Available for E-Commerce Audit Engagements

I Identify the Margin Leaks Destroying Your Profitability.

Helping e-commerce founders recover margin by auditing discount behaviour, inventory inefficiency, and operational drag — then translating that evidence into decisions that protect profitability, not just revenue.

$139K MRR Leak Quantified
61% Discount Rate Audited
£390K+ Revenue Opportunity Surfaced
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Zeeshan Akram
Audits Completed
6 Case Studies
BI Dashboards
3+ Delivered
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About

Commercial thinking, analytical rigour, business-first conclusions

Analytical Philosophy

Independent Revenue & Profitability Analyst

Most business data problems are not data problems. They are framing problems. Vague pain — "revenue is growing but profit isn't" or "we're losing customers and don't know why" — rarely arrives with a clean question attached. My work starts by translating that ambiguity into a precise, measurable analytical problem before a single query is written.

I specialise in commercial analytics for e-commerce founders and operators: margin audits, discount profitability analysis, inventory and operational risk, and customer value concentration. The emphasis is always on outcomes that inform decisions — not dashboards built for their own sake.

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Case Studies

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Certifications

$139K+

MRR Leak Traced

Analytical Toolkit

Commercial Analysis
Margin AuditingChurn DiagnosticsRFM SegmentationCLV ModellingCohort Analysis
Data Engineering & Querying
Advanced SQLPostgreSQLMySQLWindow FunctionsCTEs
Business Intelligence
Power BIDAXPower QueryExcel (Advanced)
Data Manipulation & EDA
PythonPandasNumPySeabornETL Pipelines

Commercial Analytics Audits

End-to-end investigations into real business performance problems — from raw data to boardroom-ready conclusions

The Profit Leak Diagnostic — DataCo Supply Chain
Profit Audit View on GitHub
Flagship Case Study
Case Study 07

The Profit Leak Diagnostic — DataCo Supply Chain

A full profitability and operational risk audit of a $35.2M e-commerce catalog spanning 180,519 transactions. Built across SQL data architecture, Python statistical validation, and a five-page Power BI dashboard, the investigation locates exactly which products, shipping tiers, and pricing habits are quietly converting strong revenue into weak or negative profit.

Just 8 products generate 84.5% of all profit, while the other 110 are operational noise riding on their back.
28 products combine weak margins with high delivery failure while producing only 2.2% of total revenue.
First Class shipping arrives late 100% of the time, while the cheapest tier in the catalog is also the most reliable.
A modeled 20% margin shock to the top 8 products alone would wipe out $643,000 in profit, nearly 17% of total company profit.
PostgreSQLSQLPythonSciPyPandasPower BI
Audit Conclusion
Revenue is not the issue. Mistaking revenue for business health is. Profit in this catalog depends on 8 products performing perfectly, indefinitely, with no backup plan if any of them stumble.
Diagnostic Report (PDF) Slide Deck (PDF) Dashboard Walkthrough
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Retail Discount Profitability Audit — Contoso Corporation
Margin Audit
Case Study 06

Retail Discount Profitability Audit — Contoso Corporation

A multi-layer commercial audit investigating whether Contoso's discount strategy functions as a pricing lever or a structural margin destructor. Spanning SQL data architecture, Python behavioural analysis, and a Power BI audit narrative, the investigation surfaces exactly where discounting accelerates volume while quietly compressing profit.

PostgreSQLSQLPython PandasPower BI
Audit Conclusion
61% of transactions were sold at a discount, with each discounted line delivering $97 less profit than its full-price counterpart. A full pricing policy review was recommended before any further volume-led growth targets were set.
Read Executive Report (PDF)
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SaaS Churn Executive Dashboard
Churn Diagnostic
Case Study 05

Subscription Revenue Risk & Contract-Level Churn Analysis

Identified where subscription cancellations were concentrated across contract types, usage tiers, and customer segments. Built an end-to-end Python and Power BI pipeline using dimensional modelling to quantify which operational segments were destroying the most recurring revenue — and why month-to-month contract structures were the primary risk vector.

PythonMySQL Power BIStar Schema
Business Finding
Quantified a $139K active MRR leak and proved month-to-month contracts account for 86.9% of total revenue loss — establishing concrete targets for retention intervention.
View Executive Dashboard (PDF)
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Olist eCommerce RFM Analysis
Retention Analysis
Case Study 02

Olist eCommerce: Churn Root-Cause & RFM Investigation

Root-cause investigation across 113K+ Brazilian eCommerce orders revealing that logistics failure — not pricing or product — was the dominant churn driver. Delivery delays directly correlated with zero repeat purchases, reframing the problem from a product question to an operations intervention requiring immediate fulfilment process changes.

