Actionable Insights

Client Background

A mid-sized retail and logistics company, operating across multiple regions, was struggling to accurately forecast demand and plan resources. The company had diversified into several business lines—retail distribution, last-mile delivery, and wholesale supply—but lacked clarity on which segments were driving the most profitability. Executives relied heavily on high-level financial summaries rather than granular data, which limited their ability to make informed strategic decisions.

Challenge

The client faced three major challenges:

  • Data was fragmented across multiple systems including ERP, CRM, and warehouse management platforms.

  • Profitability was measured only at a consolidated level, obscuring variations between different product categories and service lines.

  • Leadership needed forward-looking insights for resource allocation, scenario planning, and future investment decisions.

The client sought help from a technology consulting firm specializing in data and analytics solutions.

Consulting Firm Approach

The consulting firm initiated the engagement with a discovery phase, followed by data integration, BI reporting development, and advanced forecasting.

  • Discovery & Requirements Gathering
    Consultants conducted workshops with finance, operations, and sales teams to identify key KPIs: gross margin per product line, customer acquisition cost, delivery efficiency, and ROI by service category.

  • Data Integration
    A cloud-based data warehouse was implemented to unify ERP, CRM, and logistics system data. ETL pipelines were built to ensure daily automatic data refreshes.

  • Business Intelligence Reporting
    Interactive dashboards were developed in a leading BI tool. Reports included:

    • Profitability by product category and geographic region

    • Margin contribution per business line

    • Customer lifetime value models

    • Monthly and quarterly forecast comparisons against historical performance

  • Forecasting Models
    The firm leveraged statistical forecasting combined with machine learning models. Logistic regression and time-series analysis were implemented to project demand, highlight seasonal trends, and optimize workforce planning.

Results

Within six months, the client gained a comprehensive view of performance across all business lines:

  • Identified that last-mile delivery contributed only 20% of revenue but nearly 40% of total profits due to higher margins.

  • Discovered underperformance in wholesale distribution, which consumed high operational costs with minimal net contribution.

  • Improved forecasting accuracy by 25% compared to previous manual projections.

  • Enabled leadership to reallocate investment toward the most profitable lines and reduce costs in underperforming areas.

Impact

  • The client achieved a 15% increase in net profit within the first fiscal year of implementation.

  • Strategic planning cycles shortened from quarterly reviews to real-time scenario analyses.

  • Executives reported higher confidence in decision-making due to transparent, data-driven insights.