Spreadsheets intimidate people. This chapter is not a spreadsheet — it is a story told in rupees about where your household money already goes, and what happens when you route it through Gyftpe first. By the end, you will have a personal number worth more than any single bank offer.
Start with truth, not ambition
Open your bank app. Filter the last 90 days of UPI and card debits. Tag each line:
- Food delivery (Swiggy, Zomato, Dominos)
- Fashion (Myntra, Ajio, Nykaa)
- Ecommerce (Amazon, Flipkart)
- Groceries (Blinkit, BigBasket, Zepto if listed)
- Travel (MakeMyTrip, Uber, Cleartrip)
- Entertainment (BookMyShow, Netflix if gift cards available)
Annualise: (90-day total ÷ 3) × 12. That is your honest baseline — not what you wish you spent.
Sample household model (Layer 1 only)
Assume this family actually buys discounted Gyftpe balance for each row — not direct UPI.
| Category | Annual spend | Gyftpe discount | Locked savings |
|---|---|---|---|
| Food delivery | ₹96,000 | 4% | ₹3,840 |
| Fashion | ₹36,000 | 5% | ₹1,800 |
| Ecommerce | ₹60,000 | 3.5% | ₹2,100 |
| Groceries | ₹48,000 | 5.5% | ₹2,640 |
| Travel | ₹24,000 | 3% | ₹720 |
| Entertainment | ₹12,000 | 4% | ₹480 |
| Total | ₹2,76,000 | ₹11,580 |
₹11,580 without counting Big Billion Days, Prime Day, bank offers, or credit card rewards. That is one vacation flight, a year of OTT subscriptions, or a meaningful SIP top-up — from behaviour change alone.
Conservative vs aggressive profiles
Conservative saver — only categories with steady 3%+ discounts and spend certainty:
- Food + one ecommerce wallet
- Monthly top-up, 4-week float
- Typical outcome: ₹3,000–₹8,000/year
Moderate saver — top 4 categories annualised:
- Adds fashion + groceries
- Preloads before known sale windows
- Typical outcome: ₹8,000–₹15,000/year
Aggressive optimiser — all eligible spend + stacking (Modules 3 & 5):
- Gift card + sale + card on remainder
- Typical outcome: ₹15,000–₹30,000/year (high effort)
Academy goal: move everyone from ₹0 → conservative first. Optimisation is optional.
Build your model in four steps
Step 1 — Export and tag
Most banking apps export CSV. If not, manually sum top merchants for 30 days and multiply by 12.
Step 2 — Map merchants to Gyftpe brands
| You spent at | Buy on Gyftpe |
|---|---|
| Swiggy | swiggy |
| Amazon | amazon-pay |
| Myntra | myntra |
Full mapping: 50 brand playbooks.
Step 3 — Plug discounts from live pages
Do not guess 6% everywhere. Check today’s pricing. Use conservative numbers if unsure.
Step 4 — Set a monthly ritual
Example: first Tuesday → buy Swiggy + Amazon float; before major sales → add Flipkart/Myntra.
What Layer 2 and 3 add (preview)
This module stops at Layer 1 locked ROI. Stacking adds:
- Layer 2: Merchant sales (20–70% off fashion — separate from gift card discount)
- Layer 3: Bank/card on residual payment
A ₹36,000 fashion spend with 5% preload plus 30% average sale discount is not 5% + 30% = 35% — maths is messier. Module 3 teaches hygiene.
Sensitivity: what moves the needle most?
| Lever | Impact |
|---|---|
| Fixing food delivery routing | High — frequent, predictable |
| Adding Amazon/Flipkart wallet | High — large ticket sizes |
| Chasing extra 0.5% discount | Medium — compare ROI on large face |
| Grey-market “deals” | Negative — scam risk |
Shareable takeaway
When someone says gift cards aren’t worth the effort, ask: “What’s your annual Swiggy + Amazon number?” Multiply by 4%. That single conversation converts sceptics faster than theory.
Your homework
- Complete the 90-day tag exercise
- Fill the table with your numbers (not the sample)
- Run sidebar calculator on your largest category
- Buy one preload this week — smallest step that proves the model
Next: Stacking Strategies →
Frequently asked questions
We share one Swiggy account — how to model?
Use household combined spend; one wallet balance anyway.
My spend is irregular — skip annual model?
Use rolling 4-week float instead of annual preload. Same ROI, less float risk.
Are these savings taxable?
Personal spend optimisation — not income. Consult a CA for business/gifting edge cases.