Data Platform Strategy

From Checkout Tool to Intelligence Layer · Shopper Profiles · Credit Scoring · Market Intelligence · April 2026

Every

Checkout = a Data Point

1 Phone

Across 50 Stores = Financial Identity

Cost of Data Acquisition

The Thesis

CashierLogic sits at the richest data intersection in e-commerce — between the shopper and the payment. Every checkout generates intent signals, behavioral signals, identity signals, and reliability signals. This data compounds across merchants. At scale, it becomes a financial identity layer.

We don’t scrape data. We don’t buy data. We don’t partner for data. We are the checkout. The data flows through us as a natural byproduct of rendering the form, processing the input, and completing the order. Our cost of data acquisition is zero — every merchant who installs CashierLogic feeds the network for free.

The Flywheel

Merchant installs free CashierLogic. Zero barrier. Cart drawer, checkout overlay, analytics dashboard — all free up to 100 orders/month.

Shoppers checkout → profiles created automatically. Phone number, address, payment preference, device, referral source, discount behavior — all captured as part of the normal checkout flow.

Profiles compound across the network. Same phone at Store A and Store B? Linked. COD delivered at Store A? Reliability score updated for every merchant.

Intelligence improves for every merchant. Returning shoppers convert faster (pre-filled data). COD decisions get smarter (cross-store signals). Segments get richer (network-wide behavior).

Data becomes a standalone asset. Credit scoring for BNPL. Risk APIs for logistics. Demand intelligence for brands. The checkout is the observation point — the intelligence derived from it is the product.

The Data Advantage

What We See That Nobody Else Does

Data	Shopify	Payment Gateway	GoKwik	CashierLogic
Cart contents	✓	✗	Partial	✓
Checkout behavior (step timing, hesitation)	✗	✗	Partial	✓
Phone + address + payment pref	✗	Partial	✓	✓
Cross-store shopper identity	✗	✗	✓ (120M)	Building
COD delivery outcomes	✗	✗	✓	✓
Discount sensitivity	✗	✗	✗	✓
Real-time checkout UX data	✗	✗	✗	✓

We are the checkout. Not a layer on top. Not a redirect. Not a popup overlay. We render the form. We process the input. We see every keystroke delay, every hesitation, every failed discount code, every address correction, every payment method switch. Shopify sees the order after it’s placed. Payment gateways see the transaction. We see the entire decision-making process.

What We Collect

Three tiers of data collection, each progressively richer. All legal — reviewed and approved. Consent covered in Terms & Conditions.

Tier 1: Passive

No extra consent needed

Device fingerprint (lightweight canvas hash)
Referral source (UTM + referrer)
Time on page before checkout
Cart modification history
Discount codes tried (all attempts)
Checkout step timing
Browser / device type
Repeat visit detection
Pincode → city mapping

Tier 2: Active

Covered in T&Cs

Cross-store shopper linking (phone match)
COD delivery outcomes (shipping webhooks)
Return / refund data
Browsing history on store
Location from IP vs shipping address

Tier 3: Derived Intelligence

Computed from collected data

Shopper Reliability Score
Product Demand Index
Price Sensitivity Score
Geographic Risk Map
Seasonal Demand Curves
Churn Prediction

Shopper Intelligence

From Phone Number to Financial Identity

Day 1: Phone number + one address + one payment = anonymous shopper. Score 50, confidence 0.0, risk tier “unknown.”

Month 1: Same phone at 3 stores = behavioral pattern. COD preference, average order value, category affinity. Confidence rising.

Month 6: Same phone at 10+ stores = reliability profile. COD success rate, return rate, address stability. Confidence >0.5. Risk tier assigned.

Year 1: Same phone at 50+ stores = financial identity. Creditworthiness, spending power, risk score. A signal no bank, no NBFC, no fintech has.

Shopper Credit Profile

Signal	Source	Window
COD success rate	Delivery outcomes (shipping webhooks)	Rolling 90 days
Order completion rate	Checkout session data	Rolling 90 days
Address stability	Address changes across orders	Lifetime
Payment method consistency	Checkout payment choices	Rolling 30 days
Cross-merchant order frequency	Network-wide order data	Rolling 30 days
Average order value	Payment sessions	Rolling 90 days
Checkout abandon rate	Session tracking	Rolling 30 days
Discount dependency	Discount code attempts vs completions	Rolling 90 days

A phone number seen across 50 stores is a credit signal no bank, no NBFC, no fintech has. Banks see repayment. We see intent, behavior, reliability, and purchasing power — before the transaction happens. Bayesian smoothing ensures we never over-index on thin data: new shoppers start at score 50, and scores only diverge meaningfully after 20+ events across multiple stores.

