Online shopping is designed to feel easy, compressing the time from search to purchase into a series of lightning-fast clicks.
And although you might not think about it, there are sophisticated algorithms behind those clicks: Many of the most successful online stores integrate machine learning into features such as targeted marketing, intuitive customer service, inventory management, and shipping logistics.
A new era of machine learning in e-commerce has arrived: here's everything you need to know about it.
What is Machine Learning?
Machine learning is a type of artificial intelligence (AI) that trains computers to learn from data, identify patterns, and make decisions without the need for additional programming. Machine learning algorithms use historical data and past behavior to refine their output.
How machine learning is used in e-commerce
- Price Optimization
- prediction
- Inventory control
- Customer Experience
- Product Recommendations
- Site Search
- Customer churn
- Fraud detection and prevention
- Marketing campaigns
- Delivery Route
There are many business benefits to implementing machine learning into your e-commerce strategy. Whether you're using the technology to better understand your customers and enhance personalization or to optimize operational processes, machine learning can transform vast amounts of data into actionable insights.
Here's how leading e-commerce platforms and SMEs are implementing machine learning solutions.
1. Price optimization
Online businesses that employ dynamic pricing (also known as surge pricing or time-based pricing) use machine learning systems to make real-time price adjustments depending on customer behavior, competitor offerings, and inventory supply.
For example, a jewelry e-commerce business could use machine learning to test different pricing strategies for a line of trending earrings. The software would learn from sales and refine the pricing model to maximize revenue. So when a trend for earrings peaks, the price would go up, and when the trend fades, the earrings would go on sale accordingly.
2. Prediction
The power of predictive analytics gives machine learning models an edge when applied to forecasting: These models excel at finding complex patterns in large amounts of data, allowing them to predict outcomes more accurately than traditional methods.
For example, say you want to more accurately predict your company's revenue trends. First, you feed all of your existing sales and historical revenue data into a machine learning algorithm. After processing the data and identifying errors, the algorithm creates a model based on the patterns it finds. The model then applies external factors, like market trends and supply chain data, to predict the most likely outcomes. Over time, the model refines its approach based on how close its predictions are to actual outcomes.
3. Inventory Management
Managing inventory effectively requires predicting buyer trends and managing warehouse space. Machine learning can leverage large amounts of customer data to inform inventory management decisions, predict trends, and minimize supply chain disruptions.
Many businesses already use some sort of inventory management software, but when it comes to applying machine learning to your strategy, scope and granularity are key. For example, if you're expanding into global markets, these models can better handle increasingly complex data, from forecasting customer demand across diverse demographics to meeting worldwide shipping requirements.
4. Customer Experience
Using chatbots and virtual assistants to streamline customer interactions is one of the most popular ways to integrate machine learning into customer experience. While few can replace the attention of a human customer service representative, chatbots equipped with natural language processing can learn from each interaction and respond with more complex context, resulting in greater customer satisfaction.
Machine learning tools can even help human agents by summarizing vast amounts of customer feedback, reviews, and interactions. For example, by identifying recurring complaints about locking waterproof phone cases, machine learning can give customer service agents clues about the problem and set them up for success.
5. Product Recommendations
Machine learning-powered recommendation engines can be a significant revenue generator for e-commerce platforms and online stores. By using content-based filtering to analyze customer purchase history and collaborative filtering to provide recommendations based on the purchase history and preferences of buyers with similar behavioral patterns, these systems can upsell customers more effectively and increase average order value.
Shopify's Merlin platform seamlessly enables you to upsell customers and improve their overall shopping experience with targeted product recommendations and more.
6. Site Search
A standard site search is hit or miss. Unless a customer types in the exact words or phrases that are in your product description, there's no way they'll see what they're looking for in the search results.
Powered by machine learning, smart search provides a broader understanding of keywords and phrases, making it more likely that customers will be found and convert even when they can't articulate what they want. Some retailers are also incorporating visual search — a site's ability to respond to an uploaded photo of a desired product with similar products — to reflect the changes in how people discover and search for products online.
7. Customer churn
Customer churn rate is the percentage of customers who stop using a product or service within a given period of time, and ideally, this rate should be as close to zero as possible. Acquiring new customers is expensive, but by studying customer data and user behavior, you can learn where most users drop off from their shopping experience and how to fix that point.
Incorporating machine learning into your customer retention strategy can help you understand when existing customers need an extra incentive to stay. For example, say you find that a certain percentage of customers don't return to your e-commerce store after interacting with customer service. This insight can help you identify opportunities to improve your customer service experience.
8. Fraud detection and prevention
Most e-commerce fraud can be spotted by a break in a pattern, such as a sudden increase in the number of transactions on a single credit card. Many e-commerce companies use machine learning to spot anomalous breaks in payment data to improve the security of customer transactions.
9. Marketing Campaigns
Machine learning algorithms are highly customizable, making them ideal for tasks such as customer segmentation based on demographic data, purchase history, search engine history, etc. Machine learning enables granular targeting, increasing the likelihood of increased customer engagement and conversions.
You can use this nuanced understanding of your target audience to influence your marketing messaging and customize your content to get the most out of your marketing dollars.
10. Delivery Route
Machine learning solutions built for order fulfillment and product delivery can help online retailers plan more efficient delivery routes by analyzing influencing factors such as traffic patterns, driver performance, and even weather.
FAQs about Machine Learning in E-commerce
What is the difference between machine learning and AI?
Machine learning is a subfield of artificial intelligence. Machine learning models are trained using natural language processing (NLP) and reinforcement learning to continuously learn and adapt from both historical data and new inputs, improving with each new interaction.
Can machine learning and AI be used together?
Yes, as machine learning is a subset of the field of artificial intelligence. Depending on your business needs, you may also employ non-machine learning AI-powered tools to streamline repetitive tasks like transcribing meetings or summarizing documents in tandem with more dynamic machine learning solutions.
Does Shopify offer machine learning?
Yes. In 2022, Shopify launched Merlin, a machine learning platform built on Ray, an open-source framework. Merlin enables ecommerce companies to embed machine learning into a variety of applications across their internal and external business strategies. Internally, this includes revenue forecasting and fraud prevention, while external use cases include product categorization and recommendation engines.
How can machine learning improve e-commerce sales?
Machine learning solutions for e-commerce can increase sales by optimizing product recommendations based on past purchasing behavior and predictive analytics. Machine learning can also improve customer experience by creating personalized marketing and shopping experiences and optimizing order fulfillment, increasing repeat business and referrals.
