Product recommendations have become a powerful tool for e-commerce businesses. They are much more than a simple “you may also like” feature—these suggestions are driven by sophisticated algorithms and powered by data. The goal? To deliver hyper-personalized shopping experiences while increasing engagement, conversions, and ultimately, revenue.
From understanding user behavior to tailoring suggestions in real-time, product recommendations use an intricate dance of data and technology to meet customer needs and boost a brand’s bottom line.
Developing high-impact product recommendations isn’t as simple as placing random suggestions on a page. They require the right strategy, context, timing, and user insights.
An effective recommendation engine should offer the following strategies out of the box to cover diverse customer journeys:
By aligning with customer intent and browsing patterns, these approaches personalize the shopping journey. For instance, first-time visitors might engage with popular products, while return shoppers could benefit from affinity-based suggestions.
Your PIM holds detailed product attributes—category, brand, specifications, and even customer reviews. By analyzing similarities (e.g., “customers who bought this laptop also purchased this docking station”), you can create recommendations based on complementary products (cross-selling) or similar alternatives (upselling).
Many PIMs allow you to create relationships between products, such as bundles, accessories, or frequently bought-together items. These pre-defined connections make it easy to surface relevant recommendations dynamically.
While PIM primarily handles product data, combining it with customer data (from your e-commerce platform or CRM) allows for behavior-driven recommendations. If a customer frequently browses gaming monitors, your system can suggest high-refresh-rate models based on PIM attributes like “Hz” and “panel type.”
If your PIM integrates with AI-driven recommendation engines, you can automate suggestions using machine learning models that analyze product trends, ratings, and user interactions. AI can refine recommendations based on which products get clicked, added to the cart, or purchased together.
Your PIM’s structured data can also enhance dynamic filtering, helping customers refine their search and discover related products effortlessly. If someone filters for “wireless noise-canceling headphones,” your recommendation engine can prioritize models with similar specs.
Sync your PIM-driven recommendations across all touchpoints—your website, emails, mobile apps, and even in-store kiosks. Unified, data-driven suggestions ensure a consistent and personalized shopping experience.
By structuring, linking, and enriching your PIM data, you can turn it into a powerful recommendation engine that improves customer experience and boosts conversions.
Once you’ve deployed basic recommendation tools, the real work begins. Optimizing these systems ensures maximum impact and elevates both user experience and profitability.
Emails should drive website traffic. Including high prices in emails can deter users from clicking through. Instead, focus on visually captivating imagery and glowing customer reviews to pique interest.
Direct referrals and search engine traffic often land on specific product or category pages without entering through the homepage. To capture their attention and reduce bounce rates, prominently display similar product recommendations at the top of the page or as Pinterest-style grids that encourage seamless browsing.
Use available customer data to create merchandising rules that tailor recommendations. For example:
Default category pages often overwhelm users with too many options, leading to choice paralysis. Instead, use affinity-based strategies to showcase the visitor’s preferred items at the top of the page. This guarantees they will see products that resonate with their tastes first, boosting purchase intent.
Don’t limit discounted products to your Sale page. Place them strategically across the site, from category pages to product widgets, to appeal to deal hunters and maximize cart value.
The cart page isn’t just for reviewing purchases; it’s a prime opportunity for upselling. Suggest complementary items based on what’s already in the basket or encourage users to increase the quantity of any product to save on shipping or bundle deals.
Move beyond website recommendations. Use them in email campaigns, app push notifications, digital receipts, and even in-store interactions through geolocation data. Consistency across channels provides a seamless shopping experience.
Read further: News, PIM, product data, recommend products
