Knowledge Hub/Case Study
    Case StudyRetail

    How Lilla S.p.A. Built a Unified Data & BI Platform for Retail, Finance, and Store Performance

    January 20258 min read
    Client: Lilla S.p.A.
    Industry: Multi-brand Fashion Retail
    Brands: 5,500+ across clothing, accessories, home textiles
    Geography: Italy, network of physical stores
    Challenge: Fragmented data across retail, finance, and store operations with no centralized reporting
    Solution: Centralized data warehouse on Azure SQL with automated Power BI dashboards
    Outcome: Unified, real-time visibility across all business areas with automated daily reporting

    Context

    Lilla is an Italian multi-brand fashion retail company operating a network of physical stores. Its offering spans clothing, accessories, home textiles, and lifestyle products. The company manages:

    • Thousands of brands and SKUs
    • Multiple seasonal collections
    • Complex supplier and payment structures
    • Store-level performance and foot traffic

    As the business grew, Lilla's leadership recognized the need for a centralized, automated approach to business intelligence: one that could bring together operational and financial data into a single, reliable source of truth.

    The Challenge: Fragmented Data Across Every Business Function

    Disconnected Systems and Manual Reporting

    Lilla's data was spread across multiple systems, point-of-sale terminals, ERP, financial software, and traffic counters, with no integration between them. Key challenges included:

    • No unified view of sales, margins, and inventory across stores
    • Finance teams relying on manual Excel consolidation for payables and receivables
    • Store performance data (traffic, conversion) siloed from commercial data
    • Seasonal planning based on intuition rather than data-driven forecasting

    Limited Visibility for Decision Makers

    Leadership lacked the tools to answer fundamental questions about business performance: which stores were truly profitable, which brands drove margin, and where operational inefficiencies were hiding.

    The Solution: Centralized Data Warehouse with Automated Analytics

    Data Architecture

    Expecta designed and built a centralized data warehouse on Azure SQL Server with a layered structure:

    • Landing layer: raw data ingestion from source systems
    • Staging layer: business logic and transformations
    • Production layer: star schema for optimized reporting

    Automated Analytics & Reporting

    Retail & Sales Analytics

    Comprehensive dashboards covering sales by brand, category, store, and time period. Margin analysis, sell-through rates, and inventory turnover, all updated automatically.

    Finance & Payables Analytics

    Automated accounts payable and receivable reporting, aging analysis, and cash flow visibility. Replaced manual Excel processes with real-time dashboards.

    Store & Traffic Analytics

    Integration of foot traffic data with sales metrics to calculate conversion rates, average transaction values, and store-level benchmarking.

    The Results: Complete Business Visibility

    With Expecta, Lilla achieved:

    • A single source of truth across retail, finance, and store operations
    • Automated daily reporting replacing hours of manual Excel work
    • Store-level profitability analysis for the first time
    • Brand performance tracking across 5,500+ brands with margin visibility
    • Traffic-to-conversion analytics connecting footfall to sales
    • Cash flow visibility with automated aging and payment tracking
    • Scalable architecture ready for new stores and data sources

    Lilla now operates with enterprise-grade analytics capabilities, enabling data-driven decisions at every level of the organization.

    "Expecta transformed how we understand our business. For the first time, we have a single view across all our stores, brands, and financial operations."

    — Management Team, Leadership – Lilla S.p.A.

    Why this matters for Retail Organizations

    This project demonstrates how a multi-store retail operation can gain unified visibility without replacing existing systems:

    • System integration without disruption: connecting POS, ERP, and finance without changing existing workflows
    • Store-level intelligence: understanding true profitability at the individual store level
    • Automated financial reporting: eliminating manual consolidation while improving accuracy
    • Embedded partnership: ongoing BI support that evolves with the business

    By building a centralized data infrastructure, Lilla gained the clarity and control needed to make confident, data-driven decisions across their entire retail network.

    Ready to Build Your Own Success Story?

    Book a 30-minute discovery call. We'll assess your data landscape and show you how Expecta can deliver the same results for your organization.