Optimizing Product Recommendations: with PIM Data

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. 

Must-Have Product Recommendation Strategies

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:

Core Product Recommendation Strategies

• Automatic Recommendations – Leverages user behavior, context, and available data to dynamically suggest the most appropriate products at the moment.
• Similar Products – Suggests alternatives similar to the product currently being viewed, helping indecisive customers find the perfect fit.
• Bought Together – Recommends complementary items frequently purchased with the product being viewed, ideal for cross-selling.
• Most Popular – Highlights trending products shoppers are loving across the site.
• Most Popular in Category – Narrows it down by featuring the top-performing items within a specific category, increasing relevance.
• User Affinity – Leverages a user’s browsing history (e.g., product views, cart additions) to show items that match their personal preferences.
• Recently Viewed – Helps customers pick up where they left off by showcasing products they recently browsed.
• Recently Purchased – Displays recommendations based on items a user recently bought, often used to suggest refills or complementary products.
• Hybrid Strategies – Combines multiple strategies to create even more tailored experiences, such as mixing affinity-based suggestions with popular items.

Why These Strategies Work

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. 

How to Use the Data in Your PIM?

Use Product Attributes for Matching

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).

Leverage Relationships Between Products

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.

Incorporate Customer Behavior

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.”

Use AI & Automation

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.

Optimize Search & Filtering

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.

Enhance Recommendations Across Channels

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.

Best Practices for Optimizing Product Recommendations

Once you’ve deployed basic recommendation tools, the real work begins. Optimizing these systems ensures maximum impact and elevates both user experience and profitability. 

1. Exclude Product Prices in Email Recommendations

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. 

2. Engage Side-Door Traffic

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. 

3. Leverage Demographics, Behavior, & Location Data

Use available customer data to create merchandising rules that tailor recommendations. For example:

• Recommend winter coats to an audience in Boston during cold weather.
• Alternatively, personalize suggestions based on income levels, gender, or brand affinity to make a stronger connection.

4. Personalize Category Pages

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. 

5. Feature Discounted Items Sitewide

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. 

6. Upsell on the Cart Page

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. 

7. Expand Recommendations Across Digital Channels

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.