Pure'O Naturals
Strategic Intelligence Report
A 6-Month Data Forensics Study · Branch 0007-ANJANEYA NAGER · Bangalore
April – September 2025 · 183 Trading Days
The Store Behind the Data
“We often run out on busy days and over-buy on slow ones.”— Branch Owner, Pure'O Naturals, Nov 2025
Study Overview — Key Metrics
| Metric | Value |
|---|---|
| Total Revenue | ₹2,53,93,827 |
| Daily Mean Revenue | ₹1,38,764 |
| Peak Daily Revenue | ₹2,58,000+ |
| Total Transactions | 52,314 |
| Unique Active SKUs | 3,247 |
| Mean Transaction Value | ₹486 |
| Median Transaction Value | ₹200 |
| Total Units Sold | 3,35,900 |
| Transaction Skewness | 6.1 (right-tailed) |
FIG. 1 — Category Revenue Mix · Apr–Sep 2025
Diagnosed. Quantified. Solved.
Six analytical techniques, 52,314 transactions, and one business stripped bare.
Demand Volatility & Wastage
Seasonal demand spikes create chronic overstocking and spoilage in perishables.
Portfolio Complexity
SKU bloat dilutes buyer focus and ties up capital in slow-moving inventory.
Margin Compression
Inconsistent pricing erodes margins across 27% of the active SKU portfolio.
Pricing Misalignment
Staff apply inconsistent prices at POS, creating revenue leakage and customer distrust.
Category Mix Drift
Miscategorized transactions masked true category performance, invalidating analysis.
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“Five problems. One business. ₹72–100 Lakhs recoverable.”
Combined annual impact across all five problem domains
From Raw POS to Strategy
POS Export
6 CSV files · 183 days
Raw SalesDetail.rpt.csv exports
Cleaning
52,314 rows · QA: 8/8 ✓
3 duplicates removed, nulls handled
Analysis
6 techniques applied
Python · pandas · numpy · scipy
Synthesis
3-pillar strategy
₹72–100L annual impact
Data Quality Assurance Log
| Quality Check | Status |
|---|---|
| Revenue sum consistency | PASS |
| Date range coverage (183 days) | PASS |
| Missing critical columns | PASS |
| Duplicate removal (3 found, removed) | PASS |
| Outlier handling (IQR flagging) | PASS |
| Category mapping (98.5%) | PASS |
| Manual receipt audit (100 rows) | PASS |
| Pipeline reproducibility (3× runs) | PASS |
Technology Stack
Six Analytical Techniques
Descriptive Statistics
Portfolio CV: 180.9%
ABC / Pareto Classification
Class A (652 SKUs) = 70.2% rev
CV Volatility Analysis
746 high-risk SKUs flagged
P10 Margin Proxy
869 below-floor, 95 negative
Price Variance Index
36.6% avg variance
Risk Stratification
RED / YELLOW / GREEN / BLUE
The Numbers Don't Lie
Revenue Landscape
FIG 4.1 — Daily Revenue Distribution
FIG 4.2 — Monthly Revenue Trends
Category Revenue Intelligence
| Category | Revenue Share | 6-month Trend |
|---|---|---|
| Fruits | 36.3% | ↑ Dominant |
| Vegetables | 35.3% | ↑ +42.8% share gain |
| Dairy & Eggs | 6.1% | → Stable |
| Snacks & Pantry | 4.3% | → Steady |
| Other / Cleaned | 18.0% | ↓ Was 40.28% unknown |
FIG 4.3 — Category Revenue Mix
FIG 5.5 — Category Mix Shift Apr–Sep 2025
ABC Portfolio Classification
| Class | SKUs | Revenue | Share | Action |
|---|---|---|---|---|
| A | 652 | ₹1.78 Cr | 70.2% | Protect & optimize |
| B | 950 | ₹50.8 L | 20.0% | Monitor closely |
| C | 1,645 | ₹24.8 L | 9.8% | Rationalize aggressively |
FIG 4.4 — ABC/Pareto Classification Curve
Demand Volatility Profile
| CV Range | SKUs | Share |
|---|---|---|
| <12% (Stable) | 421 | 13.0% |
| 12–25% (Moderate) | 714 | 22.0% |
| 25–50% (High) | 812 | 25.0% |
| >50% (Extreme) | 1,300 | 40.0% |
| Month | Portfolio CV | Trend |
|---|---|---|
| Apr 2025 | 1.93 | ← Baseline |
| May 2025 | 2.41 | |
| Jun 2025 | 2.18 | |
| Jul 2025 | 2.87 | |
| Aug 2025 | 3.12 | |
| Sep 2025 | 3.59 | ← Peak ↑86% |
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FIG 5.1 — Daily Sales Variation Z-Score
FIG 5.2 — 7-Day Rolling Volatility Heatmap
FIG 4.5 — Volatility Distribution
Margin & Pricing Analysis — P10 Proxy Method
Without access to wholesale cost data, the P10 price heuristic was used: products with low minimum prices (P10) relative to average reveal compressed margins.
| Margin Tier | SKUs | Status |
|---|---|---|
| Negative Margin | 95 | CRITICAL — Immediate action |
| Very Low (<5%) | 180 | HIGH — Reprice Tier 1 |
| Low (5–15%) | 571 | MODERATE — Monitor |
| Near-Floor (15–20%) | 23 | WATCH — Approaching floor |
| Above Floor (>20%) | 2,378 | SAFE |
FIG 5.3 — Margin Distribution by Category
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FIG 5.4 — Margin Distribution · Top 20 SKUs
Shewhart X-MR Pricing Control Charts
Statistical process control applied to retail pricing — catching out-of-control pricing events before they leak revenue.
