This guide explains how to use the Product Recommendation score from Batch AI Predict to show each customer the products they are most likely to buy and move beyond generic bestseller lists.
Batch AI Predict's Product Recommendation score calculates, for each customer in your database, a ranked list of the N most relevant products to recommend. The output is a list of product IDs (configurable: top 1 to top N), based on each customer's purchase history and behavioral profile.
The model is configurable at several levels of granularity: SKU, category, or brand (depending on what level you want to personalize in your templates).
Product Recommendation vs. Product Propensity: which one do you need?
Product Recommendation: you start from a customer and want to find the best products to show them. Use this for personalization: "What should I put in this customer's template?"
Product Propensity: you start from a product or category and want to find the best customers to target. Use this for segmentation: "Who should I contact for this campaign?"
Generic recommendation blocks (your top 3 bestsellers, the same for every customer) perform significantly below their potential. Each customer has a purchase history and browsing behavior that makes certain products far more relevant than others.
With the Product Recommendation score, you can:
Replace static product blocks with dynamic, individually calculated recommendations.
Increase click and conversion rates by showing customers products they are genuinely likely to buy.
Run a single campaign rather than duplicating templates per segment or category.
Note on single-purchase customers: Recommendations for customers with only one past purchase will be less precise, but still more relevant than a generic bestseller list. You can combine this score with a purchase count filter in your targeting if needed.
The core use case: inject each customer's personalized product recommendations directly into your email, push, or in-app templates.
Batch AI Predict exposes the top N recommendations as separate custom attributes on each customer profile:
batch_ai_reco_top1
batch_ai_reco_top2
batch_ai_reco_top3
(and so on up to your configured N)
How to set it up:
In your template composer, add a product recommendation block (email carousel, push dynamic content, or in-app product card).
Use the Batch personalization syntax to inject the recommended product attributes:
{{ profile.custom_attributes.batch_ai_reco_top1 }}
{{ profile.custom_attributes.batch_ai_reco_top2 }}
For each product ID, map it to the corresponding product image, name, and URL from your catalog.
Each customer who receives the campaign sees a different product selection, calculated specifically for them.
Recommended test: Run an A/B Content Personalization Test: Variant A uses the Batch AI Predict recommendation block, Variant B uses your current logic (static bestsellers, last viewed items, or wishlist). Compare click rates, conversion rates, and RPM. See the Performance Measurement Guide for details.
Industry
Example use case
Retail
Personalize the "You might also like" block in post-purchase or weekly newsletter emails; every customer sees a different product selection
Media & Entertainment
Recommend articles, podcasts, or video content in weekly digest emails based on each subscriber's consumption history
Travel
Recommend the most relevant destinations or experiences based on each traveler's booking history and browsing behavior
Beauty & Cosmetics
Personalize the product carousel in reactivation emails: each lapsed customer sees the products most aligned with their preferences
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