This article explains how to integrate Tweakwise recommendations into your Deployteq email campaigns and journeys. By combining Tweakwise's powerful recommendation engine with Deployteq's data model, you can show highly relevant products to specific segments of your audience.

Deployteq in Recommendations

Directly fetching a list of article numbers from a CDP for recommendations can be counterproductive (if it's available), as it bypasses the Tweakwise rule engine. To get the "best of both worlds," we recommend "weaponizing" CDP segments by turning them into Tweakwise rules.

The Strategy:

• Configure in Tweakwise: Create a recommendation group with a specific key for each segment (e.g., GroupKey = "segment-vip").
• Retrieve Customer Segment: On the platform side, fetch the current user's segment from the CDP (using the Deployteq Datamodel API).
• Request Recommendation: Call the Tweakwise Recommendations API using the retrieved segment name as the GroupKey.
• Fallback: Always configure a generic recommendation (e.g., "Trending") if no segment is found for the user.

This method uses the CDP for audience intelligence while letting the Tweakwise rule engine handle product selection, stock logic, and sorting.

Retrieving Customer Segment

Retrieve User Data in Deployteq On the platform side (within your campaign or automation), retrieve the current user and their associated segment from Deployteq using the Datamodel API (specifically the dataextension/customer endpoints).

Example

Imagine a fashion webshop, here is how we apply this when a user is viewing a coat product detail page:

• Deployteq identifies a customer as part of the "High-Spend Winter" segment because they buy expensive coats every year.
• Your website/middleware retrieves the segment as High Spend Winter and maps this to the recommendation groupkey segment-winter-luxury.
• The website/middleware calls Tweakwise using the groupKey:
  /recommendations/{instancekey}/{productid}/segment-winter-luxury
• Tweakwise only shows products from the "Coats" category with a price > €200, applying the merchandising rules that are configured in Tweakwise.

Important Considerations

• Scalability: This approach is manageable depending on the number of segments. You must create recommendation rules for each segment you wish to target.
• Rate Limiting: Ensure your middleware function handles any API rate limits to avoid performance bottlenecks on your lister pages.

Frequently Asked Questions

Why should I use segments instead of a product list from the CDP?
If you only pull a list of IDs, you lose the ability to apply the Tweakwise rule engine (and merchandising rules, like boosting high-margin products). Using segments as rules keeps your merchandising dynamic.

What happens if the API call to the CDP fails?
Your implementation should always include a fallback recommendation. If the CDP is unreachable or the user is unknown, Tweakwise should default to a standard algorithm like "Most Popular" to ensure the user never sees an empty block. This can also be done in Tweakwise configuration by configuring a fallback recommendation on the same group.