Personal Products is part of Connected Recommendations, a cross-product integration that connects Elevate's recommendation engine with Engage email campaigns. The module automatically selects and displays personalized products for each recipient at the time of send, based on their individual behavior and purchase history. No manual product curation required.
Prerequisites
Before using Personal Products, make sure the following is in place:
Connected Recommendations must be configured. Marketing groups in Engage need to be linked to an Elevate market. If this setup is missing, the module's dropdown menus will be empty and no products will be returned. Publishing or previewing without it will result in an error.
Recipients must be identified in both Engage and Elevate through a shared discoveryKey. Without this, all three recommendation types either fall back to top-selling products or return nothing (see Fallback behavior below).
Recommendation types
The module supports three recommendation types. You choose one per module instance.
LikelyToBuy: This predicts which products a recipient is most likely to purchase next, based on transaction history, browsing and search behavior, product interactions, and offline purchase data when available. The algorithm continuously adapts to changing behavior and product performance.
Based on Favorite: This recommends products related to items the customer has saved as favorites. Influenced by the attributes of favorited products, similar customer preferences, and product popularity.
Recently Viewed: This surfaces products the customer has recently viewed on your website or app. Useful for maintaining continuity between on-site browsing and email.
Setting up the module
- In Email Design Studio, find Personal Products in the standard modules library
- Add that module to your email layout
- In the Settings panel to the right, select a recommendation type.
- Set the number of products to display.
- (Optional). Select a Custom Price ID value if your market uses multiple price lists.
- (Optional). Add Product selection criteria to filter or exclude specific products. All product attributes available in Elevate can be used, including custom attributes. Include and exclude rules can be combined.
- Use Preview Email Content to verify the configuration before publishing.
Facets and values available in the filter dropdowns are determined by the market and locale linked to your marketing group. In Design view, the marketing group with the highest priority is used.
Previewing recommendations
To preview Personal Products with real recommendation data:
- In the email editor, activate Preview with data using the toggle in the top toolbar.
- Click the cogwheel an select Preview as a contact.
- Search for the contact you want to preview as.
- The preview will populate with product recommendations generated for that specific contact, based on their behavior and purchase history.
How products are selected
Product selection happens dynamically at send time, individually for each recipient.
Personalization requires identification. All three recommendation types require that the recipient is identified in both Engage and Elevate via a shared discoveryKey — including Recently Viewed. Without identification, there is no way to know what the recipient has browsed, purchased, or saved.
Fallback behavior. If a recipient cannot be identified, LikelyToBuy and Based on Favorite automatically fall back to top-selling products, while still applying any configured filters and selection criteria. If there are not enough personalized results to fill all slots, the remaining positions are filled with top-selling products. Recently Viewed has no meaningful fallback — if the recipient cannot be identified, no viewed products are available.
Market and availability. Recommendations are automatically scoped to the Elevate market linked to the marketing group. Out-of-stock products are excluded automatically. The module avoids surfacing multiple variants of the same product (such as the same item in different sizes or colors), keeping the selection clean.
What the module returns
For each recommended product, the following data is available to use in your email template:
| Field | Description |
|---|---|
| Product Group Key | Identifier for the product group |
| Product Key | Identifier for the specific product |
| Variant Key | The variant with the lowest price |
| Department | Product department |
| Brand | Brand name |
| Title | Product name |
| Description | Product description |
| Product Page Link | URL to the product page |
| Image URL | Full-size product image |
| Thumbnail URL | Thumbnail image |
| Selling Price | Current selling price |
| List Price | Original list price |
Use cases
The examples below illustrate some common ways to use Personal Products, but any email flow where individual behavior and purchase history can improve relevance is a potential fit.
Post-purchase follow-up: Use LikelyToBuy after a purchase to surface what the customer is likely to buy next, based on their full purchase and browsing history. Note that if you want to recommend products related specifically to the item just purchased, Contextual Products may be a better fit.
Loyalty and membership emails: Personalize recurring communications to loyalty members by surfacing relevant products through LikelyToBuy, adapting automatically to each member's shopping pattern without manual segmentation.
Re-engagement campaigns: For customers who haven't engaged in a while, LikelyToBuy draws on historical behavior to surface products that were relevant to them — more effective than generic bestseller lists.
Favorites-based recommendations: Use Based on Favorite in campaigns or automation flows targeting customers who have saved items, to surface related products they haven't yet purchased.