Most ecommerce brands focus heavily on acquisition. But as customer acquisition costs continue to rise, the real question isn’t “How do we get more traffic?”, it’s “How do we get more value from the traffic we already have?”
That’s where product recommendations come in.
Product recommendations are now a core part of any successful ecommerce growth strategy. With AI-powered personalization, brands can deliver the right products to the right audiences at exactly the right time. This not only boosts customer satisfaction but also strengthens brand loyalty and increases average order value by up to 35%.
And the impact goes far beyond just improving the customer experience.
When done right, product recommendations drive measurable business growth. Brands using Netcore’s recommendation engine have seen revenue increase by as much as 300%, driven by smarter merchandising, higher engagement, and better conversion across the funnel.
Yet many brands still treat recommendations as a static add-on buried at the bottom of a PDP or checkout page. That’s a missed opportunity.
Netcore helps you unlock the full potential of recommendations by making them dynamic, contextual, and behavior-led powered by AI that understands each shopper’s intent in real time. From homepage to cart abandonment flows, every touchpoint becomes a conversion opportunity.
This isn’t personalization for vanity metrics. It’s personalization built for performance — boosting:
- Click-through and conversion rates
- Cart size and average order value
- Repeat purchases and retention
- Overall revenue impact across channels
If your product recommendations aren’t actively increasing revenue and loyalty, they’re underperforming. Netcore equips you with both the strategy and the technology to turn recommendations into a revenue engine.
Where Traditional Recommendation Engines Fall Short
We have examplined below the key areas where traditional recommendation engines fall short in today’s dynamic and customer-centric e-commerce landscape. These limitations often stem from outdated methodologies and a lack of real-time adaptability, ultimately hindering the ability to deliver truly personalized experiences. Here’s a more detailed breakdown:
1. Poor Real-Time Performance Due to Static Cohort-Based Recommendations
- The Shortcoming: Traditional engines often rely on pre-defined customer segments or cohorts built on static data like demographics and past research. This means recommendations are generated in batches, well before a customer even visits the site.
- Why it Falls Short:
- Lack of Responsiveness: By the time a customer lands on the website, their immediate intent and context might be entirely different from the assumptions made when the cohorts were created. For example, a customer who previously browsed for hiking boots (placing them in a “outdoorsy” cohort) might now be looking for a gift for a family member. The static recommendations won’t reflect this immediate need.
- Missed Opportunities: Real-time events like items added to the cart, products viewed in the current session, or even the referring source of the traffic are completely ignored. This leads to irrelevant recommendations and missed opportunities to suggest items that are highly likely to be of interest right now.
- Stale Recommendations: The “most popular” or “others also bought” recommendations, while seemingly based on past data, are often generic and don’t take into account individual browsing history or current intent. What’s popular overall might not be relevant to a specific user at a specific moment.
2. Failure to Deliver True Personalization with Generic Tactics
- The Shortcoming: Relying on growth hacking tactics like “most popular” or “others also bought” provides a superficial level of suggestion but lacks genuine personalization.
- Why it Falls Short:
- One-Size-Fits-All Approach: These tactics treat all customers similarly, ignoring their unique preferences, past interactions, and current needs. A first-time visitor will see the same “most popular” items as a loyal customer with a long purchase history.
- Lack of Contextual Relevance: These recommendations don’t consider the specific product a customer is currently viewing or what brought them to the site. Suggesting a completely unrelated “popular” item can be jarring and unhelpful.
- Erosion of Customer Expectation: Today’s customers are accustomed to personalized experiences from various online platforms. Generic recommendations feel impersonal and demonstrate a lack of understanding of their individual needs, leading to frustration and potentially driving them away.
3. Inability to Leverage Contextual Awareness for Dynamic Recommendations
- The Shortcoming: Traditional, manual recommendation approaches struggle to incorporate dynamic contextual data like user intent, real-time session behavior, and lifecycle stage.
- Why it Falls Short:
- Intent Blindness: Manual cohort creation cannot effectively capture the nuanced intent behind a user’s actions. Are they browsing, researching, comparing, or ready to buy? Traditional engines often fail to differentiate.
- Ignoring Session Dynamics: The sequence of pages a user visits, the time spent on each page, and the actions they take within a session provide valuable clues about their current interests. Manual systems cannot process and react to this real-time behavioral data.
- Lifecycle Stage Neglect: A new customer has different needs and preferences than a repeat customer or a churned user. Traditional engines often fail to tailor recommendations based on where a customer is in their journey with the brand. This leads to irrelevant suggestions (e.g., showing introductory offers to loyal customers).
- Scalability Issues: Manually creating and managing cohorts based on various contextual factors becomes incredibly complex and time-consuming as the customer base and product catalog grow. It’s simply not scalable to deliver truly context-aware recommendations using manual methods.
