Connect commercetools with Zeotap, a privacy-focused CDP, to unify first-party data with identity resolution, build consent-aware audiences, and activate them across paid media and CRM in a compliant way.
• Customer profiles and identifiers from Commercetools are exported to Zeotap and mapped to Zeotap identity graphs for deterministic and probabilistic resolution.
• Behavioral events such as product views, add-to-cart, checkout, and purchases are streamed or batch-synced, with timestamps and channel metadata preserved.
• Consent and preference signals are passed alongside profile and event payloads; audience membership is evaluated against consent status and purpose where available.
• Attribute mappings normalize fields such as email, phone, address, locale, and customer group into Zeotap’s schema with validation for required formats.
• Delta sync logic sends only changed customer attributes and new events, while retries and dead-letter handling capture failed payloads for later reprocessing.
• Audience exports are pushed from Zeotap to downstream activation destinations, with hashed identifiers used where supported and activity logs retained for auditability.
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We map commercetools API events (orders, carts, product views) and customer profiles into Zeotap via server-side tracking or batch exports. The goal is consistent IDs and clean, consent-aware data for activation.
Zeotap can resolve multiple identifiers into one customer profile, reducing duplicates across sessions, devices, and channels. This makes audiences and reporting more reliable.
Yes, audiences can be created using consent signals and then synced to ad platforms or CRM tools for compliant activation. You control what gets activated, where, and under which consent state.
Most teams start with purchase history, product interest signals, and key customer attributes, then add lifecycle events like returns or subscriptions. We also align naming and schemas so segments stay stable over time.
We implement consent capture, consent storage, and region-based rules in the data pipeline, then validate what each destination receives. This is especially important for multi-market setups where policies differ.




