to your AI tools.
The Impact of Machine Learning on eCommerce
The first thing to mention is always customer experience. Machine learning for ecommerce can make the personalized shopping experience real. The algorithms analyze vast amounts of customer data, from browsing history to purchase behavior, and the system gets to learn how to provide the most tailored and efficient customization. This means more than just providing better CX: the company gets deeper analytics and can use them during other campaigns or to plan sales.
eCommerce machine learning empowers businesses to make data-driven decisions. Predictive analytics, powered by ML, forecasts demand patterns and opportunities the company has in sales. The result is reduced overstock and minimized stockouts. Overall efficiency and profitability grow while bringing more and more value to customers.
Furthermore, machine learning contributes to the enhancement of customer support. As we discussed, AI-driven chatbots and virtual assistants become smarter; they provide real-time assistance and give what the customers really want.
One more way — AI and ML play a pivotal role in fraud detection, security, and data privacy compliance.
AI and ML: a Powerful Set for eCommerce
As we already explained, artificial intelligence is a huge set of technologies able to simulate human capabilities of thinking, analyzing, and making decisions. Machine learning is only one of the tools inside the general term of AI. So, ML is always AI, but AI includes much more than only machine learning. So how is machine learning used in ecommerce?
Besides machine learning, B2B ecommerce companies also use natural language processing, neural networks and computer vision. Here are some examples of how these tools empower ecommerce:
- Natural language processing (NLP) enhances a company’s search, navigation and sentiment analysis. This makes customer support deeper, providing accurate replies.
- Neural networks are used for predictive analysis. They improve recommendations and detection of faults or anomalies that might lead to some faults.
- Computer vision tools are used mostly by online-offline supermarkets for visual search, quality control, and more.
Boosting eCommerce Sales with Machine Learning
Just like other AI tools and technologies, machine learning aims to revolutionize the way businesses operate. Communication becomes better, and the total value of everything the company does grows. So how can machine learning improve ecommerce sales? Here, we have selected some benefits ML brings to companies and their users:
- Exceptional CX
The browsing and purchase history of our users can provide enough data to improve the company's communication with its buyers. While it could be hard to analyze in a traditional way, ML tools are able to see users' preferences in this information in minutes, bringing personalized product recommendations. AI-powered chatbots are another side: modern virtual assistants can provide real-time customer support, answer queries, and even offer products that we could never guess that the customer needs.
- Improved decision-making:
The more data you have, the easier it is to realize what to wait for the company in the future. Data collected with AI tools are not only fed to ML software but are also organized and provided to the owners in the form of tables, charts, and recommendations. Based on this data, the owner of the company gets the power to enhance operations and sales easily.
- Streamlined logistics and operations
AI and machine learning in ecommerce can optimize inventory. For example, it is always hard for businesses to predict logistics. Very complex tools are used to control all the operations. AI and ML can make it all easier: they take factors like seasonality, historical sales data, and market trends, and optimize the supply chain operations for cost-effectiveness and efficiency. Moreover, ML can help detect and prevent fraudulent transactions, reducing financial losses.
- Customer service and engagement
By utilizing machine learning and ecommerce common practices, it is possible to assess customer sentiment and pinpoint areas for development in products or services by analyzing customer reviews and social media mentions. With the assistance of AI-powered chatbots and virtual assistants, routine customer inquiries can be dealt with, enabling human agents to tackle more intricate issues.
- Visual Search and Image Recognition
Visual search technology is widely used to assist customers and internal users search for particular products. Virto Commerce also offers this capability. Powered by machine learning, the visual search tool allows customers to find exactly what they want, quickly and efficiently.
- Smart recommendations
How does the recommendation system usually work? The automated system based on rules offers similar products by name or description. Innovative ML-based tools offer search and recommendations by the image, by semantic search (not precisely the same description, but the products are actually the same, and more.
Essential Machine Learning Algorithms for eCommerce
Now, your question is probably — what are the key machine learning techniques used in ecommerce? Let’s dive deeper into some technical side of how B2B ecommerce uses machine learning to enhance its operations and customer experience. Here are three main algorithms and machine learning models for ecommerce helping it to function:
This one actually classifies items and objects following some always-changing characteristics. The algorithm induces two techniques:
- Logistic regression. This is used for binary classification tasks. For example, the system determines whether a customer will make a purchase (yes/no). Another example is realizing (by some indications), whether an email is spam or not.
- Random forest. Random forests are versatile for both classification and regression tasks (see the next). In ecommerce, they can be used for product recommendation systems. There is a huge list of features, randomly described without any rules, and the system can categorize these products or predict customer behavior based on these features.
The regression algorithm is widely used in forecasting. The two main techniques are the following:
- Linear regression. The linear regression in AI supports sales forecasting. It helps businesses predict future sales based on historical data, seasonality, and other factors.
