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De los datos a los beneficios: El impacto del aprendizaje automático en el comercio electrónico

AI y ML

AR/VR/MR

eCommerce

Tendencias

In a world where predicting your customer’s next action has become essential, machine learning (ML) hopes to be a magic pill for businesses. ML looks at vast amounts of data to predict customer behaviors and optimize everything from personalization to inventory management.

In this article, we want to show you how machine learning and eCommerce work together and how machine learning can be used in eCommerce to boost your bottom line.

The role of ML in eCommerce

The global ML market was worth $19.20 billion in 2022 and is expected to jump to $225.91 billion by 2030.

Seems huge, right?

And it’s all because of a few key trends changing how businesses and customers interact in the online shopping world.

Personalización

Shoppers today expect recommendations based on their past behavior, and when brands get it right, conversions go up. It’s all about making each shopping experience feel like it was tailor-made just for you.

Omnichannel experience

As buyers increasingly expect to buy online and pick up in-store, or even browse in-store and buy online, the lines between digital and physical shopping continue to get blurred.

Social commerce

Platforms like Instagram and TikTok are turning into online marketplaces where you can buy products. This mix of entertainment and e-commerce makes shopping easier than ever.

Sostenibilidad

More and more shoppers care about the planet and choose brands that share their values. From sustainable packaging to ethically sourced materials, green is the new black in the world of e-commerce.

Types of machine learning for eCommerce

Machine learning is fundamentally altering how e-commerce operates on the backend, allowing companies to analyze data and make better decisions in processes that are significantly more complex.

Up next, you’ll discover the key types of machine learning technology in eCommerce.

Supervised learning

This kind of learning uses data that contains both input and output. For example, if you’re trying to predict whether a customer will leave or stay, the input could be things like their purchasing behavior, and the output is whether they stayed or left.

To make predictions, a model looks at patterns in past data, like how customers behaved before they churned, and uses those patterns to predict future behavior. Typical algorithms for this purpose include neural networks, logistics regression, decision trees, and support vector machines.

Unsupervised learning

Instead of having labeled data (where we know the outcome), the machine is given raw, unlabeled data and has to find patterns or structures on its own. This is how unsupervised learning works.

Common algorithms used in unsupervised learning are K-means clustering, which groups similar items together, and Principal Component Analysis (PCA), which simplifies complex data by focusing on the most important features. These tools help the machine find hidden patterns without needing any predefined labels.

Aprendizaje por refuerzo

This type of learning is like teaching the machine through trial and error, similar to how humans learn from their mistakes. The machine interacts with its environment, takes actions, and receives feedback through rewards or penalties. Over time, it learns which actions lead to the best outcomes.

A frequently applied algorithm used in reinforcement learning is Q-learning, which helps the machine decide the best action to take in each situation based on past experiences.

AI Generativa

This type of machine learning implies the system is trained to create new data that closely resembles the data it was trained on. Unlike other types of learning that focus on classifying or predicting, generative AI focuses on creating something new.

A popular model used for this is GANs (generative adversarial networks), which consists of two parts: one generates new data, and the other evaluates it to determine if it’s realistic enough. The two parts compete with each other, helping the model improve and create more convincing results over time. This approach is often used in creating images.

No matter what ML type, we can help you make it work for your eCommerce business

How to use machine learning in eCommerce

Machine learning in eCommerce primarily focuses on two key areas: improving internal business operations and elevating customer experiences. However, if we dig a bit deeper, we’ll see that the number of machine learning solutions for eCommerce extends far beyond.

See below for some machine learning examples in eCommerce that drive innovation and efficiency.

Experiencias de cliente personalizadas

Customizing outdated marketing strategies would involve sending bulk emails to a customer base with predefined discounts. However, with the assistance of ML, discount strategies can differ across individuals depending on their past purchase behavior.

Sending people relevant offers makes it possible to increase customer loyalty, thanks to the high chances of an actual conversion. Customers love it when they get offers tailored to their interests, making them feel satisfied and turning them into repeat customers.

In platforms such as H&M, features like the personalized start page, Styleboard, and Visual Search rely on ML to recommend styles and matching items based on user data, including browsing history, trends, and images shared by other customers. ML algorithms also optimize checkout experiences by providing tailored payment and promotional options based on regional preferences and past customer behavior.

Key benefits of machine learning for eCommerce

Personalized experiences foster loyalty, keeping customers engaged and returning to shop.
Cross-selling and upselling based on individual behavior inspire customers to add extra items to their carts.
Relevant and timely nudges, like reminders or discounts, help complete purchases.
Insights from customer preferences guide better stock planning and distribution.

