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Client
UBL
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Service
ML Analytics Platform
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Industry
FMCG & Distribution
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Company Size
50,000+
The client
Managing the health and performance of a national distributor network at scale in India requires more than commercial reporting — it demands the ability to identify which accounts are at risk before the risk becomes visible in the numbers. For a major Indian FMCG enterprise operating across multiple zones and thousands of distributor relationships, the complexity of the pricing, sales, and distribution data landscape had made proactive account management effectively impossible. Decisions were being made on incomplete, retrospective information, and churn was being discovered rather than prevented.
The problem
The business faced a data complexity challenge directly limiting its ability to manage distributor performance proactively across India. High-dimensionality data across pricing, sales volume, geographic trends, and account behaviour was generating too much information to analyse manually and too little actionable intelligence to guide decision-making. The result was reactive account management, avoidable churn, and missed budget and pricing optimisation opportunities.
- High Data Complexity Obscuring Key Performance Drivers Across India — Data across pricing structures, sales performance, distribution patterns, and account behaviour across India was too complex and high-dimensional for manual analysis to extract meaningful insights. Without a mechanism to identify which variables most strongly predicted account performance, commercial decisions were based on assumptions rather than evidence.
- Limited Ability to Take Proactive Action on At-Risk Accounts — The absence of predictive analytics meant underperforming and at-risk distributor accounts across India were typically identified through retrospective reporting — by which time the relationship damage was already done. Without early warning signals, interventions were reactive rather than preventive, and avoidable churn was a regular feature.
- Inability to Predict Future Trends Across Indian Geographies — Regional differences in account behaviour, pricing sensitivity, and churn patterns were difficult to identify without sophisticated multi-variable analysis across India’s diverse geographic segments. This limitation prevented the business from tailoring commercial strategies to the specific dynamics of each zone.
OUR SOLUTION
Worxwide deployed an ML-based Factor Analysis platform to extract and rank the most critical drivers of distributor account performance across Indian zones — identifying the variables most strongly predicting commercial outcomes and churn risk. Predictive churn modelling and multi-variable segmentation were enabled to surface at-risk accounts before visible performance deterioration. AI-powered dashboards and visualisation layers were built to provide commercial and zone managers with actionable intelligence on trends, risk signals, and optimisation opportunities.
- ML Factor Analysis to Identify Critical Performance Drivers in India — Machine learning-based factor analysis was applied to the full multi-dimensional dataset — spanning pricing, sales volume, promotional response, and account behaviour across Indian geographies — to rank the variables with the strongest predictive power for account performance and churn.
- Predictive Churn Modelling and Account Segmentation Across Zones — Predictive models were built to score distributor accounts by churn risk, enabling commercial teams across India to identify and prioritise at-risk relationships before visible deterioration. Multi-variable segmentation grouped accounts by risk profile, enabling tailored intervention strategies for different account types.
- AI-Powered Dashboards for Zone and Account Intelligence Across India — Interactive dashboards surfaced account health trends, risk signals, and optimisation opportunities at zone and account level across India — giving commercial managers the visibility needed to make proactive decisions on resource allocation, pricing adjustments, and account-level intervention strategies.
Our work in action
The Impact
17% Reduction in Distributor Churn Risk Across India
Predictive churn modelling and proactive intervention capabilities reduced at-risk account churn by 17%, preserving distributor relationships and the revenue associated with them — demonstrating a clear commercial return on the analytics investment across the Indian distribution network.
Faster Decision-Making Through Data-Driven Prioritisation
By surfacing the most critical performance drivers and flagging at-risk accounts proactively, the platform accelerated commercial decision-making — replacing the slow, manual analysis that had previously delayed responses to emerging account health challenges across India.
Improved Budget and Pricing Optimisation Across Indian Zones
Regional intelligence surfaced through multi-variable zone analysis enabled more precise budget allocation and pricing strategy tailoring — ensuring commercial investment was directed toward the opportunities and interventions most likely to drive distributor performance and loyalty across each Indian geographic segment.
