Recommender campaigns are the core of PureClarity’s personalization engine, delivering AI-driven product recommendations that adapt to individual customer behavior and preferences. When creating a campaign with a recommender template, you’ll choose between automated AI recommendations or custom curated selections to optimize customer engagement and sales.
PureClarity automatically tracks all recommender interactions and attributes revenue to successful recommendations, providing clear ROI measurement for your personalization efforts.

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Automated Recommender Campaigns

Recommended starting point for most stores, automated recommenders use machine learning to deliver the most relevant recommendations for each customer and context.

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How Automated Recommendations Work

PureClarity analyzes multiple data points to select optimal recommendations: Customer behavior factors:
  • Current browsing: Products being viewed and categories explored
  • Basket contents: Items added but not yet purchased
  • Purchase history: Previous orders and preferences
  • Session patterns: Time spent, pages visited, interaction depth
Contextual factors:
  • Page type: Homepage, product page, cart, category page
  • Product relationships: Cross-sells, upsells, alternatives
  • Inventory levels: Availability and stock status
  • Seasonal trends: Current demand patterns and popularity

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Page-Specific Recommendation Types

Homepage recommendations:
  • “Recommended based on your last visit”: Personalized for returning customers
  • “Trending Products”: Popular items for new visitors
  • “New Arrivals”: Fresh inventory for engaged customers
  • “Best Sellers”: High-converting products for broad appeal
Product page recommendations:
  • “Frequently bought together”: Cross-sell opportunities
  • “Customers also bought”: Alternative and complementary products
  • “Similar products”: Alternatives within the same category
  • “Complete the look”: Style and accessory recommendations
Cart page recommendations:
  • “Add to your order”: Last-minute upsells
  • “Others also bought”: Based on cart contents
  • “Recommended for you”: Personalized additional items

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Automated Recommender Settings

Minimum items: Set the minimum number of products required for display Maximum items: Control the maximum products shown per recommendation Fallback behavior: What happens when insufficient products are available
Start with 3-6 products per recommender for optimal balance between choice and decision fatigue. Adjust based on your specific product catalog and customer behavior.

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Custom Recommender Campaigns

Custom recommenders provide editorial control over product selection while maintaining the dynamic nature of personalized recommendations.
If no products match your custom recommender criteria for a specific customer, the campaign will not display. Always include fallback options or broader criteria.

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Custom Recommender Sources

Manual Recommenders
  • Specific products: Hand-selected items for precise control
  • Product categories: Target specific product types or collections
  • Brand focus: Highlight particular brands or vendors
  • Use cases: Clearance promotions, “complete the look” styling, editorial features
Best Sellers
  • Top performers: Products with highest sales volume
  • Trending items: Products with increasing popularity
  • Category leaders: Best sellers within specific categories
  • Consideration: May overlap with automated recommendations
Recommended For You
  • AI curation: Personalized selection based on customer data
  • Behavioral matching: Products aligned with customer interests
  • Dynamic updating: Changes based on recent activity
  • Limitation: May not have recommendations for all customers
Recently Viewed
  • Browsing history: Products customer has viewed recently
  • Re-engagement: Encourage return to considered products
  • Session continuity: Maintain shopping journey context
  • Availability: Depends on customer having browsing history
Trending Products
  • Rising popularity: Products with highest sales velocity increase
  • Market dynamics: Items gaining traction with customers
  • Fresh discovery: Showcase emerging popular products
  • Algorithmic selection: Based on sales rank changes

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Product Filtering Options

Refine recommendations with advanced filtering criteria: Price Range Filters
  • Minimum price: Exclude products below specified amount
  • Maximum price: Limit recommendations to affordable range
  • Use cases: Budget-conscious segments, luxury targeting
Category Filters
  • Specific categories: Limit to particular product types
  • Multi-category: Include multiple related categories
  • Exclusions: Remove categories not relevant to campaign
Brand Filters
  • Brand inclusion: Focus on specific brands or vendors
  • Brand exclusion: Remove particular brands from recommendations
  • Partnership focus: Highlight partner or featured brands
Tag-Based Filters
  • Product tags: Use platform-specific product tagging
  • Custom attributes: Filter by custom product properties
  • Seasonal tags: Include/exclude seasonal or promotional items
  • Platform dependency: Implementation varies by e-commerce system
Contact your customer success manager for guidance on implementing tag-based filtering specific to your e-commerce platform.

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Custom Recommender Settings

Campaign title: Customize the display title for the recommendation section Minimum/Maximum items: Control quantity displayed Fallback strategy: Define behavior when criteria yield insufficient products

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Performance Optimization Strategies

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Automated vs. Custom Decision Framework

Choose Automated when:
  • Starting with personalization
  • Seeking maximum conversion optimization
  • Limited time for content curation
  • Wanting set-and-forget functionality
Choose Custom when:
  • Promoting specific products or brands
  • Running targeted promotional campaigns
  • Need editorial control over recommendations
  • Testing specific product combinations

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A/B Testing Recommendations

Test different approaches:
  • Automated vs. Custom: Compare performance in similar zones
  • Product quantities: Test different min/max settings
  • Filter combinations: Experiment with different criteria
  • Title variations: Test different recommendation headers

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Common Optimization Tactics

Underperforming recommenders:
  • Broaden criteria: Reduce restrictive filters
  • Adjust quantities: Test different product counts
  • Review placement: Consider zone location and timing
  • Audience refinement: Narrow or broaden target segments
High-performing recommenders:
  • Scale successful patterns: Apply winning formulas to other zones
  • Refine further: Test minor adjustments for additional gains
  • Expand audience: Gradually broaden successful targeting
  • Document learnings: Capture insights for future campaigns

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Attribution and Revenue Tracking

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Automatic Click Tracking

PureClarity automatically tracks:
  • Product clicks: When customers interact with recommended products
  • Purchase attribution: Sales resulting from recommendation clicks
  • Revenue totals: Dollar value attributed to specific recommendations
  • Conversion rates: Success rates for different recommendation types

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Click Total Analytics

Zone-level reporting: Revenue attribution by website location Campaign-level insights: Performance of specific recommendation campaigns Product-level data: Which products perform best in recommendations ROI analytics: Detailed return on investment for recommendation efforts
Access comprehensive recommendation performance data through the Analytics section and specialized Recommender ROI reports.

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Attribution Best Practices

Revenue tracking accuracy:
  • Consistent attribution windows: Define clear timeframes for conversion credit
  • Multi-touch attribution: Understand customer journey complexity
  • Baseline comparison: Measure lift versus non-personalized experiences
Performance monitoring:
  • Regular review cycles: Weekly/monthly performance assessment
  • Trend analysis: Identify patterns and seasonal variations
  • Segment analysis: Compare performance across customer groups
Recommender campaigns form the foundation of effective personalization, driving both customer satisfaction and business results through intelligent product discovery and strategic recommendation placement.