Learn how to harness accurate customer data, real-time product info, and strategic optimization to drive higher conversions with dynamic product recommendations.
February 10, 2025
Dynamic Product Recommendations That Actually Work: A 3-Minute Guide
CMOs often struggle with product recommendations. Manual product matching is a resource-draining nightmare, while so-called “smart” AI often suggests the same generic items across the entire catalog.
After working with dozens of brands to refine their recommendation strategies, we’ve identified three essential components for success:
Bonus tip at the end—get the most out of your recommendations!
Accurate Customer Data
Effective recommendations go beyond just purchase history. A truly effective system captures a unified view of how customer behavior, inclufing:
- Purchase history across all channels
- Product interactions (views, wishlists, cart adds)
- Category browsing patterns
Omnichannel data is essential. If you have both a website and mobile app, ensure your recommendation engine tracks both. The same goes for offline purchases—these insights are often the missing piece in understanding true customer preferences.
One of Maestra's clients integrates data from their website, mobile app, and offline stores in Maestra CDP. By combining offline purchases from cash registers with online interactions and mobile app activity they build a comprehensive profile for more precise and effective recommendations
Rich, Real-Time Product Info
When feeding product data into your recommendation engine, focus on two key goals: real-time updates to ensure accuracy and rich content to drive engagement.
Real-time updates: Your engine must have up-to-the-minute data on product availability, pricing, and performance. Without it, you risk showing out-of-stock items, incorrect prices or irrelevant products—frustrating customers and undermining trust. Missing real-time synchronization can also create misconceptions about your offerings, ultimately harming your brand.
Rich content: The more product attributes you include, the more engaging your recommendations become. Consider including a variety of product attributes to highlight performance or differentiate products:
- Social proof and popularity indicators—ratings, reviews, "Bestseller" status, customer behavior signals (e.g., "Popular in your area", "Most viewed today")
- Availability and urgency signals—labels such as “New arrival”, “Back in stock", "Selling fast", "Only 5 left," or "Limited stock"
- Product performance and differentiation—detailed specifications, unique features, and unique selling points
- Pricing and promotions—active discounts and limited-time deals
These attributes help recreate the in-store shopping experience online, where customers naturally notice what others are buying, which items are running low, and what’s being featured. Adding real-time availability and social proof helps customers make confident buying decisions.
Furniture Fair enhances its recommendations by feeding comprehensive data into Maestra—including ratings, reviews, and promotional details
Implementation and Optimization Plan (or Someone to Own It)
To make product recommendations truly effective, you need:
- Strategic, page-specific algorithms
- Ongoing performance analyses
- Continuous A/B testing and optimization
- Well-designed templates for layout and functionality
Here's the catch: while dynamic recommendations reduce manual work, optimizing them still requires expertise and resources. Fine-tuning algorithms can become overwhelming if you're handling everything alone.
That’s why having a dedicated Customer Success Manager is critical. A strong CSM brings a deep knowledge of the recommendation engine and experience in optimizing customer journeys. Even if your team consists of e-сommerce experts, a platform specialist ensures you maximize the value and execution of your recommendation strategy.
Enlightened Equipment Achieves 15% Website Conversion Growth with AI-Powered Product Recommendations
A/B test results from Maestra's CSM work with Enlightened Equipment show a 15% increase in conversion rate and 8% higher average order value compared to the control group
(Bonus) Additional Coverage
Don’t limit recommendations to just your website. Expand their reach across mobile apps, email campaigns, web notifications, and more. If you have offline stores, connect your recommendation engine to POS terminals—this allows sales associates to provide better service by suggesting products customers are most likely to want.
Focus on key flows. Recommendations are crucial for abandoned browse, wishlist, cart, and checkout emails. Most brands simply remind customers of what they left behind, but those shoppers often already remember. Instead, use product recommendations to reignite interest in those items or introduce something new.
Originally, the jewelry brand German Kabirski implemented Maestra recommendations on their website…
…later, they expanded the feature to abandoned checkout emails, displaying both abandoned items and complementary suggestions to drive conversions
Want to Get Started?
Implementation Checklist:
- Audit your current customer data collection
- Review product information completeness
- Choose the right vendor with strategic expertise and dedicated CSM support
- Set clear, realistic goals
- Select relevant algorithms starting with the most visited pages of your website
- Design your recommendation UI/UX strategy
- Plan your testing strategy
Rather than navigating this alone, why not get expert guidance? Book a demo below, and our consultant will walk you through every step to set up truly personalized product recommendations. If we're not the right fit, we'll point you to a solution that is.