Artificial Intelligence in eCommerce: Overview, Benefits, Challenges and Trends

Artificial Intelligence in eCommerce: Overview, Use Cases and Trends

Despite the growing impact of artificial intelligence (AI) on the eCommerce ecosystem, there is still little understanding of what eCommerce AI is and how it can be used in online sales and customer service.

This guide will explore the concept of AI-driven eCommerce business and provide an overview of its benefits, tools, and use cases. We will also talk about the possible trends that may arise as more online retailers use artificial intelligence technology.

Define AI in eCommerce

The introduction of AI in the eCommerce sector has completely transformed the online retail landscape by adding a level of automation and intelligence which has proven to be highly beneficial for online merchants and customers.

In eCommerce, artificial intelligence (AI) refers to a collection of techniques, tools, and software powered by machine learning and advanced analytics to create optimal online shopping experiences for customers through improved design, personalized recommendations, automation, and multi-channel customer support such as virtual assistants and intelligent chatbots. As a result, it helps maximize revenue from every visitor session and satisfy customer needs.

eCommerce artificial intelligence is typically categorized into consumer-facing AI and business-facing AI.

  • Consumer-facing AI. This category of AI-driven electronic commerce focuses on how brands and retailers can deliver the most relevant and satisfying digital shopping experience for their customers’ needs. It is about finding ways to make an online store more intelligent, interactive, personalized, and convenient.
  • Business-facing AI. As the name suggests, this eCommerce AI category is about making online stores smarter by automating many mundane tasks, increasing conversion rates and operational efficiency, reducing operating costs, and enhancing business growth.

Next, let’s talk about how AI eCommerce has evolved over the past decade.

The Evolution of AI in eCommerce

Artificial intelligence and eCommerce are two of the biggest trends in the digital world, with many businesses and consumers beginning to use intelligent tools to help run their operations. As AI technology continues to combine and grow, retailers must be able to cater to these changes to keep up and capitalize on this trend that could change their business model entirely.

As you may already know, AI applications in eCommerce started in the early 2000s. This was when online retailers began to embrace customer analytics and machine learning (ML) to improve the efficiency of their websites.

Early AI systems for online shopping mainly presented consumers with suggestions for products and offers based on previous purchases and searched items. It also allowed merchants to automate selling by providing buyers with product information.

For instance, in the early 2000s, several online retailers and shopping directories provided detailed product information. It made it easy for their shoppers to compare prices with competitors, find the products they wanted, see user comments and ratings, and view various other product information related to needed categories. Examples include ASOS, eBay, Safeway, Steam, and Zappos.

However, the technology was relatively primitive, and its performance was inadequate. AI systems could not effectively integrate into the natural online shopping flow and were quickly exposed as “fake reviews” by experienced buyers.

Retail e-commerce sales worldwide from 2014 to 2024 (in billion U.S. dollars)

Retail e-commerce sales worldwide from 2014 to 2024 by (in billion U.S. dollars)

Flash forward to the 2010s, and eCommerce AI is looking much rosier. Technology is becoming more advanced, more powerful, and more intelligent. Amazon, Google, Shopify, Alibaba, Netflix, eBay, and other major eCommerce companies and platforms have been working hard to extend their AI capabilities to meet customer demands and improve user experiences.

The main reason for the progress is enormous advances in deep learning algorithms and their application. Add to this the advent of big data and ever-growing online retailers’ datasets – it turns out that today we have enough data to build an eCommerce AI system that can make a difference.

The Benefits of AI in eCommerce

  1. Customer-centric engagement. By leveraging AI technologies, eCommerce store owners can personalize the shopping experience for their customers by delivering the most relevant products and information, leading to higher average order value (AOV), lifetime customer value (LTV), and overall customer engagement over time.
  2. New customer acquisition. Online retailers and eCommerce platforms can use machine learning and predictive analytics to target customers with video ads, chatbots, and personalized recommendations. These AI technologies help them retain their existing customers and attract new buyers, which in turn boosts profitability in the long term.
  3. Task automation. AI-based technologies act as an eCommerce automation suite, enabling automated tasks and processes such as sending emails, order fulfillment, and payment processing, reducing labor costs, and improving operational efficiency.
  4. Fraud detection. Through real-time transaction monitoring, machine learning can detect fraudulent activities such as chargebacks, money laundering, and credit card number thefts to help merchants minimize financial losses. Besides, AI-based fraud detection also helps reduce customer service costs by automatically resolving disputes and refunds.

