Trade Schemes Gone Wrong: When Great Schemes Create Terrible Returns

When Great Schemes Create Terrible Returns

Quarter-end is approaching. Your brand principal announces a scheme: "Buy 10, Get 2 Free" for retailers. Your salesmen push hard. Primary sales numbers look fantastic. Bonuses are earned.

60 days later, those "free" cases are coming back—expired, unsold, your problem.

This is the trade scheme trap, and it costs FMCG distributors crores every year.

The Anatomy of Scheme-Induced Returns

How it happens:

• Brand announces aggressive scheme (typically quarter-end)
• Sales team pushes stock to hit targets
• Retailers accept because "free goods"
• Stock exceeds retailer's actual selling capacity
• Products age on shelf
• Return window opens
• Expired/near-expiry stock comes back
• Distributor absorbs loss (or fights with brand)

The math that nobody does:

Retailer normally sells 50 units/month of Product X. Scheme offers 12 free on 100. Retailer buys 100 to get the deal.

• Normal 2-month sales: 100 units ✓
• Actual stock received: 112 units
• Shelf life: 4 months remaining
• Reality: 12 units will expire before sale

The scheme created a 10.7% return liability at the moment of sale.

Why Retailers Accept Schemes They Can't Sell

Short-term thinking:

• "Free goods = free profit"
• "I'll figure out how to sell it"
• "Competition is buying, I should too"

Relationship pressure:

• Salesman is persistent
• Don't want to damage relationship
• "Maybe sales will pick up"

Poor visibility:

• Don't know their actual velocity
• Can't calculate sell-through rate
• No system to flag over-ordering

The salesman's role:

• Incentivized to push volume
• Bonus depends on primary sales
• Returns are "later" problem

The Hidden Costs

For distributors:

• **Direct return costs:** Product value lost to expiry
• **Handling costs:** Logistics of collecting returns
• **Storage costs:** Warehousing returned stock
• **Relationship costs:** Friction with retailers and brands
• **Working capital:** Cash tied up in unsold inventory

For retailers:

• **Shelf space:** Blocked by slow-moving scheme stock
• **Cash flow:** Money locked in inventory
• **Expiry risk:** They bear some loss too
• **Credibility:** Customers see expired products

For brands:

• **Return claims:** Distributors filing for compensation
• **Market reputation:** Expired products in market
• **Data pollution:** Primary sales don't reflect actual demand
• **Planning errors:** Forecast based on inflated sales

Scheme Types and Return Risk

Low return risk:

• **Consumer-facing schemes:** "₹5 off" that pulls demand
• **Small bonus quantities:** 1 free on 10 (10% excess)
• **Short-dated products acknowledged:** Retailer knows and plans

Medium return risk:

• **Trade discounts:** Cash benefits that don't inflate stock
• **Display incentives:** Payment for shelf presence
• **Credit extensions:** More time to pay, same quantity

High return risk:

• **High bonus ratios:** 2 free on 10 (20% excess)
• **Slab schemes:** "Buy 50 to unlock better rate"
• **Quarter-end pushes:** Artificial urgency, artificial orders
• **New product launches:** Unproven demand + aggressive targets

Very high return risk:

• **Combo schemes:** "Buy A, get B free" (B may not sell)
• **Category schemes:** "Buy ₹50K across category" (forces unwanted SKUs)
• **Channel stuffing:** Systematic over-pushing to meet corporate targets

The Visibility Solution

The fundamental problem is that nobody sees the return coming until it's too late.

