Executive Summary
A fast-growing UK D2C pet products brand struggled with fragmented data across Shopify, Google Ads, Meta, AppLovin, and Recharge. DataOptix built a unified analytics foundation using a 4-layer Data Vault 2.0 architecture on Google Cloud, eliminating data silos and enabling accurate attribution, churn insights, and self-serve reporting. Within weeks, the business achieved:
- 15% increase in revenue opportunities
75% faster analysis and reporting - 95% visibility into churn and customer behavior
Client Overview
The brand sells premium pet supplies via Shopify and spends USD 30k+ each month across multiple ad channels. However, rapid growth led to:
- Isolated marketing and sales data
- Time-consuming manual reporting
- No single source of truth for CAC, ROAS, or LTV
- Decisions driven by guesswork instead of trusted insights
Key Challenges
- Fragmented Data & No Unified View
- Data scattered across Shopify, Google Ads, Meta, AppLovin, and Recharge
- No combined customer journey across touchpoints
- Attribution limited to last-click → misleading ROAS
- Manual, Error-Prone Reporting
- CSV exports, spreadsheets, and reconciliation by hand
- Slow analysis that became outdated by the time it was ready
- Conflicting numbers across systems (Ads vs Shopify vs Finance)
- Zero Visibility into Customer Behavior
- No churn signals
- Inaccurate CAC and ROAS
- No LTV insights by channel, campaign, or cohort
Solution: Unified Data Architecture with Data Vault 2.0
DataOptix implemented a 4-layer data warehouse on Google Cloud, built for scale and analytics agility.
Data Layers : | |
| 1. Raw Layer | Ingested source data as-is from Shopify & all ad platforms (with CDC for efficiency) |
| 2. Data Vault Layer | Modeled core entities (Customers, Orders, Campaigns) with full audit history with Vault2.0 architecture |
| 3. Dimensional Layer | Built star-schema models for fast BI reporting & multi-touch attribution |
| 4. Analytics Layer | Delivered business-ready tables for dashboards, ad-hoc queries & ML use-cases |
Tech Stack: BigQuery, DataForm, Python/Node connectors, Looker Studio
What Changed for the Business
Immediate Improvements
- 75% reduction in analysis time — from days to minutes
- One source of truth across all marketing and sales metrics
- Self-service dashboards for Marketing, Finance, and Leadership
Strategic Insights Unlocked
- Clear multi-touch attribution (first-touch, last-touch, position-based, time-decay)
- Accurate ROAS and CAC by channel/campaign
- 95% churn visibility powered by ML features (frequency, recency, AOV trends)
Revenue Impact
- +15% revenue opportunities through better targeting, churn reduction & cross-sell insights
- Optimized ad spend — shifting budget from vanity clicks to profitable channels
- Predictable subscription growth and better inventory planning with cohort analytics
Future-Ready Foundation
The brand is now equipped to:
- Add new ad sources (TikTok, Bing, etc.) in days
- Expand geography with minimal setup
- Scale AI initiatives (LTV prediction, recommendations, churn prevention automations)