In today's ecosystem of ecommerce, it's important to note that competition is not just about the lowest price and the widest assortment of products. Competition is now a perceived delivery of relevant experiences, where consumers expect brands to have knowledge of their consumer needs, preferences, and habits, in delivering valuable, timely, and personalized engagement.
These expectations have pushed ecommerce companies to look at ways to engage in different manners that are not replicating the previous marketing playbook.
Artificial Intelligence has enabled this type of engagement, and it is no longer just the realm of large tech companies or complex enterprise platforms, it is more extensible and available to different types of businesses.Today, custom AI development makes this technology extensible and accessible to businesses of all sizes, including growing ecommerce brands.
Artificial Intelligence penetrates many different forms of digital touchpoint from emails, product recommendations, search engines, customer service bots, pricing tools, just to mention a few.
What allows AI to be so powerful in the ecommerce space is the ability to collect and process large amounts of user data based on digital behavior in real time, which allows it to identify patterns so that they can deliver basic relevant content or actions.
Instead of predicting the verbose, conflicting desires of each customer, ecommerce platforms are quickly trending toward a point where relevant recommendations, optimized messaging, and anticipating needs are some the customer has already asked for even before they need to ask.
This article will explore how ecommerce brands are leveraging some of the methods of AI to personalize every segment of their consumer journey, to be relevant, and to be, ultimately, effective.
The Growing Role of AI in Ecommerce
AI in ecommerce has rapidly shifted from a "nice to have" to a vital aspect of any digital strategy. Where AI was mostly focused on automation and efficiencies, today, AI applications that are more related to managing humans' time, and dealing with those humans, consider not only value, but relevance, personalization, and customer satisfaction.
Brands of all sizes are rapidly realizing the benefits of employing AI, if for no other reason than to get to know their customers and to better satisfy their needs.
Every single interaction a user performs online is a data point, based on clicks, searches, purchases, or simply time spent on a page. What all these interactions provide is useful intelligence, but because each source of data is so dynamic, it is almost impossible to review manually.
Where every point of data is useful, e-commerce SaaS can analyze huge amounts of data through machine learning, advanced analytics, and predictive analytics in real time at almost the speed of a click. It can also look for patterns in data that can be invisible to human marketers.
For e-commerce companies, one of the most exciting things about AI is the ability to provide a consistent, contextual, and personalized experience across all digital touchpoints. AI provides brands with the ability to predict what a customer is going to need next.
It allows a brand to respond with relevant recommendations virtually in real time. It enables a brand to more effectively organize and streamline operations behind the scenes.
Most importantly, AI offers the potential for brands to transition from reactive to proactive engagement and dramatically bolt on personalization with a new level of dynamic, context-aware, and timely engagement.
Personalization Begins with Data
For any AI tool to provide a personalized experience, it must be first fed data. E-commerce websites identify data from almost all interactions with the website product viewing, adding things to the cart, purchasing, returning, and even where the user moves the cursor.
AI algorithms use this data to find patterns and preferences. If a customer often browses athletic shoes, they may be categorized into indicators of the fitness enthusiast group.
This information allows the AI system to personalize product and content recommendations and email marketing that is aligned with the user's interests.
Personalized Product Recommendations
One of the most prominent examples of AI in ecommerce is product recommendations. Amazon has used this option for some time now.
When a customer is on the website product page of an item, the AI engine will recommend related items based on that customer's browsing history, purchase history, and the actions of look-alike users.
Brands of any size now have access to capabilities like this. Platforms such as Shopify, BigCommerce, and Magento now have AI based plugins or integrations (ex: Shopify Magic) that enable them to automatically use AI to manage and enhance their recommendation engines.
Dynamic Pricing Strategies
Furthermore, AI can facilitate dynamic pricing models. Rather than only being set in a predetermined pricing structure, ecommerce brands can now pivot their pricing based on real-time demand, inventory, competitor prices, and the behavior of customers.
So, businesses can maximize margins and remain competitive, as AI tools can not only recognize the likelihood of a product being sold for a higher or lower price, but they can even automate the pricing decisions without human intervention.
Smarter Search and Navigation
Search is an essential component of the ecommerce experience. Search query customers are more likely to purchase than browse customers.
AI enabled search engines provide a more realistic representation of the real world by allowing natural language searches, correcting misspellings, and offering predictive suggestions.
AI can also reorganize search results based on behaviors. For example, if a customer constantly orders eco-friendly products, the search results can be organized around this.