AI Technologies Transforming Online Shopping

The Challenges of eCommerce AI

  1. Scalability. Given the unprecedented size and velocity of data generated by online retailers today, ensuring that AI technologies can respond quickly to a natural language request is a challenge. It doesn’t help that even technical staffs aren’t always trained to operate new AI-powered eсommerce systems.
  2. Cybersecurity. Malicious programs can also find it easier to target eCommerce stores that rely heavily on AI tools and technologies. Accordingly, eCommerce businesses will need to implement a robust cybersecurity program that addresses emerging AI threats, as well as work with reputable providers of AI technology.
  3. Human error. While AI technologies can be a godsend in eCommerce, they are not infallible. For example, just like in the case of the Facebook data breach crisis, AI-driven eCommerce platforms can fail to provide best-in-class customer service.
  4. Privacy concerns. With all their customer information, online retailers are exposed to privacy and security concerns when dealing with AI-driven systems. Since the technologies don’t allow humans to control and influence buying decisions fully, it eliminates error and human bias. It creates equal chances for experts and novices to deal with these systems.

AI Technologies Transforming Online Shopping

Here are some of the leading AI technologies that have transformed the eCommerce ecosystem:

  • Machine learning. ML is the critical technology behind many eCommerce AI features, such as automated decision-making and product recommendations. ML technologies utilize machine learning-based systems to improve performance over time, detect behavioral patterns, learn from customer interactions, and make predictions.
  • Natural language processing. NLP has been a critical factor in enabling voice and visual search capabilities to become a standard practice for online shoppers instead of typing keywords into a search bar. It also allows intelligent chatbots to master the art of conversation with human beings.
  • Sentiment analysis. AI-enabled SaaS platforms efficiently analyze user sentiment and what it means for the retail industry. For example, Amazon’s Alexa provides a wealth of information about products, such as pricing, shipping, and purchase history. This information is always up-to-date and available to customers.
  • Deep learning is an advanced technology that involves the development of large data sets that can be used to train sophisticated machine learning systems to make predictions based on large data sets.
  • Face recognition. This highly advanced AI technology can recognize individuals by facial features and match their images across different media platforms such as websites or social networks. Facial recognition can transform the eCommerce industry because face recognition is applied to facial scans of potential customers to streamline point-of-sale transactions.
  • Autonomous delivery systems. Delivery vehicles and robots like Amazon Scout, Robby, TeleRetail Pulse 1, Kiwibot, Nuro R2, and many others allow online retailers to deliver goods directly to consumers anywhere and anytime. This technology reduces costs for last-mile deliveries and minimizes shopping times for customers.
  • Predictive analytics. This AI technology improves consumer-facing products, such as automated email marketing campaigns, recommendations, and product rankings. Predictive analytics and machine learning help solve big data problems by developing processes to identify consumer needs and predict consumer behavior based on intent data.

Although AI-driven eCommerce is in its infancy, it has already proven to be a valuable asset for online retailers and eCommerce providers like Amazon and Shopify. That is because AI technologies are highly effective in performing repetitive tasks and identifying patterns in consumer behavior — two critical elements that lie at the heart of eCommerce artificial intelligence (i.e., searching for information, making payments, and providing customer support).

Amazon Go Ecommerce Artificial Intelligence

Examples of AI in eCommerce

Today, online retailers are no longer competing on price and convenience but have also begun to offer innovative products or services.

For example, in 2018, Amazon opened to the public the first cashier-less store called Amazon Go, which uses AI-powered cameras and sensors to track customers walking into the store. The shoppers pick up what they need, put it in their carts and leave without using the checkout counter since their payment details are already stored in the system as Amazon Prime accounts.

Besides, Amazon is using AI-driven technology to help online shoppers find products that suit their needs – something not possible when shopping in physical stores. It also uses AI to manage its warehouses, allowing them to track inventory automatically and make product recommendations based on customer buying behavior.

AI Tools for eCommerce

Through AI, merchants can utilize advanced machine learning techniques to help predict buying patterns, bring the right items to their customers at the correct times, and minimize risk.