Precios dinámicos

Let’s say you sell a range of products, from electronics to clothing. As time progresses, you begin to see portable speakers become a sales hit. Instead of waiting for human intervention, ML algorithms pop up. They monitor the demand in microseconds to determine the number of requests for the product in question and place a suggestion to increase the price when there is a spike. At the same time, ML is tracking the prices of your competitors and adjusting your prices accordingly.

To avoid overwhelming you with facts that lack proper evidence, consider a real-world example instead. Adspert’s pricing tool, created with AWS services like Amazon SageMaker, uses a machine learning model to dynamically reprice products based on factors like visibility, profit margins, and competition. This tool helps sellers keep their products visible, which ultimately boosts sales.

In another scenario, let’s imagine you have a loyal customer base. ML rewards that loyalty by offering additional value to your customers. It analyses when shoppers tend to buy the same product during a certain period, and sets a personalized price tag on it for that timeframe or offers tempting discounts.

Key benefits of machine learning for eCommerce

Real-time price adjustments capitalize on demand surges, maximizing profitability.
Automated pricing cuts down on manual adjustments.
ML detects price-sensitive products and adjusts pricing to boost conversion rates.
Personalized pricing and discounts encourage repeat business.

Gestión de existencias

Vendors use machine learning models for eСommerce to better understand sales and seasonal trends. The model allows them to accurately forecast demand which, in turn, helps avoid stock-outs of best-selling products (and upsetting customers) or overstocking low-selling items, which leads to excessive inventories and higher storage costs.

Removing the manual inventory tracking allows ML to advance automated reordering or stock redistribution policies.

Suppose a product offered at a particular location is selling rapidly, the system can reorder the product from suppliers or transfer stock from another location automatically. Additionally, ML can anticipate supply chain disruptions and even seek alternatives, such as switching suppliers or changing the shipping route.

Here’s one more example that should help guide your perspective. Walmart uses AI and machine learning to optimize inventory management and deliver an exceptional holiday shopping season. With historical data, predictive analytics, and advanced supply chain systems, Walmart lets customers find the products they need at the right time and place, all while keeping costs low.

Key benefits of machine learning for eCommerce

Replenishment adjusts inventory in real-time, preventing disruptions.
Inventory distribution is optimized for faster delivery across warehouses.
Potential stockouts or excess inventory are spotted early.
Slow-moving products are identified for timely promotions or clearance.
Warehouse layout and order-picking processes are optimized.

Smarter site search

When shoppers browse for products on your e-commerce platform, machine learning steps in like a savvy personal shopper. It tracks what they’ve clicked on, bought, and browsed before, then uses that data to tweak the search results.

So, if a customer has a soft spot for a particular brand of sneakers, an ML-powered site will show those shoes first, even if the search query is a little off or contains a typo. No more “Did you mean…?” pop-ups — just results that make sense instantly. If they suddenly start eyeing hiking boots or a new color, machine learning will shift gears, prioritizing those items in the search results. Over time, a site’s search function starts predicting what customers want and helping them find it faster.

Another real-world example to inspire and illustrate the point. Alibaba’s AI innovations, such as Taobao Wenwen, improve search results by offering personalized product recommendations, summarizing pros and cons, and providing multimedia content like videos and livestreams directly tied to search queries.

Key benefits of machine learning for eCommerce

Accurate, personalized search results make customers more likely to complete purchases.
Tailored search experiences create a sense of personalization, fostering trust and loyalty.
Reduced customer support queries about finding products free up resources for other business priorities.
Machine learning adapts to growing product catalogs and customer bases without sacrificing efficiency.

Fraud prevention and security

Machine learning in eCommerce analyzes customer behavior to establish normal patterns and swiftly detect any deviations or anomalies. For instance, it might flag transactions coming from an unexpected location or a sudden spike in high-value purchases.

When ML spots something suspicious, the system can take immediate action, like blocking the transaction or asking for extra verification, while letting legitimate customers shop freely. As fraudsters come up with new tactics, the system gets smarter by updating itself with fresh data.

This means eCommerce machine learning can reduce fraud, maintain customer trust, and focus on growth without constantly monitoring for threats. The perfect example of this is Amazon’s fraud detection ML solutions that evaluate risk instantly, giving businesses the power to act immediately. Suspicious activity can be blocked or denied on the spot, while trusted transactions move forward without a hitch.

Let’s take a deeper look at eBay. They invest millions annually in technologies, partnerships, and human resources to fight unlawful listings. The platform uses a combination of automated filters, image recognition, machine learning tools, and manual review by agents to proactively detect and remove problematic listings before they appear on the site.

Key benefits of machine learning for eCommerce

Preventing fraudulent transactions in real-time helps businesses avoid chargebacks and financial losses.
Customers feel more secure shopping on a platform that protects against fraud, leading to higher trust and repeat purchases.
With fewer false positives, legitimate customers experience fewer disruptions during checkout which improves the chances of completing purchases.
A secure shopping environment improves a company’s reputation, which can attract new customers.

