How Machine Learning Works — The Bare Essentials
Machine learning, or ML, is a subfield of artificial intelligence that enables computers to learn from data and refine this learning over time, without being explicitly programmed.
The essence of ML lies in designing algorithms—instructions for a computer to follow—that can make informed predictions or decisions.
Think of machine learning as teaching a computer to fish. Initially, we give it a fishing rod (the algorithm) and teach it how to fish (training the model with data). Once it learns, it can fish by itself (make predictions or decisions) in any part of the ocean (new data).
This vast ocean of data can take many forms, from structured types such as transaction records or demographic statistics to unstructured data like emails, customer reviews, social media posts, clickstream data, images, and videos.
ML can use both historical and real-time data to predict future outcomes. The more diverse and high-quality data we provide, the better our computer becomes at predicting and decision-making.
ML has found its way into various industries. It’s used for personalized content recommendations on Netflix, accurate arrival times on Google Maps, suspicious transaction detection at JPMorgan Chase, demand forecasting at Walmart, language understanding by Siri, safety enhancements for Tesla’s autonomous vehicles, and beyond.
Types of Machine Learning in Ecommerce: A Closer Look
There are five main types of machine learning in ecommerce and across various industries:
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Supervised Learning: This type uses labeled data (data and corresponding answers). For example, predicting customer churn might involve training a model on customer purchasing history (features) and whether the customer remained or left (labels). Common algorithms include Linear Regression, Decision Trees, and Support Vector Machines.
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Unsupervised Learning: Unlike supervised learning, this approach relies on the machine to discover hidden patterns in unlabeled data on its own. For instance, unsupervised learning can help an ecommerce business segment customers into groups based on purchasing behavior, without predefining these groups. In this category, K-means clustering and Principal Component Analysis are commonly used algorithms.
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Reinforcement Learning: This type is more about trial and error. The machine interacts with its environment and learns to make decisions based on rewards and punishments. It can be utilized to optimize warehouse layout, for instance, reducing item retrieval time through learned placements. A common algorithm here is Q-Learning.
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Generative AI. Generative AI is a type of unsupervised learning that stands out due to its ability to create new data points similar to its training set. An ecommerce site might leverage this technology to create new product designs or realistic virtual model images. GANs (Generated Adversarial Networks) are popular models.
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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 ‘deep’) to progressively extract higher-level features from raw input. In ecommerce machine learning, this method is used for image recognition (identifying products in images) and natural language processing (understanding and responding to customer inquiries in human language). It’s the technology behind chatbots and product recommendation systems.
Real-world Applications of Machine Learning in Ecommerce:
Before jumping to our list of 11 key uses cases for ML in ecommerce, let’s see how some industry heavyweights have effectively blended ML with their custom ecommerce solutions:
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Amazon revolutionized ecommerce with its ML-powered recommendation engine which is driving 35% of its sales. Harnessing the power of big data, Amazon also adjusts prices every 10 minutes, leading to a profit boost of 25%.
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Alibaba leverages ML for ecommerce to detect and filter out counterfeit products. This has enhanced trust and reduced disputes.
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Pinterest employs computer vision technology to scrutinize the content of each Pin. This helps in filtering out abusive and deceptive content, optimizing ad positioning, and arranging nearly 300 billion Pins on a daily basis.
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JD.com, one of China’s largest online retailers, used machine learning to create an ultra-efficient supply chain. This technology elevated their procurement automation rate to 85%, while also reducing inventory turnover to approximately a month.
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Asos saw a threefold increase in revenues and halved their losses from returns.
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Uniqlo uses voice recognition and ML to guide customers to nearby stores to quickly find items they searched for on their smartphones.
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Dollar Shave Club taps the power of data and ML to anticipate what DSC products customers are likely to buy.
Ecommerce challenges and goals echo the same, regardless of scale. Even with a pandemic-induced slowdown, experts forecast the ecommerce market to exceed $8.1 trillion in just three years. The space is filling up.
For ecommerce business owners, tracking trends isn’t an option; it’s a requirement.
So, here’s our ultimate guide to deploying machine learning in ecommerce today:
1. Intelligent Search Solutions― Delivering What They Seek
When customers fire up the search bar, they’re likely ready to make a purchase. A detailed query like “limited-edition rose gold iPhone 13” is about a clear buying intent. But imagine their frustration when unrelated rose gold watches or earrings clutter the results.