What distributors need:

• **Retailer velocity data:** What actually sells vs. what's ordered
• **Stock aging visibility:** How long stock sits at each retailer
• **Scheme impact tracking:** Which schemes create returns
• **Early warning alerts:** Stock aging beyond normal velocity

What retailers need:

• **Order recommendations:** "Your normal purchase is 50, scheme order is 100—are you sure?"
• **Velocity history:** See your own selling patterns
• **Expiry alerts:** What's aging in your store
• **Scheme value calculator:** "Free goods" vs. potential expiry loss

What brands need:

• **Secondary sales data:** What actually sold through
• **Return prediction:** Which retailers will return which stock
• **Scheme effectiveness:** ROI including returns
• **Territory analysis:** Where schemes work vs. create problems

Scheme Discipline Framework

Before accepting scheme:

□ Calculate retailer's actual monthly velocity

□ Determine scheme quantity vs. 60-day sell-through capacity

□ Identify shelf life of scheme products

□ Flag if scheme quantity > 1.2x normal sell-through

□ Get retailer acknowledgment of quantities

During scheme period:

□ Monitor weekly sell-through at retailer level

□ Alert if stock aging beyond expected rate

□ Proactive communication with slow-moving retailers

□ Mid-scheme correction if needed (swap SKUs, additional support)

After scheme:

□ Calculate actual secondary sales vs. primary

□ Track returns by scheme

□ Document scheme profitability including returns

□ Feed back to brand on scheme design

The Conversation Nobody Wants to Have

With your salesmen:

"Your primary numbers look great. Your return numbers look terrible. Your net contribution to the company is [X]. Let's look at which retailers you're pushing to, and whether they can actually sell what you're selling them."

With your retailers:

"I know the scheme is attractive, but your history shows you sell 40 units/month of this product. Ordering 100 to get the bonus will create 20 units of expiry risk. Can we do 60 instead?"

With your brand:

"Here's the data from last quarter's scheme. Primary sales: ₹50L. Returns: ₹8L. Effective scheme cost: 16%, not the 10% you budgeted. Let's design schemes that move secondary, not just primary."

These conversations are uncomfortable. They're also the difference between profitable distribution and margin bleeding.

Technology-Enabled Scheme Management

At order time:

• System calculates retailer velocity
• Flags orders exceeding 60-day sell-through
• Shows scheme value vs. expiry risk
• Requires override approval for excess orders

Post-delivery:

• Tracks stock aging at retailer
• Alerts when scheme stock isn't moving
• Predicts return probability
• Suggests intervention actions

At scheme close:

• Calculates true scheme ROI
• Attributes returns to specific schemes
• Scores schemes by effectiveness
• Provides data for brand negotiation

Over time:

• Builds retailer velocity profiles
• Identifies scheme-appropriate vs. scheme-risky retailers
• Optimizes order quantities automatically
• Reduces return rate quarter over quarter

The Brand Negotiation

Armed with data, you can have a different conversation with brands:

Instead of: "We're getting too many returns"

Say: "Your Q3 scheme had a 14% return rate. Here's the data by retailer, by SKU, by batch. The scheme design pushed 22% more stock than retailer velocity could absorb. For Q4, we propose [alternative scheme structure] that matches actual secondary capacity."

Brands don't want returns either. They want to know why schemes don't work. Data gives you credibility to reshape scheme design.

Scheme Participation Strategy

Participate fully:

• Schemes on products with proven demand
• Retailers with matching velocity
• Quantities within sell-through capacity

Participate partially:

• Take partial quantities
• Focus on high-velocity retailers only
• Skip low-velocity outlets for this scheme

Decline or negotiate:

• Schemes with >15% expected return rate
• Products with track record of returns
• Quarter-end pushes that feel like channel stuffing

Counter-propose:

• Different scheme structure (trade discount vs. free goods)
• Smaller bonus quantities
• Consumer-facing schemes instead of trade schemes

The Bottom Line

Trade schemes are supposed to accelerate sales. When they accelerate returns instead, everyone loses.

The fix isn't to avoid schemes—it's to match scheme quantities to actual retailer capacity. That requires knowing velocity at the retailer level, which requires tracking and systems.

Every "free" case that comes back expired wasn't free. It was margin you earned on the primary sale and then lost on the return.

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