This past winter we interviewed 75 tech professionals from the ecommerce sector. Roughly half (47%) admitted they struggled to adopt artificial intelligence (AI). Fifteen percent said they were skeptical of the value of AI.
With the excitement around ChatGPT and generative AI (GenAI), we have to wonder if those numbers will hold. We also thought this might be a good time to refresh how self-learning AI can help ecommerce.
What Is Self-Learning AI?
First, self-learning AI is not really AI — it’s machine learning. So, it’s a buzzy way of saying the machine is learning from your data.
This means the machine (your application) has the ability to learn and improve on its own — without being explicitly programmed. In the case of AI-powered search, the technology uses algorithms, data structures, machine learning models — and data — to teach itself from experience and past decisions. Through self-learning AI, machines can detect patterns within large datasets. This allows them to make more accurate predictions than humans are capable of — specifically at scale.
“The key is to have good data,” warns Vincent Bernard, director of research and development at Coveo. “Otherwise, garbage in — garbage out.”
Shoppers are already being impacted by these advanced machine learning models. From auto-filling the search box with relevant query suggestions to sophisticated personalized product recommendations, self-learning AI is used throughout ecommerce.
As online shopping experiences are made easier and more efficient with the help of AI, expectations will grow. In our Ecommerce Relevance Report of 4,000 working consumers, we found that 93% of shoppers overall expect ecommerce to be better than or equal to shopping in-store.
The only way to enhance experiences and personalize them across all channels is through machine learning.
So how is it used? Self-learning AI enhances a user’s shopping experience through a variety of methods. Many of which you’re used to seeing — and some of which you may not even be aware of. Let’s look at some of these enhancements.
Personalized Product Recommendations
First, AI provides highly personalized product recommendations. These offer opportunities for cross-selling and upselling at different points in the customer journey.
In real-time, AI analyzes elements like customer behavior, browsing history, purchase patterns, and similarities between products. This allows it to offer personalized recommendations like “we think you’ll like”, “customers also bought”, “complete the look” sections, and other popular recommendation categories you see on your favorite online store.
Site Search Optimization
As an AI system learns from user interactions, it helps improve on-site search. Based on past queries, product descriptions, and user data, site search results become more relevant over time. They can be personalized to each user, putting them one step closer to the products they are looking for or are more likely to purchase.
This helps actively push customers toward conversion by reducing friction in their experiences. Self-learning systems continually improve search results. As users query and interact with results, the system learns which results are most relevant to each type of customer.
Chatbots
In addition to improving the shopping experience, self-learning AI also supports pre- and post- transactions with things like chatbots. A chatbot with natural language understanding can act as a virtual private shopper. It can also resolve customers’ concerns — whether it’s providing order tracking, product information, or transaction support.
There is hope that Generative AI powering ecommerce chatbots will help reduce the workload put on customer service staff. The goal would be to improve response time for the customer with instant access to help.
Fraud Detection and Prevention
AI can help identify suspicious behavior and fraudulent patterns in real time to protect companies from financial or data loss. Self-learning systems have the capability to track fraudulent behavior and patterns, giving protection against ecommerce threats.
Price Optimization
AI can support a company’s financial goals and revenue stream by helping with pricing optimization and strategy. Machine learning algorithms can analyze market trends, competitor pricing, and customer behavior. This allows retailers to optimize pricing strategies and provide dynamic pricing strategies that can help companies keep up with competition.
Inventory Management
Artificial intelligence helps companies predict future product demand based on past purchasing data and current customer patterns. This is also known as Intelligent Merchandising. It helps companies avoid over or under-stocking products to support better revenue flow throughout each season or market change.
How AI Can Transform Shopping Experiences and Help Customer Success
We’ve looked at some key ways that AI shows up throughout a user’s experience on an ecommerce site. But how can it actually take their experience to the next level and promote customer success?
Because AI is commonly used in ecommerce today, if companies want to stand out from the rest, focusing on the right tools that deliver the best customer experience possible is key. It’s not just about improving the experience anymore but optimizing it at each customer touchpoint.
Search is always going to be one of the most important areas of ecommerce because it’s closely tied to purchasing intent. The search experience need to be intuitive and the search results ultra-relevant. Site search should also offer a personalized experience that requires analysis of each user’s user intent. By learning from their interactions, this should produce results that will boost the chance of a conversion and customer satisfaction.
Product Discovery
With the right AI tools, businesses can move beyond just supporting product find-ability for their customers. They can move towardmore effective product discovery. Product discovery helps customers find products they didn’t even know they wanted or that they existed. This delivers even more value and delight to their shopping experience.
By analyzing user data and comparing their data to other similar users, machine learning can make informed predictions about what kind of products customers will want. This provides a more dynamic experience that introduces them to new and exciting products. Like a friend showing you a new gadget that solves a problem you’ve had for years, product discovery elevates the customer experience to new levels, increases customer satisfaction, and encourages confidence and trust in a brand.
Product Comparison
Another often-forgotten customer touchpoint where a sophisticated AI algorithm can help an ecommerce site stand out is with product comparison. If there are similar products on your company’s site and you know customers will be choosing between them, offering AI-supported product comparison charts is a major win for customers, especially if they are personalized to each user’s own preferences and needs.
