Havish M Consulting
Customer Case Study · Trade Spend ROI
Beverages (FMCG) × Havish M Consulting

Every rupee of trade spend, traced to the cases it bought

Distributor and retailer schemes are among the largest lines in the P&L, and the hardest to prove. HMC built a trade-spend control room for a beverage major's finance team: it measures the extra cases each scheme really created, simulates new schemes before launch, checks every claim before payout and forecasts the month-end accrual.

SectorBeverages (FMCG) FunctionFinance · Commercial Finance ThemeTrade Spend ROI · Scheme Simulation
Return on trade spend
0× margin per ₹
▲ from about 0.9×
Spend moved to schemes that pay back
₹0crore
▲ from low- to high-ROI schemes
Claim leakage
0% of payout
▼ from about 6%
Month-end scheme accrual accuracy
±0%
▼ from about ±18%

Figures indicative; validated metrics available on request.

The Challenge

Big spend, thin evidence

Finance could say what was paid. It could not say what the payment bought.

Volume, not uplift

Schemes were judged on total cases sold, including cases that would have sold anyway.

Paying for the baseline

Big outlets crossed slabs on natural growth and collected payouts for doing nothing different.

Claims on trust

Thousands of distributor claims a month; only a small sample checked by hand.

Accrual by guesswork

Month-end provisions were trued up a quarter later, swinging margins.

HMC Approach

From payout ledger to profit evidence

Built with commercial finance, sales operations and the distributor management team.

Select a step to see what happened.

Discover · Follow the money

Mapped every step from the annual trade budget to the payout.

  • Scheme circulars, claims, distributor sales and outlet master.
  • Where money leaked: duplicates, ineligible outlets, slab errors.
  • How accruals were estimated at month end.

Deliverable: Trade spend flow map and leakage baseline.

Design · Measure the extra cases

Test-and-control outlet matching as the standard for ROI.

  • Matched control outlets by size, channel, pack mix and town.
  • ROI as incremental margin per rupee of spend.
  • Six automated claim checks agreed with sales.

Deliverable: ROI method and claim rule book.

Build · Control room

Dashboard, simulator, claims engine and accrual model.

  • Scheme ROI by brand, pack, channel and state.
  • Simulator that predicts uplift and payout before launch.
  • Accrual forecast with confidence bands for close.

Deliverable: Trade spend control room.

Adopt · Finance signs off schemes

No new scheme without a simulated ROI.

  • Claims checked in full before payout.
  • Accruals booked from the model on day 2 of close.
  • Quarterly ROI review with sales leadership.

Deliverable: Pilot in two regions.

Scale · All regions, all channels

Traditional trade, modern trade and HoReCa.

  • Budget reallocated from low- to high-ROI schemes.
  • Distributor scorecards on claim quality.
  • Annual trade plan built bottom-up from simulated schemes.

Deliverable: Roll-out plan.

The Solution

Open the trade spend control room

Synthetic schemes, outlets and claims for illustration. Follow the money through the flow, see which schemes paid back, design one of your own, check claims and close the month.

✦ Add GenAI to the control room

Optional. Every module runs on sample data and built-in rules. Add your OpenAI key to read scanned distributor claims and to write the scheme recommendation and the close note for the CFO.

Rules
Your key goes only from this browser to OpenAI.
01 · Follow the money

How a rupee of trade spend travels, and where it leaks

Each moving dot is money. Grey dots buy cases that would have sold anyway; red dots leak through bad claims. Switch to After to see the claim gate and ROI loop at work. Select any step for detail.

Trade spend flow · annual budget ₹850 crore (illustrative)
02 · Scheme ROI dashboard

Which schemes bought extra cases

Each bubble is a scheme: spend across, ROI up, size by extra cases. Select a bubble to see its outlets against matched control outlets week by week. The map shows ROI by state.

Scheme portfolio

Quadrant bubble chart: spend × ROI · bubble size = incremental cases · line = break-even (1×)

Test vs control

Difference-in-difference: weekly cases per outlet, scheme outlets against matched control outlets

ROI by state

Choropleth: incremental margin per ₹ of trade spend

Where the money went

Scheme spend split: extra cases, the baseline paid for, and pull-forward

Synthetic schemes and sales. Scheme and brand names are generic; figures are illustrative.

03 · Scheme simulator

Design a scheme, watch 1,500 outlets respond

Set the slabs and payouts. Every dot is an outlet deciding whether the extra effort for a slab is worth it. Grey outlets get paid while adding less than 4% beyond their normal growth, so most of their payout buys cases they would have sold anyway. Run it to see cost, extra cases and ROI.

Rules

Start from

Outlets in scope

Outlet response

Unit chart: each dot is an outlet, dropped into the slab it chose · red = grew 4% or more beyond normal · grey = paid but added little · blue = cooler visibility only

Response curve

Scale every payout up or down: spend against extra cases, with the ROI line

ROI range

60 simulation runs with different outlet behaviour

Run a simulation, then draft the recommendation.

Outlet behaviour is a simple economic model: each outlet weighs the payout against the effort of extra growth. Gross margin assumed ₹38 per unit case.

04 · Claims check

Every distributor claim, checked before payout

Six checks run on every claim against distributor sales, the outlet list and scheme rules. The UpSet chart shows which problems occur together; select a bar to filter the claims.

Rules
Which checks fail together

UpSet chart: bars = claims with exactly this set of flags · dots = the flags

ClaimDistributorSchemeClaimedAllowedStatus
Check a new claim

Type it in, or upload a scanned claim (needs a key)

05 · Month-end accrual

Book the scheme provision with confidence

Liability builds through the month as outlets sell; claims arrive weeks later. The model forecasts the month's liability with a range, so finance books the provision on day 2 of close instead of truing it up next quarter.

Rules
Scheme liability build-up

Forecast with an 80% range, earned to date and claims received · dashed = provision to book

Journal entryRegionDebit ₹Credit ₹
Budget used by region

Activity rings: month's scheme budget consumed · outer ring North

The note explains the provision, the range and what drives it.

Impact Dashboard

More cases from every rupee

Measuring uplift, simulating before launch and checking every claim moved money to schemes that pay back.

Return on trade spend by quarter

Pictorial chart: each bottle filled to its ROI (full = 2×)

How payback builds up

Cumulative payback curve: margin from extra cases against spend, schemes ranked by ROI · dashed = break-even

Figures indicative; validated metrics available on request.

Before vs After

How trade spend is managed now

DimensionBeforeAfter
Scheme successTotal cases soldExtra cases against matched control outlets
New schemesCopied from last yearSimulated for cost, uplift and ROI before launch
Baseline subsidyInvisibleMeasured per scheme and designed out
Claims5% sample checked by handEvery claim through six automated checks
Month-endProvision guessed, trued up a quarter laterForecast with a range, booked on day 2
BudgetSpread by last year's patternMoved to schemes and states that pay back
Tools & Tech Used

The control room stack

Python · pandas outlet-week sales model Matched control groups difference-in-difference ROI Simulation outlet response to slabs Rules engine six claim checks OpenAI GPT (vision) scanned claims and notes Power BI finance and sales views
Key Takeaways

What any consumer company can borrow

01

Pay for the extra case

Measure against a matched control; total volume flatters every scheme.

02

Simulate before you announce

Slab design decides how much of the payout subsidises the baseline.

03

Check every claim

Automated checks cost less than the leakage a sample misses.