Marketing strategy

An online store marketing strategy that performs well can be even more effective when machine learning e-сommerce marketing is involved. For instance, ML algorithms can segment customers based on their buying habits, browsing history, and preferences, enabling businesses to send personalized offers or recommendations. It can also identify customers who are likely to churn by analyzing their activity and allowing businesses to take proactive steps, such as sending targeted retention offers.

It’s noteworthy that companies like Starbucks use AI to analyze customer behavior and offer personalized promotions or recommendations through their Deep Brew program. AI enables more efficient segmentation to target the right message to the right audience at the right time.

Key benefits of machine learning for eCommerce

ML personalizes marketing messages, leading to higher customer engagement through relevant content and offers.
Predictive analytics helps target the right customers with tailored offers, resulting in improved conversion rates.
ML-driven churn prediction models identify at-risk customers, enabling proactive retention strategies that reduce churn.
ML optimizes ad targeting and budget allocation, which results in more cost-effective and higher ROI marketing campaigns.

A/B testing with AI

Traditional A/B testing involves creating multiple versions of a webpage to determine which one performs better. It’s slow, tedious, and, frankly, a bit old-school. In contrast, machine learning accelerates the process. It dynamically tests and optimizes every little element on a page — the CTA button, the color scheme, or the layout — across diverse metrics like conversion rates and clicks.

And the best part? It can analyze and adapt continuously, identifying the best configuration faster than any human could. So, instead of running experiments for days or weeks, AI can fine-tune pages in minutes.

Key benefits of machine learning for eCommerce

Dynamic adjustments result in pages that convert visitors into customers more effectively.
ML optimizes elements based on user behavior, creating a more tailored experience.
Through continuously improving page elements, ML maximizes the value of each website visit.
ML can handle complex testing across multiple pages or platforms, scaling without additional effort.

Logistics and operational efficiency

Picture placing an online order and receiving it at your doorstep almost instantly. That kind of speed is powered by machine learning that takes vast amounts of data — your shopping habits, inventory levels, even road conditions — and uses it to make quick decisions that optimize delivery times and logistics.

Take Walmart, for example. AI is used to curate product catalogs, analyze customer and shopping trends, and speed up logistics for third-party sellers using Walmart Fulfillment Services. Meanwhile, Amazon leverages generative AI to take things a step further. It’s optimizing delivery routes, improving warehouse robotics, and predicting where inventory should be placed to make same-day shipping a reality.

Key benefits of machine learning for eCommerce

Data-driven delivery routes and predictive inventory placement provide quicker order fulfillment.
Smarter robotics and ML-driven systems speed up operations, reducing manual labor and improving accuracy.
Efficient routing and inventory handling reduce transportation and operational expenses.
Refined logistics lower transportation distances, minimizing carbon footprints.

Visual and voice innovations

When customers upload images or take photos, ML algorithms analyze the visuals, match them with product catalogs, and suggest similar items. Visual recommendations also personalize the shopping experience based on a user’s past behavior. Virtual try-ons, powered by augmented reality (AR), allow customers to see how products like clothing or accessories would look on them before purchasing.

A good example here is the well-known L’Oréal Paris. The brand has partnered with beauty tech leader ModiFace to create an immersive virtual makeup simulator. The Virtual Try On feature is fueled by augmented reality to deliver realistic makeup simulations, helping users personalize their beauty experience.

Voice search lets customers find products using natural language commands. ML-driven voice assistants also provide real-time customer support, answering questions or helping with order tracking. Using Azure AI technologies, ASOS integrated language models and trend data to curate fashion selections instantly, emphasizing customer preferences and the latest fashion trends.

Key benefits of machine learning for eCommerce

Visual and voice technologies allow brands to maintain a cohesive experience across websites, mobile apps, voice assistants, and social media platforms.
Consistent visual branding (logos, colors) and unique voice elements (tone, style) help reinforce brand identity.
Voice interactions can provide insights into customer sentiment and preferences and visual data reveals how customers respond to specific content.
Voice technology enables hands-free interaction, offering greater accessibility for people with disabilities and convenience for those on the go to engage with a brand.

Sentiment analysis & social listening

ML goes beyond simple keywords; it understands context, sarcasm, and emotional undertones, capturing sentiments that might otherwise be overlooked. This lets businesses address concerns faster and keep their finger on the pulse of trends, adjusting their strategies accordingly.

A good example here is Amazon, which has leveraged AI to help users quickly navigate and understand reviews, for example, by generating summary highlights that capture common themes and sentiments from reviews.

