FMCG & Snacks
Increase in distributor SKU penetration
Precision of the next-best-SKU model
About
DFM Foods, maker of the Crax snack portfolio, was running demand planning across fourteen disconnected regional data silos. Reps pushed the same handful of familiar SKUs to every distributor while long-tail demand sat untapped. We built a deep-learning recommender that profiles every distributor and product, then layered a natural-language analytics assistant on top so brand and category teams can interrogate performance without waiting on an analyst.
Industry
FMCG & Snacks
Company size
1,000 – 5,000 employees
Founded
1985
The Company
A snacks portfolio with more distributors than visibility
DFM Foods manufactures and distributes the Crax snack portfolio through a network spanning modern trade, general trade, and regional wholesale channels. Two years of sell-in and sell-through data existed across the business, but it lived in fourteen regional systems that never spoke to each other.
With no unified view, every distributor was sold the same core range regardless of what their local outlet mix, income band, or competitive set could actually move. Fast movers ran out in strong markets while slow movers piled up in others — a pattern worth an estimated ₹30 crore a year in mismatched stock.
The challenge
Reps could only sell what they already knew
Sales reps default to habit. Without a systematic way to surface what a specific distributor was under-buying relative to comparable distributors nearby, the sales conversation stayed anchored to the same five or six hero SKUs on every visit.
The result was two compounding problems: stock-outs on fast-moving SKUs in markets that could have sold more, and dead stock on slower SKUs sitting in warehouses that never had the demand profile to justify the allocation in the first place. Neither the sales team nor category management had the data infrastructure to see this before it showed up as a P&L variance.
The Solution
An embedding model that profiles every distributor and product
We unified two years of sales data across the fourteen silos into a single schema, then trained a deep-learning embedding model that represents every distributor and every SKU in the same latent space — capturing outlet mix, price band, seasonality, and category affinity. Each morning the model scores every distributor-SKU pair and serves a ranked next-best-SKU list through a Power BI app connected directly to SAP HANA, so reps walk into every call with a specific, data-backed recommendation rather than a generic order sheet.
On top of the recommender we deployed a natural-language analytics layer so brand and finance teams could ask direct questions — "why is Crax underperforming in the Northeast this quarter" — and get an answer with root-cause drivers and competitive context in under thirty seconds, work that previously took an analyst three to five days of manual data pulls.
The Results
15-point penetration gain and a ₹32 crore pipeline
Distributor SKU penetration rose 15% within two quarters, with the largest gains among mid-tier distributors who had never been shown their own white space before. The recommender's precision held above 75% in live testing, and field adoption reached 93% — reps kept using it because the recommendations were specific enough to act on, not generic category-level guidance.
The unified data layer surfaced ₹32 crore in previously invisible white-space revenue across the distributor network. On the analytics side, brand-performance queries that used to take three to five days of analyst time now return in under thirty seconds, freeing the category team to spend their time acting on root causes instead of assembling the data to find them.
The recommender does not replace our reps' judgment — it removes the guesswork before the conversation even starts. They walk in already knowing what that distributor is missing.
Head IT DFM Foods (Crax)
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