Unlocking Revenue Insights via Retail Analytics Transformation
Overview
One of the largest furniture retailers in the United States was struggling with high analytics costs, operational complexity, and inconsistent revenue reporting. Discrepancies between digital analytics and internal financial systems created a trust gap, leaving leadership reliant on Power BI while marketing data remained siloed and underutilized.
The client was seeking a scalable, cost-efficient analytics solution to unify disparate data sources, align digital and financial reporting, and deliver a consistent view of revenue and performance. They also needed to empower marketing teams with actionable insights and enable faster, data-driven decision-making within their existing BI environment.
Rysun addressed these challenges by implementing a modern, Google-centric analytics architecture that unified online and offline data, established a single source of truth, and delivered reliable insights directly into the BI tools executives already used.
Industry
Furniture Retail & Manufacturing
Challenge
High analytics costs, complex tooling, and lack of trust between marketing analytics and financial data
Solution
Migration from Adobe Analytics to a Google-based analytics stack with GA4, BigQuery, Server-Side GTM, and Power BI
The Challenge
The retailer’s analytics ecosystem had evolved in silos, leading to rising costs, fragmented data, and limited trust in insights. As digital and in-store operations grew, the lack of alignment between systems made it increasingly difficult to gain a consistent, end-to-end view of performance and customer behavior.
They faced three core challenges:
1. Rising Costs and Operational Complexity
Adobe Analytics licensing costs were high, and the platform required specialized expertise to maintain, limiting marketing agility.
2. A Persistent Data Trust Gap
Analytics-reported revenue regularly showed a 10–15% variance compared to internal financial records. As a result, leadership viewed third-party analytics data with skepticism and relied primarily on internal BI reports.
3. Disconnected Customer Journeys
Online customer behavior and offline point-of-sale transactions existed in silos, preventing a complete view of the customer journey and limiting accurate marketing attribution. Without a trusted analytics foundation, data-driven decision-making was constrained across marketing, merchandising, and leadership teams.
Rysun’s Solution
Rysun implemented a future-ready analytics architecture designed to improve accuracy, reduce cost, and scale with the business.
1. Architecture & Tracking Foundation
- Deployed Google Analytics 4 (GA4) with Server-Side Google Tag Manager (sGTM) hosted on Google Cloud Platform
- Implemented 15+ standard ecommerce events and ~30 custom business events, including financing applications, store locator interactions, and delivery scheduling
- Established cross-platform parity across the website and progressive web app (PWA)
2. Data Engineering & Integration
- Built a nightly POS data pipeline into BigQuery, syncing offline sales and customer profiles
- Migrated 15 marketing and advertising integrations into the new GTM framework, including major retail media and paid social platforms
- Archived historical Adobe Analytics data into GCP Coldline Storage for reference, avoiding contamination of the new trusted dataset
3. Visualization & BI Enablement
- Delivered six foundational Power BI dashboards powered directly by BigQuery
- Closed historical revenue gaps by including tax, shipping, and order adjustments
- Corrected attribution inaccuracies, particularly across affiliate and paid media channels
4. Accelerated 30-Day Delivery
- Week 1: Architecture setup, access provisioning, solution design, and data layer finalization
- Week 2: GTM and server-side GTM deployment, POS pipeline development
- Week 3: BigQuery modeling and “Gold Layer” views combining online and offline data
- Week 4: End-to-end validation, stakeholder training, go-live, and 30-day hypercare
Benefits
- Reduced Analytics Spend: Eliminated high Adobe licensing costs
- Single Source of Truth: Unified online engagement with offline sales data
- Trusted Executive Reporting: Power BI dashboards aligned with financial systems
- Improved Marketing Attribution: Clearer visibility into channel performance
- Future-Ready Platform: Foundation built for advanced analytics, AI, and predictive modeling
Impact
- Replaced a fragmented and mistrusted analytics ecosystem with a trusted, scalable data foundation
- Restored leadership confidence in marketing and revenue reporting
- Enabled faster, data-backed decisions across marketing and merchandising teams
- Positioned the retailer for future AI-driven personalization and forecasting initiatives