- Time series forecasting. This kind of algorithm includes, for example, the ARIMA model. Long Short-Term Memory (LSTM) neural networks are tailored to predict trends and forecast how the inventory should be enhanced.
This one is similar to the mechanic segmentation in manual or automated systems. The two examples below are used to categorize whatever you need, from customers to products and more.
- K-Means. This one groups customers based on similar behavior. The data received allow for targeted marketing and personalized product recommendations.
- Hierarchical clustering. What can be better than the hierarchical categorization of products in an inventory? The technique helps organize products into categories and, further, subcategories.
Collaborative filtering algorithm
This type of ML algorithm is mainly used for accurate recommendations based on the analysis of similar groups. See the two examples here:
- User-based collaborative filtering. This one assesses the behavior of similar users based on the formed group preferences and offers personalized product recommendations.
- Item-based collaborative filtering. The technique is tailored to recommend products similar to the customer's preferred products. Companies can base their cross-selling and upselling campaigns on this tool.
Dimensionality reduction algorithm
This is an interesting one: the technique helps make vast amounts of data clearer and transparent, kind of "reducing" them. With the tool, you can visualize large datasets as simple ones and better understand customer behavior.
Key Machine Learning eCommerce Techniques
Deep learning is not a simple algorithm: this is a class of algorithms able to extract important information from massive amounts of data. The approach works without any human intervention. In ecommerce, the process usually includes image and voice recognition. Companies further use such data to enhance visual search, product image classification, and quality control.
Reinforcement learning algorithm
Along with the supervised and unsupervised machine learning for ecommerce. This is used when other methods can’t be efficient enough as it can work without any correction searching for an optimal balance. Such algorithms are tailored to optimize pricing strategies in accordance with the market data.
Neural networks and language models like ChatGPT include complex pattern recognition and natural language processing like sentiment analysis to understand the level of customer satisfaction and categorize items in accordance with the defined order.
The Tangible Benefits: Can ML Improve eCommerce Performance?
How does machine learning affect ecommerce? Absolutely, ML can lead the company to quantifiable benefits. Some of them include increased sales, reduced operational costs, and improved customer retention. To provide an example, we have selected some machine learning cases on ecommerce that successfully leveraged ML.
The first thing to say about Amazon machine learning use case in ecommerce is that Amazon uses ML for personalized product recommendations. Their recommendation engine is powerful enough to be considered as the tool providing a huge part of the total business revenue.
As for operations enhancement, ML-powered robots optimize order picking and packing at the warehouses. This definitely reduces operational costs and improves the time of order fulfillment.
The Amazon Echo and Alexa use natural language processing (NLP) — they do not only recognize words but learn to understand the mood and the level of satisfaction of their users.
Netflix's recommendation system suggests high-quality personalized content to subscribers, increasing user engagement and retention. As a result, subscription rates grow, and the company earns a higher LTV for each customer.
eBay utilizes ML algorithms differently: they do not only use them for personalization but can also enhance the search feature for users. Increased sales and greater loyalty come with ML usage.
Adopting Machine Learning in Your eCommerce Business
Before learning, how is AI and ML used in ecommerce, find out the perfect steps to start your first AI implementation:
1. Define clear objectives.
Think about your business goals and what you want to achieve with machine learning. Identify what you want to improve and list the bottlenecks and opportunities.
2. Data quality and collection.
Ensure that your data is clean, relevant, and well-structured before use. Collect as much data as you can and check if the data is not outdated.
3. Talent and expertise.
Try to hire a team of knowledgeable experts. Another way to go is to partner with an expert team who will help you to deploy and manage the systems, at least at the initial step.
4. Data privacy and security.
Be mindful of data privacy regulations, and implement appropriate data protection measures to ensure the security and privacy of your customers' data.
5. Select appropriate algorithms.
Choose machine learning algorithms that will be useful in achieving your goal. For example, if you want to improve product recommendations, consider selecting collaborative filtering or deep learning models.
6. Ensure your B2B ecommerce platform supports chosen algorithms and technologies.
Check if the system is API-first and modular, like Virto Commerce B2B ecommerce platform. For example, Virto Commerce supports connection to any third-party machine learning or any other AI tool. Moreover, thanks to Virto Atomic Architecture™, Virto provides some integrated capabilities, some NLP techniques, and more.
Many features like semantic search and natural language processing come out of the box. Other solutions can be connected to Virto seamlessly to enhance the system.
Machine learning is an essential part of modern B2B ecommerce, just like artificial intelligence is. The important step before implementing any ML tools is to make sure your B2B ecommerce platform can easily support such integrations and handle the results of AI operations.
Virto Commerce is an API-first modular system and an extensible commerce platform that is tailored to handle it all. Thanks to Virto Atomic Architecture, the solution can be easily modified in accordance with the requirements of AI tools, and the API-first approach helps to connect to whatever solution you choose.
These capabilities mean that once you purchase Virto Commerce B2B Platform, you will never need any other mediocre tool to develop further when the whole market turns to AI.