Email and Communication Automation
Email marketing has come much further down the path of personalization and segmentation. Instead of sending the same newsletter to all the subscribers, now e-commerce brands can use AI to segment audiences and send the same message, to different targeted recipients.
Automated workflows are typically triggered with cart abandonment, product viewing, and the anniversary of purchases.
AI even has been able to help determine the best time to send those emails and also suggested subject lines based on previous behavior that are likely to be opened.
Personalized Content and Copy
The content displayed on websites, apps, and social media is becoming increasingly personalized. AI tools can adjust headlines and text, and they can dynamically swap out banner images based on who is viewing them.
For example, first-time visitor sees a welcome offer, and someone who has purchased goods or services before sees their previous purchases and suggested products.
Dynamic Yield, Optimizely, and Adobe Target can all help you foster these automated personalized experiences.
Chatbots and Customer Support
AI chatbots are not just computer-generated responses. When fueled with customer data, they can give bespoke replies.
For example, a chatbot can remember a customer about their last order, check the status or take it even further by answering related questions directly tied to their account.
Instant support driven by customer data greatly enhances the customer experience while taking much of the weight off support teams.
AI in Advertising
Targeted advertising is entirely AI-based. Facebook, Google, and TikTok all serve ads through machine learning. They are getting to the most relevant users that enable them to serve ads based on user behavior, interests, and user interactions.
Ecommerce brands can leverage these social media channels to launch personalized ad campaigns. AI will also be able to optimize ads for their performance real-time, by constantly testing infinite combinations of creative, format, and targeting opportunities.
Post-Purchase Personalization
The customer journey does not end when the order is placed. AI allows ecommerce brands to stay in touch with the customer post-purchase. Smart platforms also connect post-purchase flows with POS data, ensuring that loyalty offers and product recommendations reflect the full customer journey.
This could be anything from personalized follow-up emails asking for a review, suggesting complementary items, or offering loyalty rewards.
Some AI tools including CAT Tools can even predict when a customer might be interested in reordering, so they can give reminders at the right time.
AI for Inventory and Supply Chain
Although they are not customer-facing in the traditional sense, backend personalization plays a critical role in meeting customer expectations.
AI contributes to the operational side of personalization by predicting product demand, identifying low-stock items, and optimizing warehouse efficiency. One key performance metric improved by AI is Days Inventory Outstanding (DIO): the lower the DIO, the faster inventory moves—leading to quicker fulfillment and fewer stockouts.
When these systems work in sync, customers receive what they want, when they want it—faster and more reliably.
AI in Customer Retention and Loyalty
AI is also key in nurturing customer loyalty. Brands can identify customers at risk of lapsing, using data from customer engagement, and trigger retention campaigns.
Retention campaigns might use a personalized offer or early access to sales and even reminders for loyalty points.
Loyalty platforms that integrate AI offer normalized data points with consideration for purchase frequency, average value of purchases, and product categories. Once customers are identified, loyalty platforms can suggest pathway decisions to increase lifetime value.
Real-World Examples
Amazon
Amazon applies artificial intelligence throughout its ecosystem, from voice shopping to product recommendations. Its AI software analyzes vast amounts of information about users to improve the shopping experience.
Sephora
The beauty brand Sephora uses artificial intelligence to create personalized product recommendations and has a virtual assistant to help customers select products based on skin tone and preferences.
Nike
Nike utilizes artificial intelligence within its apps to create descaled training plans, product drops based on user preferences, and personalized content.
Netflix (for Media Commerce)
Netflix may not be viewed as an "ecommerce" brand in the conventional sense; however, it serves as an illustration of how personalized recommendations can increase user engagement and satisfaction, and ecommerce brands can learn what they need from Netflix.
Ethical and Privacy Considerations
With power goes a tremendous responsibility. Artificial intelligence-based personalization relies on a significant amount of user data, so it's vital that brands are transparent in how they utilize user data and comply with data privacy regulations such as GDPR and CCPA.
Clear opt-ins, privacy policies, and the ability for users to choose how they wish to control their data are good ways to preserve trust.
Looking Ahead
AI will continuously evolve the e-commerce sector. As technology advances, enhancing customer personalization will become more refined.
Voice shopping, augmented reality, and predictive shipping are on the verge of becoming standard practices.
Brands that leverage AI now are better equipped to cater to future customers' demands. The challenge lies in utilizing AI as a functional tool rather than merely a trendy gimmick.
Depending on the objectives, harness AI to create useful and relevant experiences across various customer interactions.
