Once found only in sci-fi stories, artificial intelligence (AI) technology is now a part of our daily lives. We use it to write emails, check the weather, and even diagnose diseases. AI is especially impactful in ecommerce.
In the past, businesses viewed advanced technologies like AI as “nice to have,” primarily using them to delight customers and encourage repeat visits. Now, ecommerce businesses rely on AI to grow their company, maintain global operations, and meet customer demands across multiple channels.
Ecommerce businesses have implemented AI across various business functions and will continue to find new and innovative applications for AI in ecommerce as adoption increases and the technology advances. Here’s what you need to know.
Types of AI technology used in ecommerce
AI is not a singular technology; it encompasses various models. There are four leading AI technologies used in ecommerce:
- Natural language processing (NLP): Natural language processing focuses on enabling computers to interpret and generate natural human language.
- Machine learning (ML): Machine learning uses statistical techniques, including algorithms, to enable computers to learn from data and make predictions or decisions without being explicitly programmed. Deep learning models—such as transformers and large language models (LLMs) like OpenAi’s ChatGPT—layer algorithms to understand data better.
- Computer vision (CV): Computer vision is a field of artificial intelligence that enables computers to interpret information from images and videos.
- Data mining: Data mining is the process of discovering data to inform AI algorithms and systems.
7 applications of AI in ecommerce
- Personalized product recommendations
- Chatbots and virtual assistants
- Fraud detection and prevention
- Inventory management
- Dynamic pricing
- Customer churn prediction
- Generative AI
From helping customers find the right products to price matching, you can apply AI across all your ecommerce business operations and processes. Here are the seven main use cases:
1. Personalized product recommendations
Personalized product recommendations use data from past customer behavior, browsing history, and purchase history to suggest products.
For example, NLP-based AI can understand online shoppers’ language and images to match them with desired products. AI-powered features like “People also purchased” or “Customers also viewed” can suggest complementary products based on size, color, shape, fabric, and brand.
2. Chatbots and virtual assistants
Chatbots and virtual assistants can act as customer service representatives for your ecommerce business, helping field customer queries and facilitating online shopping by providing tips. They use AI, NLP, and, most recently, generative AI to understand and respond to customer requests.
You can use chatbots and virtual assistants to:
- Make efficient customer interactions. Chatbots and virtual assistants can handle simple transactions, process orders, and provide personalized offers to customers, making it easier to field a large volume of requests across various point-of-sales (POS) channels—from a physical store, online, or through a mobile app.
- Collect customer data. Chatbots and virtual assistants can collect customer data, such as sizing and the reasons for inquiry, which can help inform product development and improve customer service.
- Enhance checkout. Online businesses can also integrate a chatbot into the checkout page so customers can easily ask about product details, quantities for highly sought-after items, and shipping information, without leaving their cart.
- Provide 24/7 customer service. Chatbots and virtual assistants can provide prompt responses 24/7, allowing your live support agents to address more complex customer service issues. AI can help you reduce customer service costs by automatically resolving disputes and processing refunds.
3. Fraud detection and prevention
AI can assist in fraud detection and prevention by analyzing data, detecting anomalies, and monitoring transactions in real time. The technology can spot unusual transactions, such as high-value transfers, multiple transactions within a short time frame or from unfamiliar locations, and flag them for further investigation.
You can also use machine learning models to generate user profiles based on behavior data like browsing habits, transaction history, and device history, then compare current consumer behaviors with historical data to identify fraudulent behavior. For example, if a user suddenly makes a large purchase from an unfamiliar location, the machine learning model can flag it for fraud if it doesn’t align with their data profile.
4. Inventory management
AI can help you manage inventory by analyzing historical sales data and predicting future demand. For example, real-time data through sensors and RFID tags—wireless identification technology using radiofrequency—can give you a sense of what products are selling, where they’re going, and whether they’re coming from a physical store or distribution center.
AI-enabled inventory management can automate the inventory replenishment processes by integrating with suppliers to ensure timely restocking. You can also use AI to forecast transit times and shipment delays and communicate these updates with stakeholders, including customers.
5. Dynamic pricing
Dynamic pricing allows you to adjust your prices and offerings based on real-time user behavior, global supply and demand, and competitors. With the power of AI, you can anticipate optimal discounting opportunities and dynamically determine the minimum discount required to drive a successful sale.
