Leveraging AI & Machine Learning in Ecommerce & Retail

1. Enhancing Customer Experience

There are three main ways that AI and ML can help enhance customer experience—and many retailers are already taking advantage of them. They are:

• Personalization: AI can personalize online shopping experiences by analyzing customer data like browsing history and purchasing habits. AI-enabled tools like Monetate’s Personalization Platform use this information to tailor recommendations to customers in a way that scales. Basically, AI allows retailers to provide one-to-one personalization to thousands or tens of thousands of customers. Since people tend to equate personalized offers and content with good shopping experiences, this translates to happier, more loyal customers.

• Product recommendations: Machine learning (ML), a subset of AI, makes product recommendations more personalized, relevant, and timely. ML uses advanced algorithms to analyze historical data, third-party insights, and real-time visitor behavior. The more context the system has about a customer, the more precise the recommendations become. Monetate customer Helly Hansen, a Norwegian-based retailer with 19 stores worldwide, uses ML to adapt messaging across the changing seasons and regions. Helly Hansen’s website highlights products based on past browsing behavior and refines product recommendations based on user demographics. This hyper personalized approach received 50% more clicks than their old (less personalized) approach.

• Customer service: AI-powered chatbots and personal assistants are trained to handle customer queries using natural language processing (NLP) and ML. They give retailers an automated way to provide very human-sounding answers to customer questions. Intelligent bots are always-on, offering 24/7 support across any time zone or location, and many bots are multilingual. Additionally, AI-driven chatbots keep a history of the customer’s interaction with your brand, providing more context into customer issues and trends.

2. Improving eCommerce Security 

AI and ML can spot anomalies and suspicious behavior without relying on pre-established rules, which makes them excellent tools for fraud detection and prevention. In eCommerce, advanced ML techniques use unsupervised learning models combined with large amounts of data to analyze transactions and detect anomalies. 

Since the system is looking for patterns versus following established rules, it adds flexibility to fraud detection. This flexibility allows AI-enabled security tools to make decisions in real time. It’s also why AI excels at detecting fraud and avoiding “false positives” which is when a customer’s activity is flagged as fraud when it’s a legitimate transaction.

3. Optimizing Pricing and Inventory Management

ML helps retailers take advantage of dynamic pricing by considering hundreds of factors across thousands of SKUs. AI can calculate the potential impact of price changes on sales and find the optimal price based on predetermined requirements. 

Dynamic pricing can also help retailers manage inventory, for example, by adjusting prices based on fluctuations in demand (e.g., a spike in demand for air conditioners during a heat wave or wood-burning stoves during a cold spell). This approach helps maintain a balanced inventory, while optimizing sales.

4. Streamlining the Retail Supply Chain

AI and ML are helping retailers improve supply chain logistics in a few different ways. Supply chain optimization platforms analyze large volumes of data like traffic patterns, current weather conditions, and inventory location to plan optimal shipping and delivery routes. 

ML algorithms automatically adjust scheduling and resource allocation based on this real-time data, making the entire delivery process more efficient. The big headline here is automation—tools that can adjust to real-time events on their own, without the need for human intervention, help companies minimize delays from random events like storms, traffic jams, and other snafus.

Big-name retailers like Walmart, Amazon, and Zara also use ML to glimpse into the future. Predictive analytics, a form of machine learning, gleans patterns from past data to help companies forecast product demand. This enables them to optimize inventory levels and minimize stockouts.

5. Powering eCommerce Marketing Strategies

It may sound cliché, but it’s true—AI is revolutionizing the way retailers plan, execute, and optimize how they market their goods to consumers. By analyzing customer data, AI algorithms can create personalized promotions, boost engagement through custom offers and content, and automate important follow-up tasks like sending shopping cart reminders. 

AI also helps retailers fully leverage email by sending personalized offers and discounts to customers and subscribers. Personalization is the magic ingredient when it comes to driving retail sales with email. According to Mailjet’s Inbox Insights 2022 report, nearly 60% of successful email marketers cite personalization as one of the best ways to increase engagement.

To this end, predictive ML algorithms can be used to analyze customer data and create more effective (and targeted) email campaigns.

How Get Started with AI in Ecommerce

Monetate’s AI-Powered Personalization Platform is a great way for retailers to dip a toe (or an entire leg) into AI and ML for eCommerce. Our platform uses AI to personalize customer experiences and provides you with data-backed information to create optimized eCommerce experiences. 

Our top-rated eCommerce personalization platform is already built on AI and machine learning algorithms that analyze customer data and behavior to deliver personalized experiences. Platform features include automated personalization, segmentation and targeting, audience discovery, and dynamic testing (to name a few). 

One example of how personalization improved website performance (in this case, clickthrough rates), is Monetate’s Individual Fit Experience, a pilot program launched by a luxury retailer. For this test, the retailer took a static home page and created 4 variants for different customer segments. 

When they compared the personalized home pages to the static (not personalized) home page, they found that the personalized home page drove a cumulative +26.42% lift in click-through from the static homepage after 14 days.