PythonPandasSeabornRFM Analysis
Business Finding
Logistics failure — not price or product — was the dominant driver of churn. Delivery delays directly correlated with one-star reviews and zero repeat purchases.
Read Executive Report (PDF) View Dashboard Layout (PDF)
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UK Retail CLV & RFM Dashboard
CLV Modelling
Case Study 04

UK Retail: Customer Lifetime Value & Segment Migration

Analysed £17M+ in revenue across 1M+ UK retail transactions to surface the true concentration of customer value. Built a custom Power Pivot relational model with Power Query ETL to separate VIP behaviour from stagnant cohorts and quantify the revenue opportunity hidden in segment migration.

Advanced ExcelPower Pivot DAXPower Query
Business Finding
A 40x CLV gap between VIP and mid-tier customers (£8,767 vs £220) revealed a £390K+ incremental revenue opportunity achievable purely through cohort upgrade — no new acquisition required.
Watch 3-Min Model Walkthrough
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ClassicModels SQL Analysis
SQL Intelligence
Case Study 03

ClassicModels: Revenue Concentration & Inventory Risk Audit

Analysed a multi-table retail database using advanced SQL — CTEs, window functions, and multi-level joins — to map where revenue was concentrated, which inventory positions represented fulfilment risk, and where the customer base had untapped upsell potential.

SQLMySQLCTEsWindow Functions
Business Finding
Identified a severe 67:1 demand-to-stock ratio bottleneck creating fulfilment risk, and pinpointed micro-upsell segments capable of driving VIP tier growth without additional acquisition spend.
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Executive Sales Intelligence Dashboard
Executive BI
Case Study 01

Executive Sales Intelligence System — Power BI

Replaced static Excel reporting with an enterprise-grade Power BI dashboard featuring dynamic YoY time-intelligence, cross-filtered regional breakdowns, and a visual hierarchy designed for boardroom-level decision-making — not data exploration.

Power BIDAXPower QueryData Modelling
Business Finding
Dynamic YoY intelligence immediately surfaced margin bottlenecks and regional performance gaps that were invisible in prior static reporting — enabling faster executive escalation.
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Professional Standards & Workflow Signals
GDPR / Data Privacy Conscious
Documented SQL Audit Trails
Git-Versioned Analysis
No Raw Data Retained Post-Engagement
Business-First Reporting Standard
Async-First — US/UK Timezone Compatible
Analytical Finding — Contoso Discount Audit

"The logic applied in this audit successfully isolated a $97-per-line profit gap that was previously hidden in aggregate revenue reporting. By separating discounted from full-price transactions at the order line level, the analysis surfaced a structural margin compression — from 54% to 49% — driven entirely by unmanaged discounting habit, not market pressure. This finding was invisible at the category level and would have remained so under standard dashboard reporting."

Zeeshan Akram — Retail Discount Profitability Audit, Contoso Corporation (2026)

What Most Founders Miss

Common patterns where standard reporting obscures the underlying business problem

Revenue grows while profit stagnates

Top-line growth can mask structural margin compression from discounting, customer mix shifts, or rising acquisition costs that don't appear in headline revenue figures.

Discounting drives volume, not value

High discount rates often inflate order counts while simultaneously training customers to never pay full price — creating retention that is entirely price-dependent and operationally fragile.

Churn is attributed to the wrong cause

The actual driver is frequently operational — delivery failures, fulfilment inconsistencies, or logistics performance — and never surfaces in product analytics.

The top 20% of customers are obscured by averages

Aggregated CLV and ARPU figures hide extreme concentration. Knowing that 18% of your customer base generates 74% of profit changes every acquisition and retention decision you make.

Cohort behaviour is never isolated

What looks like a catalogue problem is often a single acquisition period or campaign cohort with structurally poor fulfilment performance.

Margin is measured at category, not line level

Category-level margin reporting allows loss-making SKUs to hide behind strong performers. Profit destruction is almost always concentrated in specific product lines that aggregate data cannot isolate.

Analytics Consulting Services

Structured engagements for founders and operators who need commercial clarity, not more dashboards

Each engagement is scoped around a specific business question. The output is a quantified finding and a set of evidence-backed recommendations — not a generic report.

Profitability & Margin Audits

Investigating where margin compresses between revenue recognition and net profit — across product lines, customer segments, geographies, and discount tiers. Built for operators who see top-line growth but stagnant or declining bottom-line performance.

Churn & Retention Diagnostics

Locating the exact fulfilment, product, or pricing stage where repeat purchase behaviour breaks down — separating operational root causes from product or pricing signals.

Customer Segmentation & LTV Analysis

Decomposing an aggregated customer base into behavioural segments to surface the true concentration of revenue and lifetime value — isolating which cohorts to protect, upgrade, or deprioritise based on evidence rather than intuition.