ROI of Netcore’s Product Recommendations
Netcore’s AI-powered product recommendation engine delivers measurable impact across the customer journey. It boosts engagement, reduces friction, and drives exponential revenue growth. Here’s how leading e-commerce brands are seeing tangible ROI:
Reduce Cart Abandonment
Smart product suggestions, such as similar or complementary items, help re-engage users and influence purchasing decisions. Brands using Netcore have seen up to 92% improved engagement with these nudges.
StarQuik, for example, used AMP email gamification like spin-the-wheel offers to boost email engagement by 700% over static HTML campaigns. This interactive strategy contributed to a 20–40% uplift in cart recovery from retargeting efforts.
Boost Repeat Purchases With Behavior-Led Personalization
Product recommendations are not just about the first sale. They are also key to bringing customers back. Netcore has helped leading brands achieve a 22% increase in repeat purchases, driving long-term loyalty. The result is a staggering 42X ROI attributed to improved customer retention and sustained engagement.
Increase Average Order Value With Precision Targeting
By personalizing product suggestions based on customer behavior and interests, Netcore nudges shoppers toward high-value carts. These recommendations often lead to customers adding more items they didn’t initially intend to purchase.
This strategy has delivered up to a 300% increase in revenue, fueled by a notable rise in Average Order Value (AOV).
Increase Conversions
Netcore’s product recommendations also power smarter cross-sells and up-sells across digital touchpoints. Brands have reported a 28% lift in conversion rates thanks to improved product discoverability and targeted suggestions, especially through Netcore’s intelligent search functionality.
Year-on-year growth in conversion rate powered by product recommendations.
Netcore’s AI Product Recommendations: Built for Performance at Scale
When it comes to product recommendations, most marketers think of the typical “You may also like” suggestions on a product detail page. While that’s a start, Netcore takes it to a whole new level, backed by advanced AI and purpose-built to perform at scale.
Netcore’s AI product recommendation algorithm, visualized.
Let’s break down how Netcore personalizes the entire shopping journey with precision and impact:
Similar Product Recommendations That Keep Shoppers Engaged
Customers don’t always find what they’re looking for on the first try. Netcore’s AI-driven “similar product” suggestions account for product type, brand, category, and even nuanced features like color, material, or length.
This helps retain customers who may not be satisfied with the current product they’re browsing. With built-in synonym recognition and advanced product discovery, shoppers can also uncover products they didn’t know how to search for—keeping them engaged and moving closer to purchase.
Behavior-Based Recommendations That Learn Like YouTube
Netcore’s algorithm doesn’t just serve static suggestions. It continuously learns from every interaction a customer has with your brand—browsing history, search patterns, past purchases, and even session behavior.
Think of it like YouTube’s recommendation engine, but tailored for ecommerce. The result? Personalized, relevant product recommendations that increase the likelihood of conversion with every click.
Personalized Search Results That Improve Product Discovery
Shoppers today expect speed and precision in their search experience. Netcore’s intelligent search doesn’t just match keywords, it understands the intent behind each query.
It gathers data from shopper behavior and integrates it into real-time search results, helping customers find the right products faster. This level of personalization keeps visitors engaged and significantly improves product discoverability.
Built-In Engagement Across the Funnel—from Discovery to Purchase
Netcore goes beyond recommendations. As a Full-stack Customer Engagement Platform, it offers powerful tools to guide users through their journey from first click to repeat purchase.
Marketers can easily set up automated retargeting across AMP emails, push notifications, in-app messages, and more. Netcore also provides enterprise users with a dedicated Customer Success Manager to help launch and optimize campaigns focused on customer retention, loyalty, and engagement.
Plan retargeting campaigns to send more effective product recommendations than ever before by leveraging Netcore’s preferred channel options and expert support.
Seamless Integration with Your Ecommerce Stack
Deploying Netcore is fast, flexible, and friction-free. It’s built to integrate smoothly into your existing tech stack with minimal dev dependency and plug-and-play support.
No more back-and-forth between marketing and tech teams to launch recommendation-driven campaigns. With Netcore, marketers take control, with full technical support when needed.
How much revenue are you missing? Find out by calculating the ROI of smarter product recommendations.
Why Senior Ecommerce Marketers Choose Netcore Over Other Platforms
Netcore isn’t just another ecommerce personalization platform. It’s a product recommendation engine designed for ROI and ease of use.
- Proven impact: up to 300% increase in revenue
- Built-in AMP and omnichannel capabilities
- Faster time-to-value
- Dedicated support teams for onboarding, integration, and strategy
- Trusted by leading ecommerce brands
Book a free consultation with our experts to explore how product recommendations can be tailored for your ecommerce brand. Set clear goals, and let our team help you deliver measurable results faster.