Market Intelligence

What the Aggregate Data Tells Us

Intelligence	How Built	Value
Product Demand Index	Add-to-cart × conversion × cross-store demand	Which products sell and which don’t — across the entire market
Category Heat Map	GMV by category × growth rate × seasonal patterns	Market entry decisions, feature prioritization
Price Elasticity	Discount attempt rate × abandonment at price step × conversion vs AOV	Optimal pricing for our own tiers and merchant guidance
Geographic Commerce Map	Order volume × COD rate × delivery success × return rate by pincode	155K pincodes with real commerce data
Payment Method Shifts	UPI vs COD vs card trends over time	Where the market is going — product roadmap input

Anonymized, aggregated, never individual. No merchant sees another merchant’s data. They see anonymized benchmarks: “Your conversion rate is 2.1% — industry average for your category is 3.4%.” “COD orders from pincode 560001 have 8% rejection rate — consider a prepaid nudge.” The intelligence is derived from the network, shared as insight, never as raw data.

The Palantir Play

100

Merchants → Useful Analytics

1,000

Merchants → Shopper Network Moat

10,000

Merchants → Financial Identity Layer

At scale, we don’t need to build a bank. We score, others lend. We don’t need to build a logistics company. We predict, others ship. The checkout is the observation point — the intelligence derived from it is the product.

Three Monetization Horizons

Now: SaaS + Data Features

Shopper reliability scores power COD auto-approve. Address pre-fill powers faster checkout. Segments power campaigns. All bundled into existing paid tiers.

Revenue: SaaS subscription fees. Data is the value driver, not a separate line item.

Next: Data APIs

Risk scoring as a service (for logistics companies, BNPL providers). Demand intelligence feeds (for brands, marketplaces). Affiliate attribution network.

Revenue: API call pricing + data licensing. Per-query or monthly access tiers.

Future: Financial Layer

BNPL underwriting (we score, partner lends). Merchant cash advance (we see real GMV, not reported). Insurance (we know return rates by category by geography).

Revenue: Revenue share with NBFC partners. Underwriting fee. Risk premium.

GoKwik proved this model works. Their 120M+ shopper profiles are worth more than their SaaS revenue. That network data is what drove their $200M+ valuation. We build the same flywheel — but with full checkout control, full data ownership, and no payment gateway dependencies.

Merchant-Facing Products

What merchants see in their dashboard. Each capability maps to a paid tier — the data platform is the value engine behind every feature.

Capability	What Merchants Get	Tier
Shopper Reliability Score	COD auto-approve / block based on cross-merchant reliability. Score + confidence visible at checkout.	COD ₹499/mo
Address Pre-fill Network	Phone → name + address + payment pref pre-filled from cross-store profiles. 155K+ pincodes.	Checkout ₹1,999/mo
Cross-Store Segments	Network-wide shopper segments: high-value, COD-reliable, price-sensitive, churned.	Engage ₹799/mo
Geographic Risk Map	Pincode-level delivery intelligence: COD acceptance rate, return rate, avg delivery time.	COD ₹499/mo
Demand Intelligence	Category trends, seasonal demand curves, product performance benchmarks.	Future: Analytics Pro
Affiliate Attribution	Cross-merchant source tracking. First-touch, last-touch, cross-store attribution models.	Future: Marketplace

Internal Capabilities

These stay internal. Not in marketing. Not in the merchant dashboard. Not on the website. Our private competitive edge — the intelligence that powers our strategic decisions and future financial products.

Capability	What We Learn	How We Use It
Shopper Credit Profile	Financial identity from checkout data. COD reliability, payment patterns, cross-merchant consistency, order value distribution.	BNPL underwriting, merchant cash advance, risk pricing for COD guarantee
Market Intelligence	Category GMV, AOV, conversion rates, payment method mix — aggregated and anonymized.	Strategy, market entry, competitive positioning, investor materials
Price Sensitivity Scoring	Discount dependency, abandonment patterns, price-step drop-off rates.	Optimize our own pricing tiers. Future: dynamic pricing API for merchants.
Churn Prediction	Merchant churn: usage decline, config changes, order volume drops. Shopper churn: days since last order, frequency decline.	Proactive retention. Flag merchants before they uninstall. Re-engagement campaigns.
Fraud Signals	Device fingerprints, velocity checks, address mismatches, multi-store rapid orders.	Protect the network. Reduce chargebacks. Block bad actors across all merchants.

Implementation

16 tasks, 4 sprints, legal approved. AI-coded with Codex (parallel agents), reviewed by Claude. Each sprint = 1–2 days. Total: 4–5 days of execution.

