Ever feel like your online store has plenty of visitors, yet they often leave without adding more items to their carts—or worse, abandon them altogether? That’s where personalized product recommendations come in.
By tuning into each shopper’s browsing habits and purchase history, you can deliver spot-on suggestions that boost average order value (AOV), reduce cart abandonment, and build genuine loyalty.
Below, you’ll find eight proven tactics—backed by real-world examples from BrandAlley—to help you get more out of every visitor. First, let’s learn about the impact of personalization, why it matters, and the types of recommendation systems.
Most online shoppers say they’re more likely to revisit sites offering tailored suggestions, and personalization affects everything from engagement to cart size.
According to our research on omnichannel retention and loyalty in 2024, 31% of customers are more inclined to remain loyal when their shopping experiences feel personal.
Another prime motivator is reducing cart abandonment. In 2024, the Baymard Institute found that 70.19% of all online retail orders were abandoned—a sobering figure for any e-commerce brand. Well-timed, relevant product recommendations can help cut that rate significantly, showcasing items customers genuinely want rather than letting them slip away.
Let’s now examine the types of recommendation systems, how each one functions, and the unique benefits they bring to e-commerce success.
To push average order values higher, you need more than guesswork. AI helps dissect real-time user actions, identify patterns, and uncover hidden sales opportunities. By pinpointing what each shopper will likely want next, you can serve relevant cross-sells and upsells right when they’re most receptive.
Why it helps: Advanced algorithms quickly spot behavioral patterns and predict customer intent.
How it works:
Explore how SAP Emarsys AI-driven Omnichannel Marketing empowers you to build, launch, and scale personalized, AI-driven marketing campaigns that convert buyers and drive repeat purchases.
Relying on data you’ve gathered—rather than third-party sources—builds a more accurate customer profile and fosters long-term user trust. This robust foundation makes it easier to tailor personal and relevant messaging.
Why it helps: Unified customer touchpoints (website, email, loyalty apps) enable accurate profiles and fuel consistent experiences.
How it works:
Learn more about first-party data with our complete guide.
The items that catch a shopper’s eye today may be out of stock or overshadowed by new arrivals tomorrow. Keeping your product suggestions fresh ensures you’re always showcasing what’s current and compelling.
Why it helps: Consumer interests shift fast, and inventory changes can make older suggestions irrelevant, so constantly adapting is essential.
How it works:
Whether it’s the festive holidays, summertime travel spree, or back-to-school rush, tapping into seasonal events can boost purchase intent. Aligning your recommendations with these temporal cues keeps your brand relevant and timely.
Why it helps: Timely promotions keyed to current events boost relevance and urgency.
How it works:
With more people browsing on smartphones than ever, a friction-heavy mobile experience can push users away—dropping your AOV. Presenting recommendations in a sleek, mobile-friendly format is key to sustained engagement and more substantial cart totals.
Why it helps: Most shoppers browse via smartphones, so having a clunky layout can hamper engagement.
How it works:
Want to learn more about mobile optimization? Check out how SAP Emarsys’s Mobile Customer Engagement solution helps you grow the lifetime value of mobile-first customers through data-enriched personalization and other methods.
Small design tweaks—like changing headline text or swapping a slider for a grid layout—can make a big difference in conversions. A structured A/B testing strategy shows what truly resonates, boosting both user satisfaction and sales figures.
Why it helps: By running A/B tests, you can evaluate the impact of changes made to content and audience targeting through metrics such as reach, engagement, click-through rate, and conversion.
How it works:
Customers hop between search pages, homepages, product detail sections, and the checkout flow – this has been the norm recently. Showing relevant product suggestions at each of these junctures consistently can significantly raise the overall cart value.
Why it helps: Consistent exposure to relevant items throughout the user journey keeps your brand top-of-mind.
How it works:
Key insight: Holistic integration ensures each touchpoint reinforces the next, creating a unified brand and shopping experience.
Trends and preferences can shift overnight—one item might surge in popularity due to social buzz, while another falls off the radar. Monitoring these changes in near real-time keeps your recommendations from becoming stale or irrelevant.
Why it helps: Consumer tastes pivot quickly; real-time analytics ensure your strategy adapts.
How it works:
In action: Adidas Running App’s Real-Time Triggers show how swiftly reacting to new user data can drive engagement and retention across the entire funnel.
Creating a data-driven recommendation engine can yield great results, but it can have several hurdles. Understanding these challenges upfront and planning to overcome them ensures your personalization strategies stay robust and compliant.
Smaller retailers often lack the vast data sets required for more accurate, AI-driven personalization. Sparse datasets can undermine recommendation accuracy and leave you with generic suggestions that fail to resonate. Possible solutions:
Amid regulations like GDPR and CCPA, brands must carefully handle customer data. Although personalization thrives on details, it must be balanced with transparent data collection and usage practices. Possible ways to address it:
By proactively tackling data volume and privacy challenges, you can build robust recommendation systems aligning with user expectations and regulatory requirements.
Modern recommendation engines combine AI with real-time data, focusing on three key layers:
Looking ahead, the next generation of recommendation systems will emphasize:
Why it matters: These trends empower e-commerce brands to deliver more innovative, relevant experiences while respecting user privacy.
Pro tip: SAP Emarsys Omnichannel Customer Engagement Platform for e-Commerce helps you grow a database of engaged, high-value shoppers and dramatically improve time to value with pre-built, AI-powered marketing automation.
Challenge: Generic email blasts were yielding diminishing returns. Their marketing team could see in their customer lifecycle that customers were lapsing, but it was too late by the time they had the insight.
Solution: Deployed AI models to predict customer preferences, particularly for mid-funnel users. They also improved AOV lift by placing “Frequently Bought Together” widgets directly on product pages—turning a single-item visit into a multi-item purchase.
Results:
Key takeaway: This rise in average order value illustrates the effectiveness of personalized product recommendations in driving revenue growth. BrandAlley demonstrated adaptability by adjusting its product offerings and marketing strategies during the pandemic.
Focusing on the home and garden categories, they achieved a remarkable 130% revenue increase from these segments. This highlights the importance of understanding and responding to shifts in consumer behavior.
As we know, personalized product recommendations consistently raise AOV, reduce cart abandonment, and deepen loyalty. By applying the eight best practices—fueled by unified data, AI algorithms, and regular testing—you can craft the tailored experiences online shoppers now demand.
From abandoned carts to customer churn, SAP Emarsys Customer Engagement Platform identifies key growth opportunities and helps you capitalize on them quickly. Its strategies are easy to use and can be executed with a single click.
Ready to sharpen your recommendation strategy? Book a demo to discover how AI-driven personalization can integrate across your entire customer journey, from the homepage to post-purchase outreach, ultimately driving higher AOV and long-term growth.
Nick Odom is a Principal Solutions Consultant for SAP Emarsys and has been with the company since 2016. He has worked with e-commerce, consumer products, and retail companies to discover impactful omnichannel use cases that drive business results.