X-MR 1
ANAR X-MR Chart
X-MR 2
Apple Royal Gala X-MR
X-MR 3
Banginapalli Mango X-MR
X-MR 4
Tomato Local X-MR
X-MR 5
Baby Orange X-MR
X-MR 6
Onion X-MR
Extended Analysis
FIG 6.1
CV Distribution
FIG 6.2
Rolling Volatility By Month
FIG 6.3
Margin Distribution
FIG 6.4
ABC Pareto Extended
FIG 6.5
Slow Movers DSLS
FIG 6.6
Price Variance Top 20
FIG 6.7
Wastage Risk Map
FIG 6.8
Category Reclassification Impact
FIG 6.9
Day of Week Efficiency
The Recovery Roadmap
Three coordinated interventions. Twelve weeks. ₹72–100 Lakhs recovered.
SKU Rationalization
Pricing & Margin Optimization
Dynamic Inventory Management
| Type | Z | SL |
|---|---|---|
| Class A Stable | 1.645 | 95% |
| Class A Moderate | 1.96 | 97.5% |
| Class A High Volatile | 2.33 | 99% |
| Festival / High-Value | 2.576 | 99.5% |
12-Week Implementation Timeline
Identify & tag 97 dormant SKUs
Liquidate dormant stock, free ₹8.3L
Tier 1 repricing: 95 negative-margin SKUs +15–25%
Discontinue 600 Class C tail SKUs
Deploy dynamic ROP for top 100 SKUs
POS price-floor controls on top 20 misaligned
Full KPI review & strategy adjustment
Post-Implementation KPI Targets
| KPI | Current State | Target (12 Weeks) | Delta |
|---|---|---|---|
| Active SKUs | 3,247 | ≤1,800 | −44% |
| Stockout Frequency | Baseline | −40% | ↓ |
| Negative-margin SKUs | 95 | 0 | −100% |
| Avg Price Variance | 36.6% | ≤15% | −59% |
| Dormant SKUs (>90d) | 97 | ≤10 | −90% |
The Business Case
Base Case — Benefit Components (₹ Lakhs)
Breakdown of the ₹85 Lakhs Base Case annual benefit by initiative category
Benefits Accumulation — Month by Month
X-axis: Month 1–12 post-implementation · Y-axis: Cumulative net benefit (₹ Lakhs)
Risk Sensitivity Analysis
| Risk Factor | Conservative Impact | Mitigation |
|---|---|---|
| Staff resistance to repricing | −₹5–8L | Phased implementation + change mgmt |
| Seasonal demand spike misalignment | −₹3–6L | Dynamic ROP reviews monthly |
| Supplier price pass-through | −₹2–4L | Annual price review contracts |
| Customer attrition from price hikes | −₹4–7L | Max 25% increase per SKU in Wk 1 |
Primary Data. Verified.
Every data point traced to a physical transaction. Every method verified at source.
Owner Interaction Videos
Owner Interview — Primary Data Discussion
45-minute structured interview covering business pain points, data authorization, and operational context.
Store Walkthrough — Anjaneya Nagar Branch
Physical store tour documenting store layout, shelf organization, POS terminal, and product category placement.
POS System Demo — Data Collection Process
Interaction with cashier demonstrating POS workflow and SalesDetail export process.
Data Acquisition — Live POS Export
Screen-recorded demonstration of exporting SalesDetail.rpt.csv files from the branch POS system.
Field Research Photos
Pure'O Naturals Store Front — Anjaneya Nagar, Bangalore
Discussion with Branch Owner
POS Terminal & Billing Counter
Interaction During Store Hours
SalesDetail Export — Data Documentation
No Objection Certificate — Signed by Branch Owner
Click any photo to enlarge · Field visit: November 8, 2025 · Branch 0007-ANJANEYA NAGER
Meeting Notes
Formal Meeting Notes — Documented Digitally
The Minutes of Meeting (MOM) from the field visit on November 8, 2025 were formally documented in a structured Word document — covering data access confirmation, business problem discovery, and agreed deliverables.
Academic Credentials
Proposal → Midterm → Final
Three formal submission stages spanning October–December 2025. Each stage built on the last, escalating from problem discovery to quantified strategy.
Proposal
Problem identification, SMART objectives, methodology design, and complete data authorization from branch owner.
Midterm Report
Full EDA complete, all 6 ADA techniques applied, volatility + margin analysis, 5 problems fully quantified.
Final Report
3-pillar strategy, ₹72–100L financial projections, master 9-section consulting report, portfolio website.
Everything, Open-Source
Reports, code, and data — fully available for review and academic reference.
GitHub Repository
Full source code, analysis scripts, and raw data · MIT License
Final Report
9-section consulting-grade report with full analysis, strategy, and financial projections.
Midterm Report
EDA + ADA analysis, volatility study, margin analysis, and X-MR control charts.
BDM Proposal
Problem identification, SMART objectives, and methodology design.
Viva Presentation
32-slide defense presentation covering all 5 problems, 3 pillars, and financial impact.
EDA Python Script
Complete exploratory data analysis pipeline — cleaning, CV analysis, ABC classification.
ADA Pipeline
Advanced analytics: margin stratification, PVI index, X-MR charts, risk scoring.
Raw CSV data (cleaned_sales.csv, wastage_risk.csv, et al.) available in the GitHub repository · data/ · Used under written authorization from Pure'O Naturals for academic purposes only.