How AI agents are transforming the way e-commerce stores increase revenue through smart, real-time product recommendations.

The Challenge: Static Recommendations Aren’t Enough

In the world of e-commerce, cross-selling and upselling are critical strategies for increasing revenue and improving customer satisfaction.

But traditional recommendation systems fall short in several ways:

• Pre-set rules: Many recommendation systems rely on static rules or fixed recommendation engines that only work in broad categories (e.g., “People who bought X also bought Y”).
• Lack of context: They don’t take into account the real-time context of a shopper’s behavior, purchase patterns, or current interests.
• No adaptability: As shoppers browse, their needs and preferences change — yet static systems remain fixed, leading to irrelevant suggestions.

To address this, we’ve built Dynamic Product Recommendations into our Trusted AI Agent Builder, allowing merchants to provide highly targeted, real-time suggestions for cross-sell and upsell based on continuous customer insights.

How Our AI Agent Works: The Process

1. Purchase Pattern Analysis
   The AI agent starts by analysing historical and real-time purchase data. It looks at:
   • What items were purchased together in the past.
   • Trends in customer behaviour (seasonality, promotions, cart abandonment).
   • Similar products that drive higher value.
2. Real-Time Data Integration
   As a customer browses, the agent uses real-time data to adjust recommendations based on:
   • Current session activity (items viewed, time spent on product pages).
   • Previous purchase history.
   • Engagement with on-site content (e.g., product reviews, FAQs).
3. Targeted Cross-Sell and Upsell Recommendations
   The AI agent identifies contextual opportunities to recommend:
   • Cross-sell: Relevant, complementary products (e.g., “You might also like” items).
   • Upsell: Higher-value alternatives (e.g., “Upgrade to this premium version”).
   • Suggestions appear in real time across key touchpoints:
     • Product pages
     • Checkout pages
     • Cart abandonment emails
4. Continuous Learning and Adaptation
   The agent learns from each interaction:
   • It adjusts the recommendations based on what the customer clicks, adds to the cart, or purchases.
   • Over time, the agent’s suggestions become more personalised, improving conversion rates and boosting average order values.

The Results: Revenue Uplift and Improved Customer Experience

Our Dynamic Product Recommendations have delivered:

• Higher Average Order Value (AOV): Merchants have seen up to a 25% increase in AOV through personalised upsells and cross-sells.
• Improved Conversion Rates: By offering products based on a shopper’s preferences, conversion rates have increased by 15% in key segments.
• Customer Satisfaction: Shoppers appreciate receiving recommendations that align with their current interests, leading to more repeat customers and improved customer loyalty.

Example in Action:

One fashion retailer saw a 10% increase in conversion by adding real-time product recommendations for shoes based on a customer’s browsing activity (i.e., recommending complementary clothing or accessories to match the selected shoes).

Why Real-Time Recommendations Matter

• Personalisation is key: Generic recommendations don’t resonate with today’s savvy shoppers. Real-time suggestions tailored to the specific user’s journey increase the likelihood of conversion.
• Shoppers expect relevance: When a customer is in the checkout flow, they’re more likely to purchase an upgrade or add complementary items if the recommendation is contextually relevant to their current selection.

Adaptive to behavior: Real-time recommendations adapt to customers’ current intent, whether they’re browsing or ready to purchase, increasing the chances of a successful sale.

Final Word

AI-driven, real-time product recommendations are a game-changer for e-commerce merchants.
With our Trusted AI Agent Builder, you can instantly start cross-selling and upselling in a way that feels natural, relevant, and personalised to each customer’s unique buying journey.

Instead of relying on outdated rules-based systems, let AI take over the decision-making process and boost your revenue without lifting a finger.