How a ₹200 Cr Indian exporter identified ₹44 Cr in three-year revenue uplift — without a single new customer.

A successful exporter with a structural pricing problem it could not see

The company had been exporting agri-commodity products for over a decade. Revenue was above ₹200 Cr. The business had a credible manufacturing footprint, strong certifications, and relationships with buyers across more than 40 countries. By most measures, it was a success.

Pricing, however, was set the way most Indian exporters set pricing: by reference to what competitors seemed to charge, adjusted by buyer relationships, and validated by the fact that orders continued to arrive. There was no economic model behind the price. No analysis of what each market would actually pay. No view of whether the company was capturing the value it was generating.

The management team had a vague sense that they might be leaving something on the table in some markets. They had no idea how large that gap was, where it was concentrated, or what was causing it. They had never been given the tools to see it.

Three analytical workstreams, three weeks, full transparency

The engagement began with a structured economic diagnostic: mapping the company's realised export ASP (average selling price per kilogram) against publicly available trade data for each destination market, then building a market attractiveness framework that ranked opportunity by economics rather than by relationship history.

The work used three data sources: the company's own export invoicing and volume data, UN Comtrade import unit value data by HS code and destination, and ITC Trade Map for competitive dynamics and market import growth. No expensive proprietary data. No assumptions that could not be verified. All sources cited, all methodology documented.

Workstream 1 — ASP gap analysis

For each of the company's 40+ active destinations, we calculated the gap between India's realised export ASP and the market average import price across all origins. This gave us two numbers for each market: where India sits versus the global average, and where this company sits versus India's own average. The gap was larger and more consistent than the management team expected.

Directional figures for illustration. Actual values withheld at client request.

Workstream 2 — Market attractiveness ranking

We ranked seven priority markets across five dimensions: import potential (volume and growth), India's current competitive position, ASP gap versus market average, route economics (freight and duty), and competitive intensity from other origins. The output was a single-page market priority view with a clear recommended sequence for pricing and positioning investment.

Workstream 3 — Root cause analysis and pricing architecture

The ASP gap had four structural causes visible in the company's data: commodity-grade positioning across all markets regardless of product specification; pack format limitations preventing access to premium retail channels in high-ASP markets; certifications already held but not reflected in pricing architecture; and no segmentation between the four distinct product grades being exported under a single effective price.

The pricing architecture recommendation restructured the offering across four product families — standard commodity, specification-led industrial, certified premium, and high-spec retail-ready — each with its own pricing logic, target market profile, and minimum ASP. This was not a new product development exercise. The four families already existed in the company's product range. They had just never been priced or positioned differently.

Three scenarios. All three based on existing volumes.

The scenario revenue model was built on the company's own volume data, not on growth assumptions. Each scenario represented a different level of ASP recovery and market mix improvement, applied to existing export volumes with no new customer acquisition required.

The moderate scenario of ₹44 Cr was considered the planning base because it did not require new customers, did not assume optimistic buyer behaviour, and could be delivered primarily through pricing and positioning changes already within the company's control. It assumed a two-year implementation timeline for the pricing architecture and a selective expansion into two high-ASP markets in year two.

The decision framework is owned by the client. No ongoing dependency.

The full engagement — from initial diagnostic to model handover — took three weeks. Deliverables included the market attractiveness view across seven priority markets, the interactive scenario revenue model built on the client's own data, the pricing architecture recommendation across four product families, and a first-year implementation roadmap with decisions sequenced by risk and payback.

The management team reviewed the scenario model in a board meeting. The ₹21/kg gap in their largest market — a number that would not have been visible without the trade data analysis — changed the conversation. Pricing had previously been treated as a sales function. It was redesignated as a strategic function with board-level visibility from that point.

The model, the market ranking, and the pricing architecture were handed over in fully documented form. The operations team was trained on updating the ASP tracking quarterly using public data sources they could access independently. There is no ongoing retainer. The work is owned.

If you export to five or more markets and have not mapped your ASP gap

This engagement is representative of a pattern we see across Indian agri-commodity, food processing, and light industrial exporters. The ASP gap is consistent, structural, and fixable. It is not a market problem. It is a decision problem — and decisions only change when the economics become visible.

The analysis required no proprietary data, no expensive market research, and no consultants embedded in the business for months. It required publicly available trade data, structured economic analysis, and a willingness to look at the pricing question through an economic lens rather than a relationship lens.

The diagnostic that surfaces this takes 30 minutes. The analysis that quantifies it takes three weeks. The value it unlocks compounds for years.