Agri-Inputs & Crop Science
Base-case annual revenue gap closed
Actionable, quantified levers identified per branch
About
A Bangalore-based agri-input distributor had two branches selling into similar catchments but performing very differently, and no reliable way to say why. We used causal — not correlational — analytics to isolate the real drivers of the gap and prioritise branch-level fixes with confidence instead of debate.
Industry
Agri-Inputs & Crop Science
Company size
100 – 500 employees
Founded
2016
The Company
Two branches, similar markets, very different results
The company distributes crop-protection and agri-input products through a regional dealer network. Two of its branches served comparable catchments in terms of cropping pattern and dealer density, yet one consistently outperformed the other by a wide margin — a gap leadership could see in the P&L but not explain.
Regression-style analysis kept mis-reading confounders — season, monsoon timing, local price competition — as causes, leading to interventions that looked reasonable on paper and made no measurable difference on the ground.
The challenge
Correlation kept pointing leadership at the wrong lever
Standard trend analysis showed the underperforming branch had more dealers and more customers than the leading branch, which on its face suggested a coverage problem — the opposite of what was actually happening. Without a causal framework, the business kept adding dealer touchpoints in the underperforming branch and getting no revenue response.
Inventory turnover, dealer capability, and customer mix were all suspected contributors, but nobody could quantify how much of the gap each one actually explained, or in what order to fix them.
The Solution
A causal map validated across four independent methods
We built a causal map of how inventory discipline, dealer capability, and customer-value mix drive branch revenue, then estimated effect sizes, confidence intervals, and significance using an 18-month panel with explicit corrections for seasonality and product mix. Econometric modelling, a machine-learning ensemble, direct benchmarking against the leading branch, and a causal network built with DoWhy and EconML were run independently and converged on the same base-case gap.
The analysis found the underperforming branch was carrying far more low-value, low-frequency dealers than the leading branch, which was quietly eroding average revenue per dealer even as headline dealer count looked healthy — the opposite conclusion a simple correlation would have suggested.
The Results
A quantified, sequenced fix instead of a debate
The four-method analysis converged on a base-case revenue gap and a synergy-adjusted expected value once inventory, dealer capability, and customer-mix fixes were sequenced together — inventory first, since it improves both cash position and service level simultaneously, followed by dealer development, followed by a deliberate shift toward higher-value customer accounts.
Target branches recorded 6–8% EBIT gains within two quarters of implementing the sequenced playbook, and leadership now has a repeatable, auditable method for diagnosing any underperforming branch rather than relitigating the same debate every quarter.
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