Social listening uses advanced tools to monitor social media and online platforms for relevant mentions, hashtags, or keywords. They can also analyze sentiment, similar to sentiment analysis, but with a broader focus on conversations around a topic rather than individual reviews or feedback.

Zara’s approach to understanding consumer needs and adapting its product offerings relies heavily on real-time customer feedback. The company uses data from social media and email surveys to gather insights directly from its customer base.

Key benefits of machine learning for eCommerce

Immediate access to how customers feel allows for promptly responding to emerging issues or capitalizing on positive trends.
Businesses can address pain points, improve products, and create more personalized marketing strategies.
Social listening helps businesses monitor public perception, quickly identifying potential PR crises or negative trends before they escalate.
Brands can use the feedback gathered to inform marketing, product development, and customer service strategies.

Chatbots for automated customer support

These chatbots rely on natural language processing (NLP) at their core, allowing them to understand and respond to user queries with accuracy.

For instance, if a customer asks, “When will my order #12345 arrive?” the chatbot identifies the intent (a question about delivery) and extracts the key information (the order number). It then fetches the relevant details from the database and provides a clear response, such as: “Your order is scheduled for delivery tomorrow.”

Key benefits of machine learning for eCommerce

Chatbots process and answer queries in seconds, providing timely and helpful answers.
Businesses can reduce the workload on support teams and cut operational costs with chatbots as they are always on and ready to assist customers anytime.
Chatbots are ideal for large-scale operations, as they simultaneously handle thousands of requests.
With every new interaction, chatbots learn and adapt, improving their accuracy and usefulness over time.

Machine learning in eCommerce: our clients’ success stories

Ad campaign analytics platform

Innowise developed an AI-powered analytics platform that uses ML to optimize ad campaigns by matching user queries with the most relevant ads, improving keyword coverage and ad relevance. The solution increased ad clicks by 53%, reduced ad generation time by 25%, and achieved 92% coverage of user requests.

Herramientas de AI para la creación de sitios web y contenidos

Our team integrated OpenAI’s GPT models into a no-code website-building platform, enabling AI-powered code generation and content creation. As a result, the solution reduced website customization time by 60%, improved SEO with optimized meta descriptions, and boosted search engine rankings by 17%.

AI-powered web app ecosystem

We transformed the digital media ecosystem of our client, integrating advanced AI solutions to modernize web apps, upgrade user experience, and optimize operational efficiency. Key achievements include a 12% increase in monthly visitors and a 66% reduction in professional photography costs through text-to-image generative AI.

ML can optimize, personalize, and scale your business — let’s find what’s best for you!

Roadblocks and limitations of machine learning in eCommerce

Sure, eCommerce ML is powering personalized shopping experiences, predicting customer needs, and driving business decisions with incredible precision. But behind all the excitement lies a series of challenges that often go unnoticed.

Let’s take a closer look at the hurdles that machine learning e-сommerce can face and what can be done to navigate these hurdles.

Desafío

Solución

ML models rely heavily on large volumes of high-quality data, but e-сommerce businesses often struggle with incomplete and inconsistent datasets.

Use data cleaning tools and techniques to preprocess data and eliminate errors before feeding it into ML models. Partnering with a third-party provider specializing in data enrichment can also fill gaps in datasets.

Incorporating ML into existing e-commerce systems, such as CRMs or ERPs, can be complex and time-consuming.

Choose ML platforms that offer APIs and connectors for easier integration with popular CRM, ERP, or marketing tools. Work with experienced integration specialists to get smooth alignment with existing systems.

Historical data used for training ML models may contain biases, such as underrepresenting specific customer segments or seasonal trends.

Regularly audit datasets and models to identify and mitigate bias. Use techniques like re-sampling, algorithmic fairness adjustments, and diverse data collection to reduce bias.

Developing, training, and deploying ML models requires substantial investment in technology, talent, and infrastructure.

Start small and gradually scale investments as you achieve measurable results. Outsourcing ML development to experienced partners can also lower initial costs while accessing in-depth expertise.

ML models crafted for limited-scale apps may lack the capacity to scale efficiently, especially with increasing data complexity.

Leverage modular architectures and cloud infrastructure to handle growing datasets and user bases. Use tools like AutoML or pre-trained models to reduce the complexity of scaling up.

Finalicemos

The initial cost of implementing machine learning algorithms for eCommerce might be high, but the efficiency and return on investment will undoubtedly make it valuable in the long run. Businesses can develop a competitive advantage, increase satisfaction rates, and earn more profit by integrating such tools. Our team would be delighted to help you use machine learning in eCommerce for your benefit.

Volha Ralko Gestor de entregas en comercio electrónico

Date: 14 de febrero de 2025

Volha Ralko Gestor de entregas en comercio electrónico

Date: 14 de febrero de 2025

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