Turning fragmented commerce data into profitable growth decisions.
I build the decision layer between raw commerce data and the commercial calls that move margin — structured models, customer value analytics and scenario simulation that leaders can act on in minutes.
Client & organisational exposure
0+
Data sources unified
0
Decision dashboards
0
Scenario levers modelled
01 — The business challenge
Growth stalls where the data breaks.
Four recurring failure points sit between commerce teams and confident commercial decisions. Select a card to read the diagnosis.
02 — The solution
A decision system, built end to end.
Click any node in the architecture to inspect what happens at that layer.
Shopify · Marketplaces · Ads · Logistics
Data
Raw exports and API feeds are landed with consistent grain, currency and refund treatment so every downstream number reconciles to a single source.
Artefacts at this layer
- Order lines
- Customers
- Ad spend
- Shipping cost
03 — Power BI showcase
Five report surfaces, one governed model.
A walkthrough of the reporting layer. Every figure below is illustrative sample data built to demonstrate structure and logic — not client results.
Net revenue against contribution margin, monthly. The gap between the two lines is the discount, shipping and returns cost the top line hides.
Net revenue
₹0k
Aug, illustrative
Contribution margin
0.0%
of net revenue
AOV
₹0
blended channels
04 — Scenario lab
Move a lever. See the margin consequence.
A simplified version of the simulation layer. Adjust the commercial levers and the model recalculates revenue, AOV and contribution margin instantly.
Blended promotional depth across the catalogue. Lifts volume, compresses unit margin.
Raising the threshold pushes basket size up but suppresses conversion at the margin.
Paid acquisition spend indexed to the base plan, with diminishing returns applied.
Net revenue
₹6,54,840
▲ 0.1% vs. base (₹6,54,000)
Average order value
₹2,140
▲ 0.0% vs. base (₹2,140)
Contribution margin
₹1,99,071
▲ 0.1% vs. base (₹1,98,816)
Read-out
This combination protects contribution margin. It is a fundable move — validate against inventory cover before committing.
Margin rate 30.4% · Orders 306
05 — Impact
What changes when the decision layer exists.
Reporting cycles collapse into a refresh. Commercial questions get an answer inside the meeting that raised them.
One governed measure set across Shopify, marketplaces, media and logistics — so finance, growth and ops argue about the decision, not the number.
Segment, affinity and cohort signals surface as a ranked shortlist with modelled margin impact attached.
06 — Methodology
Discover → Structure → Model → Analyse → Simulate → Act
A repeatable engagement sequence. Each stage produces an artefact the next stage depends on.
01
Discover
Map the commercial questions that actually get asked, the decisions behind them, and where the current data breaks.
02
Structure
Land sources at a consistent grain and design the star schema: conformed dimensions, clean keys, single-direction relationships.
03
Model
Build the Power Query transformations and the governed DAX measure set so definitions are written once.
04
Analyse
Apply RFM, CLV and cohort analysis to expose where value concentrates and where retention economics fail.
05
Simulate
Wrap the model in scenario levers — discount, shipping threshold, media — so trade-offs are quantified before they are committed.
06
Act
Hand over a ranked decision shortlist with owners, expected margin impact and the measurement plan to verify it.
07 — About
Monica Murugaiyan
Revenue Analytics & Customer Value Strategist. I work with commerce and B2B teams to turn scattered transactional data into a governed decision system — the model, the measures, the customer economics and the simulation layer that sits underneath commercial calls.
Capabilities
- Dimensional modelling (star schema) for commerce data
- Power BI, DAX and Power Query measure governance
- RFM segmentation, CLV projection and cohort retention
- Contribution-margin and unit-economics analysis
- Pricing, promotion and shipping-threshold simulation
- Executive reporting and decision facilitation
Exposure
Unilever
FMCG commercial exposure
JK Paper Limited
Industrial B2B exposure
ISC Paris
Business school — analytics & strategy
Contact
Let’s pressure-test your revenue data.
Available for consulting engagements covering data modelling, customer value analytics and commercial scenario planning. A first conversation is a 30-minute diagnostic of where your decisions are currently blocked.
Response time is usually within one business day.