Alternatively, consider a scenario where a customer has seen a unique lamp at a friend’s house and wants a similar one. But, how do they search for an “Industrial Loft Style Iron Cage Desk Lamp” without knowing its exact name?
Smart search, empowered by ecommerce machine learning, changes the game. It returns relevant results and intuitively fixes typos, interpreting “Nkie” as “Nike,” ensuring your customer doesn’t miss out on the perfect running shoes.
ML supercharges search in a number of ways:
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Suggesting product categories and descriptions automatically, using product details and image recognition
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Facilitating autocomplete as users start typing in the search bar
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Fixing spelling errors on the fly
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Powering visual search, where customers upload photos and the system finds the closest matching items available
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Detecting individual elements within images and using them as standalone search items
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Facilitating voice-activated searches
2. Personalized Product Recommendations― Custom-Crafted Shopping
Remember your latest shopping spree on, let’s say, eBay. Even before your fingers hit the search bar, tailored suggestions appeared. How did eBay seem to know your mind? The secret is smart data interpretation.
By using various algorithms of ML, ecommerce platforms can analyze a customer’s browsing history, past purchases, shopping cart contents, and even the behavior of similar users. This analysis leads to predictive product suggestions. So, when you browse for a vintage vinyl record, you’re more likely to be shown related items like record players or vinyl cleaning kits than random kitchen appliances.
The mechanics behind such recommendation engines is the following:
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Learning from the Crowd – Collaborative Filtering: This technique peers into a user’s past shopping habits, along with the choices made by other shoppers with similar tastes. For instance, if shopper A has bought books by Hemingway, Fitzgerald, and Salinger, and shopper B has picked Hemingway and Fitzgerald, it stands to reason that B might enjoy a bit of Salinger too.
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Content Knows Best – Content-Based Filtering: This method suggests items resembling those the user has previously shown interest in, relying on an analysis of product features. If a customer has been considering high-megapixel cameras, the system can suggest other high-resolution cameras.
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The Best of Both Worlds – Hybrid Systems: Combining content and collaborative filtering, hybrid systems can generate even more accurate suggestions. Netflix, for example, uses a hybrid approach that takes into account both user behavior and movie characteristics.
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The Deep Dive – Deep Learning Techniques: More complex techniques like Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) delve deeper into the data, finding patterns that traditional techniques might miss. They’re the ‘intuition’ suggesting a customer searching for camping gear might also need hiking shoes.
SalesForce highlights that site dwell time jumps from 2.9 minutes to an average of 12.9 minutes when shoppers click on a recommended product. Also, a site’s return customer rate climbs by 56% if it offers product suggestions.
McKinsey underscores this, revealing that algorithm-driven recommendations influence 75% of viewing choices on streaming platforms and drive 35% of Amazon’s purchases.
3. Smart Pricing― Setting the Right Price for Profit Maximization
Pricing isn’t an easy task. It demands an eye on rivals, seasons, market shifts, local demand, and even the weather.
When you ship internationally, the task twists into a puzzle, weaving in factors like local rules, shipping costs, and regional market rates.
Still, price is pivotal. Even a slight uptick above competitors can prompt customers to abandon their carts.
Instead of clutching to fixed prices and hasty markdowns when sales slump, there’s a solution — price adjustments, guided by machine learning. They help forecast prime prices, pinpoint when discounts are needed, or urge upsells when ripe.
With machine learning for ecommerce, all influencing factors can be evaluated instantly, enabling dynamic pricing on your site.
4. Customer Segmentation― Creating Unique Experiences for Unique Customers
Let’s step back and picture a store filled with customers, each unique in shopping habits, preferences, and budget. Addressing this diversity might seem daunting. But machine learning in ecommerce simplifies it with customer segmentation, grouping customers by shared traits for personalized marketing.
Take Emily, a book-loving loyal customer. Machine learning, leveraging techniques like predictive analytics, calculates her Customer Lifetime Value (CLV). It foretells that Emily might respond positively to a custom-made loyalty program. The prediction hits home, leading Emily’s purchases to double and enhancing the cost-efficiency of your marketing effort.
Then meet John, a sporadic buyer on the brink of becoming a lapsed customer, as identified by ML’s churn prediction algorithms. Offering him timely discounts on his preferred outdoor gear reignites his interest, saving a potential customer loss.
