Jamplus
jamplus.com
US
E-commerce
A leading online retailer needed to move beyond surface-level analytics. Their existing Looker Studio dashboard pulled data from Google Analytics 4 but lacked the depth to understand customer lifetime value or which acquisition channels were generating the most valuable buyers. Datakimia was brought in to bridge their Magento order data with GA4 attribution data, building a unified data foundation in BigQuery.
The engagement delivered a full LRFM (Recency, Frequency, Monetary Value, Loyalty) segmentation model enriched with channel-level attribution, a redesigned analytics dashboard with funnel analysis, product-level filters, and LTV visualizations—plus a complete ETL pipeline to keep everything current.
The result: a self-sustaining analytics stack that gives the team on-demand visibility into who their best customers are and where they come from.
Before Implementation
Magento and GA4 data lived in separate silos with no shared key
No LRFM model existed, the team couldn't distinguish high-value repeat customers from one-time buyers
Funnel performance across the Homepage was invisible, preventing CRO prioritization
Key Improvements
Achieved ~88% order match rate between Magento transactions and GA4 sessions
Deployed a full LRFM algorithm with cohort filters and product-type segmentation
Built a multi-stage funnel dashboard with channel, device, and category breakdowns
Project Overview
1.
Data Ingestion
Magento order exports in CSV were cleaned, schema-mapped, and loaded into BigQuery via Cloud Storage. Early errors from unquoted text fields with commas were resolved, establishing a stable queryable foundation for all downstream work.
2.
Data Modeling
Magento transactions were joined to GA4 sessions using Order IDs. Applying JAM, ENV, and F prefix filters achieved an ~88% match rate. Clean BigQuery tables were built with LRFM definitions, customer segments, cohort structures, and channel attribution fields.
3.
Dashboard Development
The Data Studio dashboard was rebuilt with LRFM segment views by channel, LTV charts with contribution margin, print vs. non-print filters, top-format filters (#10, A7, A5), and a full funnel analysis across the customer journey broken down by channel, device, and product category.
4.
Automation
A direct SQL Server → BigQuery connection was established to automate transaction refreshes, with Dataform scheduling to keep LRFM analysis current. Revenue was validated end-to-end, and the project closed with a recorded handoff session, clean repository, and full documentation.
BigQuery
Dataform
Data Studio
GA4
Adam Adelman
Chief Executive Officer
"Datakimia took our disconnected Magento and GA4 data and turned it into something we actually use every day. We can finally see which channels bring in our best customers, not just the most traffic."
