Every e-commerce business knows the power of cross-selling, but manually curating product recommendations is time-consuming and often becomes outdated quickly. What if your platform could automatically identify which products customers actually buy together and present those recommendations at the perfect moment in their shopping journey?
Aurora Commerce is excited to introduce Co-Purchases (Frequently Bought Together), a powerful new feature that transforms your historical sales data into intelligent, automated cross-selling recommendations that drive measurable increases in average order value.
Unlike static product recommendations that require constant manual updates, Co-Purchases analyzes actual purchasing patterns from your customers to automatically surface products that are genuinely bought together. The system continuously learns from every order, building a sophisticated understanding of product relationships that reflects real customer preferences rather than assumptions.
The algorithm considers multiple factors to ensure recommendations remain relevant and effective. It analyzes purchase frequency to identify strong product relationships, focuses on recent data using a rolling six-month window to capture seasonal trends and evolving customer preferences, and automatically excludes out-of-stock items to prevent customer frustration.
This data-driven approach means your cross-selling recommendations become more accurate over time, creating a self-improving system that adapts to your customers' changing behaviors and preferences.
Co-Purchases delivers recommendations at the most effective touchpoints throughout the customer journey. On product detail pages, shoppers see items frequently bought with the product they're viewing, encouraging larger basket sizes before checkout. In the shopping basket, the system suggests complementary items based on current basket contents, capitalizing on customers' demonstrated purchase intent.
Additionally, recommendations can appear on order completion pages to encourage follow-up purchases, turning satisfied customers into repeat buyers. This strategic placement ensures cross-selling opportunities are presented when customers are most receptive, maximizing conversion rates and revenue impact.
Aurora Commerce has designed Co-Purchases to deliver enterprise-level functionality with remarkable ease of implementation. Technical teams can integrate recommendations into existing templates using simple template tags, while business users can enable the feature through standard Aurora Commerce settings without requiring developer resources.
The system handles the complexity behind the scenes, automatically managing data processing, cache optimisation, and performance considerations that ensure recommendations load quickly even for large catalogs. When first activated, the feature intelligently backfills six months of historical purchase data, then maintains real-time updates as new orders are processed.
For stores with substantial transaction histories, this means immediate access to rich recommendation data rather than waiting months to build useful insights. The platform's robust architecture ensures this data processing happens seamlessly without impacting site performance.
Cross-selling through intelligent product recommendations has consistently proven to increase average order values across e-commerce verticals. Research from Boston Consulting Group and Shopify, analyzing over 1 billion data points across 220,000+ e-commerce sites, found that basket building through cross-selling can increase conversion rates by up to 63% when customers add multiple items versus single purchases.
By surfacing products that customers actually purchase together, rather than generic suggestions, Co-Purchases creates more relevant shopping experiences that feel helpful rather than pushy. The automated nature of the system means these revenue improvements compound over time as the algorithm learns from each transaction. Product relationships that might not be obvious to merchandising teams often emerge from the data, revealing unexpected cross-selling opportunities that can significantly impact business performance.
Co-Purchases exemplifies Aurora Commerce's platform philosophy: powerful features that integrate seamlessly with existing capabilities. The system works naturally with inventory management to exclude unavailable products, integrates with pricing systems to display accurate costs, and respects customer segmentation for personalized experiences.
This comprehensive integration means recommendations always reflect current business rules and customer contexts, providing accurate and actionable suggestions that support rather than complicate the shopping experience.
Co-Purchases is available now for all Aurora Commerce clients and can be activated through your standard platform settings. The feature requires no additional licensing fees, complex integrations, or separate vendor relationships, it's simply another powerful capability within your Aurora Commerce platform.
Ready to transform your customer data into revenue-driving recommendations?
Q: How quickly will I see Co-Purchases recommendations after activation?
Upon first activation, Aurora Commerce automatically backfills six months of purchase history to generate immediate recommendations. While this initial processing can take a few hours for stores with extensive transaction histories, you'll have meaningful recommendation data available as soon as processing completes, rather than waiting months to build useful insights.
Q: How does the system ensure recommendations stay current and relevant?Co-Purchases uses a rolling six-month data window that continuously updates as new orders are processed. The system automatically excludes out-of-stock products and considers purchase frequency to ensure only strong product relationships generate recommendations. Data is cached and updated hourly to balance performance with accuracy.
Q: What are the best locations to place these recommendations?Post-purchase pages typically deliver the highest conversion rates for cross-selling recommendations. This occurs because customers have just completed a purchase, trust is at its peak, and there's zero risk to the primary conversion since the transaction is already processed. The order confirmation page is particularly effective for complementary items, accessories, or consumables related to the purchased product. Product detail pages work well when recommendations appear below the main product information but above customer reviews, capitalizing on the moment when purchase intent is forming. For shopping cart placement, focus on genuine complementary items that enhance the primary purchase rather than generic suggestions, as irrelevant cart recommendations can increase abandonment rates.