By painting a clearer picture of your customers, machine learning in ecommerce adds a personalized touch to your store. It transforms it from a one-size-fits-all model into a “made-for-me” destination, ensuring everyone from a loyal Emily to a wavering John finds what they need.
5. Chatbots―Seamless Customer Service at Their Fingertips
Managing customer support isn’t a clear-cut affair. Lean too much on human staff, and you end up with a sizeable, costly team handling inquiries that could be addressed by an FAQ page. But a fully automated system lacks the human touch, which can leave customers feeling frustrated.
ML-powered chatbots emerge as an ideal solution. They are cost-effective, providing round-the-clock support without a round-the-clock payroll. And they are more than your average responders. By learning from user profiles and past behavior, they tailor answers, boosting conversion chances.
Armed with deep learning and natural language processing, smart chatbots act as your customer service soldiers. They answer questions, handle complaints, suggest products, process payments, and track deliveries. They’re good at their jobs.
Furthermore, chatbots are getting better. They’re learning to understand not just what the customer says, but how they say it. With sentiment analysis and emotional AI, a chatbot becomes more than a tool. It becomes a listener, an empathizer. It turns customer service into something more. Explore below.
6. Sentiment Analysis ―Understanding Emotions to Improve Customer Engagement
Customers talk. In reviews, on social media, they spill thoughts, often coated in sentiment. “Page-turner,” they say, or “lifesaver in winter.” Not just words, but tokens of satisfaction. For businesses, it’s crucial to hear this sentiment and respond. Equally important is to spot the rare complaints, buried under mountains of data. But how can a business catch the signals amidst the noise?
This is where sentiment analysis powered by ecommerce machine learning steps in.
Sentiment analysis discerns the emotional tone underlying words, interpreting “not bad” as a thumbs-up to ensure business understands customers’ feelings.
Using NLP, deep learning, and some ML algorithms, sentiment analysis can help your ecommerce business in various ways. It deciphers product reviews and comments for insights to refine offerings, monitors social media buzz to measure public response to marketing campaigns, and unearths customer service hitches to enhance satisfaction levels.
But that’s not all. Sentiment analysis can do a more remarkable job when incorporated into a chatbot. It gives your bot an ability to feel. And here’s what you can get from your emotionally intelligent chatbot:
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Tailored Customer Experience: These bots read tone, sentiment, and feelings in customer chats, tuning responses to fit. The result is a more empathetic, personalized customer experience that boosts loyalty and satisfaction.
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Proactive Conversations: They’re not wait-and-see types. These bots engage customers based on their browsing behavior or past interactions, providing a smart way to upsell or cross-sell.
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Engaging Feedback: They’re good listeners, collecting customer opinions in an engaging manner to give a clear view into their likes and dislikes.
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Cart Recovery: Emotionally intelligent bots ping customers with abandoned carts, offering a hand or a reason to complete the purchase.
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Trend Spotting: These bots are great trend-spotters, finding patterns in customer interactions and providing useful input to improve products, services, or customer support.
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Customer Keepers: They also watch out for discontent, catching dissatisfied customers with sentiment analysis and stepping in a well-timed offer or message to prevent their churn.
7. Omnichannel Strategies―Reaching Customers Where They Are
In the theater of marketing, omnichannel plays a lead role. Done right, it unlocks higher retention, conversion rates, and revenue spikes. But the secret isn’t in more manpower – it’s in machine learning.
Take, for instance, a customer who switches between devices, browsing shirts online before finally buying one in-store. ML trails this journey like a shadow, capturing the full picture across platforms. It crafts a single, unified customer profile, breaking down device silos.
Imagine another who abandoned a cart full of dresses. ML doesn’t let this be a missed opportunity. It triggers a personalized email reminder, or a custom offer, nudging the buyer toward completion.
It’s machine learning for ecommerce that keeps your finger on the pulse of customer behavior. It notes what ads click, what content captivates, what emails get opened, factoring it all into its equations. And it doesn’t stop at analyzing; it learns, predicts, and personalizes.
8. Social Commerce―Harnessing Social Power to Harness Sales Opportunities
Social commerce is the new big thing. It’s a blend of online shopping with the social chatter we all love. By 2026, Statista predicts that social commerce sales could hit a staggering US$2.9 trillion.
People on social media aren’t fans of traditional ads. Many find them annoying. The Influencer Marketing Hub says the key is to integrate ads into social media posts. Make them helpful and interesting, not just salesy.
How? Machine learning for ecommerce holds the answer.
