Challenge
The core challenge was turning scattered, inconsistent data into a trusted “single customer view” while remaining Shopify-native and scalable. The platform had to ingest data reliably from Shopify and connected systems, normalize it into a unified model, and keep it queryable for analytics without performance degradation. Another complexity was providing segmentation that goes beyond Shopify’s native logic, plus real-time RFM computation and behavior-driven personalization—without introducing operational overhead or brittle pipelines.
The core challenge was turning scattered, inconsistent data into a trusted “single customer view” while remaining Shopify-native and scalable. The platform had to ingest data reliably from Shopify and connected systems, normalize it into a unified model, and keep it queryable for analytics without performance degradation. Another complexity was providing segmentation that goes beyond Shopify’s native logic, plus real-time RFM computation and behavior-driven personalization—without introducing operational overhead or brittle pipelines.
Solution
Onix designed and implemented a Shopify application as the entry point for structured data ingestion and platform interaction. We built a unified customer data model, an RFM analysis engine, advanced segmentation logic, and an analytics layer with dashboards for customer, product, and event insights. To enable personalization, we added rule-based workflows and recommendation logic based on live behavior. The solution used a modular, API-driven architecture with an event-driven pipeline (TypeScript/Node.js, Shopify APIs, PostgreSQL, Redis, AWS), ready for controlled scaling.
Onix designed and implemented a Shopify application as the entry point for structured data ingestion and platform interaction. We built a unified customer data model, an RFM analysis engine, advanced segmentation logic, and an analytics layer with dashboards for customer, product, and event insights. To enable personalization, we added rule-based workflows and recommendation logic based on live behavior. The solution used a modular, API-driven architecture with an event-driven pipeline (TypeScript/Node.js, Shopify APIs, PostgreSQL, Redis, AWS), ready for controlled scaling.
Results
The client received a production-ready foundation for a Shopify-native CDP: centralized access to customer/product/event data, automated RFM segmentation, flexible targeting beyond Shopify defaults, and actionable analytics views that support marketing and retention decisions. The platform also introduced a personalization layer that connects insights to execution, allowing teams to move from dashboards to measurable actions. The modular architecture reduced long-term risk and makes it easier to extend ingestion sources, segmentation models, and personalization rules over time.
The client received a production-ready foundation for a Shopify-native CDP: centralized access to customer/product/event data, automated RFM segmentation, flexible targeting beyond Shopify defaults, and actionable analytics views that support marketing and retention decisions. The platform also introduced a personalization layer that connects insights to execution, allowing teams to move from dashboards to measurable actions. The modular architecture reduced long-term risk and makes it easier to extend ingestion sources, segmentation models, and personalization rules over time.