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Case Study · MENA Ride-Hailing Leader · Analytics Product 05 / 07

Pricing & Revenue Intelligence

Scenario simulators that answered the most expensive question in the business — what happens to GMV, subsidies, COGS, and net revenue if we change the margin in city X — before anyone touched production pricing.

Scenario ModelingPricing SimulationGoogle Sheets SQLFinancial ModelingElasticity Assumptions
product scorecard — structural factsproduction
2
scenario models — pricing simulation & GMV expectation
1
input cell drives each scenario — New Margin %
4
delta formats per line — ABS · % of GMV · DIFF · DIFF ABS
−10%
explicit elasticity assumption, stated in the model

Text-only case study, by design: these models live in files carrying an internal data classification, so no screenshots are published — the methodology is the portfolio piece.

01The Business Problem

Margin changes are the highest-leverage, highest-risk lever a marketplace has. Raise the margin and trips may fall; add subsidies and net revenue bleeds. Commercial teams needed to see the full financial cascade of a pricing decision — per city, per product — before deploying it, not after.

The goal: turn "what if we change the margin?" from a debate into a calculation anyone could run by editing one cell.

02My Role

Sole analyst on the product: I designed the model structure, wrote the SQL that fed the historical baselines, and built the scenario calculators with designated input cells, explicit assumptions, and full delta accounting.

03What I Built

Pricing Simulation Model

Per-city calculators (built city by city, tab by tab) comparing three months of baseline data. The analyst enters a New Margin % into a designated input cell; the model cascades the change through expected trips, GMV, subsidies, returns & waivers, COGS, and net revenue for each product line.

GMV Expectation Model

The same engine applied month by month with monthly baseline blocks: expected GMV, rider and captain subsidies, promotion discounts, marketing credits, returns and waivers, current vs expected COGS, and current vs expected net revenue — every line in absolute, %-of-GMV, and difference formats.

Explicit Assumptions

The elasticity assumption — an expected growth rate of −10% under margin increase — sits in a visible, labeled cell, not buried in a formula. Anyone reviewing the scenario sees exactly what was assumed and can change it.

Price-Change History Tracking

Weekly trend charts of bookings and trips per product line spanning past price changes — so every new scenario is read against what actually happened the last time pricing moved.

04Signature Build Details

Input cells, not input meetings: the interface is one designated blue cell — New Margin % — against labeled current-margin references. Editing it recomputes the entire financial cascade.
Four formats per line item: every financial line reads in ABS, % of GMV, DIFF, and DIFF ABS — so finance, commercial, and ops each read the scenario in their native unit.
The full cost side, not just revenue: subsidies (rider and captain), returns, waivers, and COGS are modeled explicitly — a margin change that "grows GMV" but bleeds subsidies shows its true net effect.
History beside hypothesis: the price-change trend charts sit in the same file as the simulator — assumption checking built into the workflow.

05KPIs Defined & Governed

GMVNet RevenueCOGS Rider / Captain SubsidiesReturns & Waivers Margin %C/RATF

06Decisions Enabled

Commercial teams tested margin scenarios per city and product line before deployment.
Pricing debates moved from opinions to a shared model with visible assumptions.
The full subsidy-and-COGS cascade prevented "revenue up, profit down" surprises.

07Skills Demonstrated

Scenario & sensitivity modelingFinancial modeling Elasticity assumptionsModel UX (input-cell design) SQL baselinesSheets as a modeling tool
Mohamed Eltanbedawy · Business & Data Analyst — BI & Analytics
mohamed.tanbedawy@gmail.com · linkedin.com/in/tanbedawy