Other key customer touchpoints where AI can have a large impact on customer success are suggested product ad-ons, location-based recommendations (for example products based on current weather), targeted discounts, site navigation, cart optimization, customer service tickets, order tracking and shipping, and more.
What Is an Example of Artificial Intelligence in Ecommerce?
Let’s look at a specific example of how AI can support ecommerce through dynamic machine learning with intelligent merchandising. Using a self-learning system for AI merchandising can both improve the customer experience and support your business’s shifting needs.
When integrated with SAP, an AI search platform like Coveo can intelligently apply inventory rules that will boost certain products and bury others based on inventory levels. When certain products are running out fast, and others are stagnant, machine learning models can dynamically alter search results to reflect purchasing patterns. It can also help filter out high-return products and boost high-margin products to promote profitability and keep customers from purchasing items they are more likely to send right back.
Another dynamic feature of intelligent merchandising is A/B testing different sets of site search results to measure the overall search experience and optimize it over time. As the self learning system tracks which results lead to more conversions, it can create new rules for particular data sets at a speed that no human implementing a test could keep up with.
Intelligent merchandising also involves demand forecasting. AI algorithms analyze historical sales data, market trends, and various external factors to predict future demand for products. This can help companies optimize inventory levels and strategize for upcoming seasons based on real customer data and insights. By accurately forecasting demand, AI enables more efficient inventory management and reduces the risk of out-of-stock products or excess inventory that’s hard to move.
Ways to Integrate AI in Your Ecommerce Business
Anonymous Personalization & Cold-Start Product
A new-ish technique lets organizations with low volumes of customer data, variable shopping habits, anonymous, or first-time customers, deliver personalization. This means you can determine the intent of any given customer and serve up personalized products most likely to convert. This requires using product embeddings and vector search.
The way it works, as a person searches – and selects, subsequent searches are filtered based on where the original search was found in vector space. This allows you to determine shopper intention with a high degree of probability.
Similar techniques can be used to address cold-start products.
Omnichannel Personalizations
There are a lot of ways to integrate AI personalization into your ecommerce business, but the important thing is that personalizations are integrated across channels consistently.Personalizing your customer’s onsite experience is one thing, but what if you combined that with personalized marketing across other channels?
You can maximize your customer engagement strategies by meeting customers wherever they are – on your site, on their phone, or in their email through omni-channel personalization.
How does it work? AI can combine a user’s mobile app, onsite, and email experience to provide an even more personalized engagement strategy using unified customer profiles.
Collecting site data on customer preferences, browsing behavior, purchase history, and interactions across various channels such as a website, mobile app, social media, and physical stores allows you to create hyper-personalized experiences. And this includes personalization at any touchpoint —in emails, push notifications, site experiences, and even when they check out in a brick and mortar store. Across these different channels, you can personalize A/B testing, content, social proof, offers, promotions, cart abandonment recovery tactics, and more.
Omnichannel Personalization Examples
- Customized Offers and Promotions: You can tailor offers and promotions based on customer preferences, purchase history, and engagement levels and deliver personalized discounts, exclusive deals, and relevant incentives to customers through various channels.
This could be a birthday discount sent via email, a Mother’s Day Special in an SMS notification, a personalized discount code in a site banner, or mobile app push notifications geared toward loyal customers. There are endless ways to integrate personalizations in your marketing offers and self learning AI makes it easier than ever to execute.
- Dynamic Content and Messaging: Speak to customers in a way that will move them toward a purchase by delivering dynamic content and messaging that adapts to each customer’s preferences and behavior.
AI-driven content optimization tools can customize website banners, landing pages, and email content based on customer profiles and individual preferences and adjust messaging and visuals to align with the customer’s interests and past interactions. Even social proof can be personalized for different audiences to provide the most compelling proof based on a user’s interests, habits, and demographics.
However you choose to engage your audience across channels, the main goal is to provide consistent, relevant, and engaging experiences that enhance customer satisfaction and drive business growth.
Generative AI and Ecommerce
In its most modest form, self-learning AI has already revolutionized the shopping experience by providing numerous benefits and enhancements that make an online shopping experience even better than in-person experiences. Gone are the days of clunky search engines and hit-or-miss results. We’re in an era where ecommerce shopping is one of the most personalized and enjoyable online experiences users can have.
Of course, now we are looking at the heir apparent to machine learning: Deep-learning Generative AI. GenAI’s impact on ecommerce is only just in its infancy – holding enormous promises for even more impact on shoppers, staff, and workflows.
By leveraging self-learning and now deep-learning AI, businesses can offer a shopping experience that’s hyper-personalized at a level that leads to a more enjoyable, seamless, and effective shopping journey, increasing customer satisfaction and engagement.
With AI-powered chatbots transforming the shopping experience with on-demand customer service that provides instant support, dynamic algorithms that support business’ profitability and security, and user interaction continually informing self learning systems, the possibilities for improving online shopping both — pre and post transaction — are endless.
Personalized suggestions, instant access to support, and an automated customer journey make today’s online experiences a breeze, and as AI continues to rapidly improve, businesses will see more and more success come through AI strategies and self-learning tools.