Stabilizing existing data analytics platform for increased revenue for a leading FMCG company
A leading FMCG company operating across the country with strong focus on digital transformation and data-driven decision-making leveraged data analytics platform as a central engine for reporting, monitoring, and AI/ML-driven decision-making. While it supported over eight business critical cockpits and five AI/ML models, it was in bad shape and faced fundamental challenges such as design limitations, high infrastructure costs, and low user adoption, hindering its effectiveness.
About
While it supported over eight business critical cockpits and five AI/ML models, it was in bad shape and faced fundamental challenges such as design limitations, high infrastructure costs, and low user adoption, hindering its effectiveness.
These issues were further compounded by governance gaps, operational inefficiencies, and inconsistent performance, leading to frequent breakdowns across critical tools. Additionally, growing data quality challenges made it increasingly difficult to deploy, scale or operationalize AI/ML models with confidence. Despite rising business demand, the platform was unable to keep pace, highlighting the urgent need for transformation to enable scalable, reliable, and user-friendly analytics. These challenges deeply impacted several core functions such as sales, supply chain, finance and marketing, by delaying access to reliable data and insights. It also hampered performance tracking, demand forecasting and strategic planning.
The Deloitte way
The approach included both foundational and AI-led approach. In the first phase that involved stabilising the analytics platforms, we carried out tasks like fixing broken pipelines, addressing data quality challenges, and implementing governance mechanisms to manage demand effectively. We rebuilt cockpit logic for select dashboards and evaluated critical KPIs to align with business expectations. We also optimised the codes and automated several manual processes, improving maintainability and efficiency.
In the second phase of AI-led solution implementation, i.e. the post stabilization phase, we rolled out a portfolio of targeted cockpit enhancements and AI/ML-led interventions to drive business growth, improve productivity, and deliver measurable gains across functions.
So, what did we do?
We stabilized the existing data analytics platform through a structured, phased approach focused on restoring system reliability and data integrity. Once that was achieved, we further transformed it using AI and Generative AI solutions.
Drilling in success
In the first phase, we successfully stabilized the client’s data analytics platform, creating a one-stop data and analytics solution for improved business outcomes. We are currently managing over 16 cockpits and 10+ AI/ML models.
- Cost savings and risk mitigation: Delivered savings of approximately INR 2 crore in obsolescence costs and prevented lost sales by creating a robust data driven insights to monitor inventory and mitigate supply chain and obsolescence risks for raw and packaging materials.
- Performance improvement: The solution automated the KPI reporting and performance tacking resulting in saved manual efforts by over 42,000 hours via proprietary dashboard. This was done to transform their nutrition business.
In the later phases of strategic AI/ML interventions, which varied across the business lines, the client was able to see benefits in terms of improved performance tracking and better revenues.
- Revenue growth: 2-5% increase in revenue target achieved by providing data driven strategic insights for retailer selection in promotions and accurate sales target allocation
- Retailer adherence improvement: Increased retailer adherence to must-sell SKUs from 45% to 51%, while reducing manual effort by 51%, through data driven analytics analysing purchasing patterns and sales trends for optimal pack recommendations.
- Optimised retailer selection: Enhanced retailer selection for premium product launches using a sophisticated geo-spatial data
Building on the momentum, we have now taken a leap forward to explore how business users extract insights from data. We have developed an Agentic AI solution that serves as a single interface to query KPIs and surface insights across multiple datasets within the sales function — all through natural language. It is already showing remarkable results in terms of efficiency improvement. What used to take hours or even a couple of days now takes seconds. The solution is already empowering over 30 business users, cutting down effort, speeding up decision-making, and driving faster, insight-led actions.