Here are some of the AI tools for eCommerce:

CRM systems. Using deep learning to analyze customer data and learn from past purchases to improve targeting. Some of the essential functionalities of AI-powered eCommerce CRM include:

  • Predictive lead scoring
  • Sales forecasts
  • Data entry automation
  • Automated email sequences
  • Sales recommendations and insights

HubSpot, Salesforce, SugarCRM, Microsoft Dynamics 365, and Zoho are top CRM systems with AI-enabled eCommerce modules.

Chatbots. Chatbots are virtual agents programmed to understand human speech and respond accordingly. They can process human voice commands and text messages to interact with customers via messaging apps like Facebook Messenger, Viber, etc. Chatbots can also capture feedback, personalize the shopping experience, and process payment requests.

Through messaging features powered by AI, chatbots can interact with customers in real-time to help them through the checkout process. For example, mobile banking chatbots like Citi Bank’s Citi Bot and Bank of America’s Erica apply machine learning to predict customer behavior, highlight the right products, and apply the appropriate payment method to their account.

Intelligent search engines. Using AI to provide intelligent search results that can answer user questions, understand natural language, and filter relevant content. Google, OpenAI’s GPTChat, and Microsoft’s Bing are some of the widely used intelligent search engines and chat systems that can help customers easily find the best products and services.

Top eCommerce AI Predictions for 2023

More AI-enabled eCommerce services will be launched

With more eCommerce platforms looking to incorporate artificial intelligence into their websites and apps, the number of AI-driven services offered to online shoppers is expected to rise.

In 2023, retailers will increasingly look to adopt machine learning, natural language processing, and predictive analytics to make better predictions about their customer’s preferences and behavior, create personalized experiences for shoppers, increase productivity and drive profitability. Tech giants like Google, Facebook, IBM, and Amazon are expected to improve their AI-enabled eCommerce services to make shopping a more seamless experience for customers.

In 2023, the number of eCommerce AI services is expected to be as many as four times higher than in 2019, according to Statista.

More online shoppers will be using voice assistants

By 2024, more than 50% of online shoppers will use voice-enabled shopping assistants to make purchases. This will be partly driven by the fact that popular eCommerce platforms such as Amazon and eBay already offer voice shopping capabilities.

 More online shoppers will be using voice assistants

Photo by James Bareham / The Verge

Conversational commerce will transform the online shopping experience

In 2020, online consumers spent 61% of their mobile commerce time on messaging apps. The rise of conversational marketing has ushered in a completely new paradigm for eCommerce platforms, allowing them to seamlessly integrate their offerings with popular messaging apps like WhatsApp, Facebook Messenger, and Slack.

In 2023, eCommerce platforms will be expected to create and integrate chatbots and other AI-driven conversational systems into their apps and websites to help customers navigate the shopping experience more quickly.

Besides, there has been a recent shift in eCommerce, with companies placing more importance on conversations with customers instead of broadcasting ads. A report by Deloitte indicates that only 33% of retailers use chatbots for customer service and engagement. However, this figure is expected to rise to 47% in 2023.

Machine learning and NLP will be integrated into many eCommerce services.

This trend will be especially evident in two areas — personalization and analytics.

In 2023, eCommerce platforms will be expected to use machine learning and natural language processing technologies to analyze customer data, understand their buying preferences, and recommend items based on past purchase history. This will allow them to target marketing campaigns more effectively, create personalized shopping experiences, and increase sales.

Businesses will use AI to reduce costs and increase productivity

In the face of stiff competition from eCommerce giants, especially Amazon, companies are looking for ways to compete in terms of cost and product range by turning to AI technology.

In 2023, many small and medium enterprises will use AI and automation to improve the efficiency of their operations. This could be achieved through natural language processing, image recognition, or online monitoring tools that capture large amounts of collected data without compromising privacy. The technology will perform regular checks on their eCommerce websites, spot malicious files, and alert businesses of potential issues.

eCommerce AI will help predict customer behavior and make better decisions

eCommerce AI will help predict customer behavior and make better decisions

Analytics systems will increasingly be used for making decisions in pricing, promotions, and inventory planning based on data related to external market conditions and individual customer behavior patterns.

Intelligent algorithms can replace humans in merchandising, inventory management, promotions and pricing, and optimization.

More eCommerce marketplaces will start using machine learning and big data to boost their online operations by conducting predictive analysis for improved marketing, customer experience, and profitability.