AI gives multichannel retailers more flexibility in price structuring. By leveraging AI, retailers can vary prices across different POS channels depending on observed demand. For instance, if you sell products on your website and Amazon, you can intelligently discount your items on Amazon when there is a significant influx of purchasing activity from this particular channel.
AI also facilitates assortment intelligence—data-driven optimization of product variety and selection. Assortment intelligence provides insights into your products and competitors, making adjusting your selection and pricing easier. You can also use AI to price-match your competitors to ensure your customers always get the best deal.
6. Customer churn prediction
AI allows ecommerce businesses to understand customers better and identify new trends. It can analyze customer engagements across POS channels and offer insights for optimization as more consumer data becomes available.
Machine learning can help your business identify and reduce customer churn by predicting when customers might be on the verge of leaving your platform. First, AI can pull data on customer churn indicators such as abandoned carts, browse abandonment, or website bounce rate. Then, you can automate purchase completion emails, loyalty discounts, and follow-up abandoned cart inquiries, making it easier to encourage customers to complete the purchase process.
7. Generative AI
Generative AI is an artificial intelligence system that generates text, images, or other media based on prompts. Popular generative tools include ChatGPT and DALL-E.
Ecommerce businesses are using generative AI to scale the production of their marketing collateral and tailor it to different audiences. For example, a copywriter can write a marketing email and run it through a generative AI tool to customize it for various customer segments. Marketers can also prompt generative AI to give feedback on their brand messaging and positioning to ensure it aligns with targeted customer personas.
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Benefits of using AI in ecommerce
AI offers several benefits to ecommerce businesses:
- Increased sales. AI can help you create a more efficient sales process by gathering and analyzing customer data to personalize your sales funnel. With more data, you can engage with the right prospects with the right message at the right time. French delivery service Chronopost saw an 85% increase in sales revenue after using AI-driven campaigns during its 2022 holiday season.
- Better and more personalized customer service. AI can analyze customer feedback and big data from multiple touchpoints to measure customer interactions. Ecommerce websites can use this data to deliver a seamless omnichannel customer experience. Collecting customer data helps you identify shoppers preferences so you can create custom offers that encourage them to make a purchase. Brands like Ruti have implemented virtual sales associates, leading to an increase in conversion rate and average order value.
- Reallocation of time and resources. AI can help you automate tasks and processes like emailing, order fulfillment, customer service, and payment processing. Automations help you reduce labor costs and improve operational efficiency so you can spend less time on maintenance and more time innovating. AI-powered forecasting in supply chain management can reduce errors by up to 50%, lessening lost sales and product unavailability by up to 65%.
Challenges of using AI in ecommerce
While AI has several benefits in ecommerce, it can also present challenges:
- Data privacy. AI algorithms rely on consumer data to make personalized recommendations and predictions. Collecting this data raises concerns about privacy and data protection. AI also presents a security risk for companies around proprietary intellectual property, leading to accidental leaks and breaches.
- High initial investment. Implementing AI can be costly. It involves an investment in infrastructure, talent, and maintenance. Additionally, AI solutions might not always yield a positive return on investment (ROI).
- Potential for poor-quality customer service. Because AI customer service relies on chatbots, you may fail to offer the same support and empathy as a human customer service representative. Done poorly, AI customer service can cause friction, customer dissatisfaction, and a poor reputation.
AI in ecommerce FAQ
How is AI changing the ecommerce industry?
AI tools offer retail businesses the analytical insight and data they need to understand their customers, make more informed business decisions, deliver a better customer experience, and optimize their operations. As a result, artificial intelligence in ecommerce helps stores maximize their offerings, boost conversion rates, and increase sales.
How is machine learning used in ecommerce?
Retailers rely on machine learning algorithms to capture, analyze, and use data to deliver a personalized shopping experience, optimize pricing, and generate customer insights. Ecommerce businesses also use machine learning to manage supply and demand, predict churn, detect fraud, and power chatbots and dynamic pricing. They can also use it to set up automations to streamline operations.
How is AI being used in ecommerce marketing?
AI is used in ecommerce marketing to help ecommerce businesses understand their customers and identify new purchasing behaviors and trends. It also allows companies to create more targeted ads, marketing campaigns, and offers. Marketers use generative AI tools to scale their content production and ensure their messaging aligns with the company’s target audience. Lastly, marketers use AI to retarget potential customers with an omnichannel strategy to encourage them to purchase goods and services.