Discount & Pricing Intelligence

Auditing whether a discount strategy is generating incremental profit or systematically compressing margin. Identifies customer-level discount dependency and models the financial impact of a pricing policy change before it is implemented.

Revenue Leakage Investigation

Tracing operational and commercial gaps where revenue is generated but not fully captured — through returns, fulfilment failures, contract structure, or pricing inconsistency. Outputs a prioritised list of leakage sources with estimated financial impact per issue.

Executive KPI Intelligence Systems

Designing and building Power BI reporting systems around the business questions that actually drive decisions — not vanity metrics. Built for leadership teams who need a single, trusted source of commercial performance intelligence with full drill-down capability.

My Analytical Process

Every engagement follows the same rigorous framework — from raw question to boardroom-ready recommendation.

1
Define the
Business Problem

Translate a vague business pain into a precise, measurable analytical question.

Problem Statement
2
Collect &
Clean Data

ETL pipelines, deduplication, outlier handling — building a trustworthy single source of truth.

Clean Dataset
3
Analyze &
Model

SQL joins, Python aggregations, RFM scoring, cohort breakdowns — digging until a signal emerges.

Key Findings
4
Visualize &
Communicate

Power BI dashboards and Seaborn charts designed for a non-technical executive audience.

Live Dashboard
5
Recommend
& Quantify

Every output ends with a £/$ figure — a specific revenue impact, not a vague "consider improving."

Revenue Impact

The last step is non-negotiable. Any analysis that doesn't end with a quantified business recommendation is just a data exercise — not a decision tool. Every case study in my portfolio closes with a specific £ or $ figure for this reason.

Credentials

Verified certifications from IBM and Google via Coursera

Data Analysis with Python

IBM · Coursera

Data Visualization with Python

IBM · Coursera

Databases & SQL for Data Science

IBM · Coursera

Crash Course on Python

Google · Coursera

Diagnostic Services

Three fixed-scope entry points. One clear output: a quantified commercial finding and an evidence-backed recommendation you can act on.

Entry Point 01

The Profitability Audit

A full-stack review of your transaction data to locate exactly where margin compresses between revenue recognition and net profit — across product lines, discount tiers, customer segments, and geographies. Built for operators who see top-line growth but declining bottom-line performance.

  • SQL-documented audit trail
  • Python behavioural analysis
  • Margin leakage quantified by segment
  • Power BI executive narrative
Book a 15-Minute Revenue Diagnostic
Entry Point 02

Retention & Churn Deep-Dive

Identifying exactly when and why customers stop buying — isolating the lifecycle stage, root cause (operational vs. product vs. pricing), and the precise revenue-at-risk figure that justifies intervention. Outputs a cohort-level breakdown, not a generic churn rate.

  • Cohort-level churn breakdown
  • RFM segmentation & LTV modelling
  • Revenue-at-risk quantification
  • Retention intervention roadmap
Book a 15-Minute Revenue Diagnostic
Entry Point 03

Executive Dashboard Build

Turning messy billing or operational data into a clean Power BI or Excel command centre — built around the business questions that actually drive decisions, not vanity metrics. Designed for leadership teams who need one trusted source of commercial intelligence.

  • Power BI / Advanced Excel dashboard
  • Dynamic YoY time-intelligence
  • Cross-filtered executive views
  • Full drill-down capability
Book a 15-Minute Revenue Diagnostic

Analytical Insights

Published breakdowns of real commercial analytics investigations — written for founders and operators, not data scientists.

E-commerce Churn Analysis Python Code
Churn Investigation

Why 97% of Customers Never Returned: A Root-Cause Analysis of 113K eCommerce Orders

A complete Python analytics case study on the Olist dataset — covering data engineering, memory optimisation, and RFM segmentation to determine whether a near-total churn rate was a product, pricing, or operational failure — and what that distinction means for intervention strategy.

Read on Medium
17M Retail Data Analysis Excel Dashboard
Revenue Concentration Analysis

The 40x Customer Multiplier: How RFM Segmentation Surfaced £390K Hidden in Plain Sight

A breakdown of how processing 1M+ retail transactions via Power Query and a custom RFM segmentation model revealed that a small VIP cohort was generating 40x the lifetime value of mid-tier customers — and what a migration strategy could realistically recover.

Read on Medium

Start a Conversation

Available for analytics consulting engagements, profitability audits, and commercial analysis partnerships

Book a 15-Minute Revenue Diagnostic

Discuss a Business Problem

If your business is experiencing margin pressure, unexplained churn, or revenue growth that isn't translating to profit — that is exactly the kind of problem worth discussing. Reach out to explore whether an analytics engagement would surface the answer.

Twitter / X

@ZeeshanAkram_DS