ML quietly crunches mountains of data ―likes, shares, pins, retweets, comments—into meaningful insights. That artisan coffee a customer never knew they wanted? ML brings it to their feed, no guesswork involved.
It draws links between what users like. It understands that if you love handmade soaps, you might also enjoy organic face oils. If you’re into rustic home decor, how about a hand-carved wooden clock?
In social media, ML can guide customers to the perfect fit. Isn’t that impressive?
9. Just Right Inventory―Stocking Smart for Ideal Product Mix
Inventory management is a chess game where foresight is key. It calls for a strategic understanding of data and the market landscape.
An overstocked warehouse ties up funds that could drive your business forward. For perishable or quickly depreciating goods, each day they’re static, their value diminishes. The ultimate misstep? A dry cash flow with empty product shelves.
Running a successful online store is about commanding your pieces wisely: monitoring stocks, reordering items, predicting demand trends, coordinating contractors, liaising with manufacturers, suppliers, mail services, and managing revenue.
This is once again where machine learning in ecommerce shines.
It watches every piece in your inventory, forecasting supply, demand, and cash flow dynamics, relying on a vast database of historical data.
It supports your inventory management decisions across multiple dimensions:
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Suggesting upsells when specific items gather dust
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Reading the runes of product demand influenced by seasonality or trends, suggesting larger orders
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Optimizing your supply chain, from streamlining delivery routes to scheduling
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Implementing dynamic pricing to adjust prices according to supply, demand, and market conditions
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Automating restocks to maintain ideal stock levels
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Spotting the slow movers to prevent overstock and free up storage space
Moreover, as mentioned above, sophisticated ML platforms are capable of analyzing data from social media. They sift through trends, viral moments, and celebrity influence, alerting businesses to the next ‘it’ product. A popular fashion item flares up on the scene? Machine learning spots it, anticipates the demand surge, and advises inventory adjustments.
No more stockouts. No missed opportunities. Businesses seize the moment, capitalizing on trending items.
10. Fraud Prevention―Safeguarding Your Business Transactions
Fraud takes a heavy toll on ecommerce. From stolen credit card usage to customer database breaches, or manipulated returns, ecommerce fraud bleeds money, erodes trust, and drives away customers.
Machine learning isn’t just solving fraud detection, it’s reinventing it.
It uses ‘anomaly detection,’ where algorithms analyze transactions by the millions, spotting unusual ones. It’s a feat beyond human capability in terms of speed and scale, yet routine for ML. From device type and location to time zone, ML flags inconsistencies like overspending, address mismatches, repeating orders with different cards, surprise international orders, or suspicious returns and reviews.
With cluster analysis, ML identifies risky customer segments, products, and periods, empowering businesses to be proactive against fraud attempts. And with social network analysis, it unearths coordinated fraud rings, by mapping and scrutinizing links between accounts, devices, and emails.
Moreover, ML algorithms in ecommerce root out counterfeit reviews. Language, IP address, review frequency, or even the time elapsed since purchase – nothing escapes their watchful gaze.
11. Smart Returns Strategies―Making Returns Work for You
One-quarter of customers, with intent, fill their carts over the brim, knowing some will return to the shelf. This dance of indecision, fear of ill-fitting garments, or shoddy quality costs merchants dearly. Unseen by the consumer, each return sets off a domino line of tasks: cleansing, repackaging, and readying for resale. If the product comes back ruined? It’s a stark loss.
Machine learning algorithms for ecommerce can combat excess returns through accurate product suggestions. Quality control becomes sharper, predicting and intercepting potential failures from historical data and feedback. Product portrayals ring true, curbing dissatisfaction born from misleading descriptions.
More so, ML forecasts return likelihood from factors as varied as customer history, product type, and price. In the fashion realm, ML turns virtual tailor, offering size recommendations custom-fit to individual dimensions.
ML reins in returns, protecting the merchant’s bottom line and enhancing customer satisfaction.
Wrapping up
So, there you have it. These are the 11 ways machine learning is making waves right now. Embracing machine learning in ecommerce:
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Enhances your understanding of your customers’ preferences
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Boosts your sales and amplifies average order value
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Trims out unnecessary processes
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Offers profound insights that exceed human capabilities
Stockpiling customer data without analysis? It’s like having a key but never unlocking the door. Integrating machine learning in ecommerce isn’t about keeping up with the times, it’s about setting the pace and leading the race.