Sprint	Focus	Tasks	Key Deliverables	Timeline
D1: Foundation	Data infrastructure	5	Credit profiles, touchpoints, attribution tables, reliability scoring, order outcome pipeline	1 day
D2: Intelligence	Scoring & analytics	4	Scoring engine, cross-merchant analytics, risk API, segment enrichment, merchant benchmarks	1 day
D3: Feature Tiers	App architecture	4	Single app + feature flags + billing tiers + incremental OAuth + theme app extension	1–2 days
D4: Monetization	Revenue features	3	Smart COD automation, affiliate dashboard, COD guarantee infrastructure	1 day

Technical Foundation

New Entities (6)

shopper_credit_profiles — scoring signals, rolling windows, Bayesian confidence
shopper_touchpoints — UTM, referrer, click IDs, device, session
shopper_attributions — first/last touch, cross-merchant attribution
market_signals — anonymized cross-merchant aggregates
merchant_subscriptions — tier registry, billing provider, status
cod_guarantees — guaranteed COD orders, settlement tracking

Already Built

shoppers table with phone as primary key
shopper_events with cross-store tracking
shopper_addresses for address history
payment_sessions for order/checkout data
Pincode database (155K+ records)
COD engine with rules + OTP
Analytics module (S1 complete)

Scoring formula with Bayesian smoothing: New shoppers start at score 50 with confidence 0.0. Confidence reaches 1.0 at 20+ events. Minimum 3 orders before a non-“unknown” risk tier is assigned. Daily batch recomputation at 3 AM UTC. Time decay: inactive >90 days causes confidence to drift toward 0. No score inflation, no false precision.

Competitive Moat

CashierLogic vs GoKwik

	GoKwik	CashierLogic
Shopper profiles	120M+ (5-year head start)	Building from zero
Data ownership	Shares with PG partners	We keep everything
Checkout control	Popup overlay (limited data)	We ARE the checkout (full data)
Pricing transparency	Opaque + 2–3% GMV fee	Transparent, no GMV %
Network lock-in	Merchants stay for KwikPass data	Same play, better economics
Discount sensitivity data	Limited (redirect model)	Full (we see every attempt)
Cart-level data	Partial	Complete (cart drawer is ours)
Multi-platform	Shopify only	Shopify + WooCommerce + Nuvemshop

GoKwik proved the model works — their 120M shopper profiles are worth more than their SaaS revenue. We build the same flywheel but with full checkout control and data ownership. They share data with payment partners. We don’t have to. They charge 2–3% of GMV. We charge flat fees. They lock merchants in. We let them leave. The data advantage compounds regardless — every merchant who installs and uninstalls still leaves shopper profiles behind.

Revenue Impact

Projection at different merchant counts showing SaaS revenue plus data-derived revenue potential.

Merchants	SaaS Revenue (Monthly)	Data-Derived Revenue	Combined
100	₹2.5L	₹0 (building profiles)	₹2.5L/mo
500	₹9L	₹1L (COD guarantee fees)	₹10L/mo
1,000	₹18L	₹5L (risk APIs + COD guarantee)	₹23L/mo
5,000	₹75L	₹30L (risk APIs + BNPL rev share + demand intel)	₹1.05Cr/mo
10,000	₹1.2Cr	₹80L (financial layer + data licensing + affiliate network)	₹2Cr/mo

₹0

Data Revenue at 100 Merchants

₹5L/mo

Data Revenue at 1K Merchants

₹80L/mo

Data Revenue at 10K Merchants

The inflection point is 1,000 merchants. Below that, the data platform is a feature differentiator — reliability scores and pre-fill make the SaaS stickier. Above that, the data itself becomes monetizable: risk APIs, credit scoring for BNPL partners, demand intelligence subscriptions. At 10,000 merchants, data-derived revenue approaches SaaS revenue.

Timeline

Phase	Milestone	Merchant Count	Data Capability
Q2 2026	Foundation (D1–D4)	0–50	Credit profiles, touchpoints, reliability scoring, tier system
Q3 2026	Network growth	50–200	Cross-merchant linking active, COD auto-decisions, affiliate tracking
Q4 2026	Intelligence layer	200–500	Market signals aggregation, merchant benchmarks, geographic risk maps
H1 2027	Data APIs	500–1,000	Risk scoring API, demand intelligence feeds, COD guarantee launch
H2 2027	Financial layer	1,000–5,000	BNPL underwriting (NBFC partner), merchant cash advance, data licensing
2028	Scale	5,000–10,000	Full financial identity layer. We score, others lend. Insurance products.

Now → Q2 2026: Build the plumbing. Credit profiles, touchpoints, scoring engine. No revenue from data yet — it powers the SaaS.

Q3–Q4 2026: Network effects kick in. Cross-store pre-fill is the killer feature. Merchants stay for the data, not just the checkout.

2027: Data becomes a revenue line. Risk APIs for logistics companies. Credit scores for BNPL providers. COD guarantee as factoring product.

2028: Financial identity layer. A phone number seen across 10,000+ stores is a credit signal worth more than a credit bureau report. We don’t lend — we enable lending.

16 tasks. 4 sprints. Zero cost of data acquisition. Every checkout makes the network smarter.

Data Platform Strategy · April 2026 · v1.0.0 · CashierLogic · A Kasha Venture