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Swaroop Rao, Founder of SpendProof.

Swaroop Rao

Founder, SpendProof

Commerce decision professional with ten years of experience connecting demand, inventory, pricing, customer, marketing, and margin evidence.

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Founder-led by design

I built SpendProof for decisions that have plenty of numbers and no trusted answer.

I have spent ten years working on commerce decisions across demand, inventory, price, customer behaviour, marketing, and margin. The recurring problem was rarely missing data. It was that several accurate reports could still support different stories, while a decision owner had to commit money anyway.

SpendProof is the narrow service I wanted that situation to have: one decision, the minimum useful evidence, an explicit treatment of uncertainty, and a written conclusion with guardrails.

Every SpendProof review is personally completed by Swaroop Rao. SpendProof provides an independent second opinion on one decision. It does not manage the campaign or replace the agency, internal team, or decision owner.

See the work firstReview the founding offer

Why SpendProof exists

A bounded challenge before money is committed.

An advertising platform can report a strong return while store revenue, contribution, returns, customer quality, promotions, or product availability tell a more cautious story. An agency or internal team may be entirely competent and still be responsible for recommending and executing the move rather than independently challenging it. SpendProof provides that bounded challenge. It does not attempt to reconstruct the entire business, identify one perfect attribution number, or take over campaign management. It asks what must be true for the proposed move to be justified, tests those conditions against the agreed evidence, identifies the largest remaining risk, and recommends the safest defensible action.

I have spent my career working on commercial decisions where no single system contained the whole answer. Demand forecasts, inventory, price, promotions, customer behaviour, marketing reports, returns, and margin each described a different part of the business. The job was to determine what the combined evidence supported, what remained uncertain, and what action was defensible. SpendProof applies that discipline to one financially meaningful advertising-budget decision at a time.

Selected decision work

The projects most directly connected to a SpendProof review.

Employer context establishes where the work happened. The decision, responsibility, evidence, and result show why it matters to the customer.

Selected work 01

Beyond average return

At Namshi

Senior Commercial Analytics Lead

SpendProof examines what the proposed next move is likely to do after commercial evidence outside the advertising platform is considered.

Decision

Whether to increase, hold, reallocate, or reduce advertising when platform return conflicted with store revenue, contribution, inventory, and customer-quality evidence.

Contribution

Led the cross-functional analytical workstream, reconciled reporting definitions, built marginal-response and contribution scenarios, identified evidence gaps, and translated the work into budget options with exposure limits and stop conditions. Marketing leadership retained the final decision.

Scope

Five markets, nine channel or campaign groups, 18 months of order and interaction history, and a proposed four-week increment of roughly $900,000.

Result

The contribution-adjusted return estimate was 22% below the platform-reported return in selected segments. The recommendation supported selective scaling rather than one blanket increase, with a two-week review point and a contribution stop rule.

Selected work 02

Demand the inventory could support

Within Arvind Fashions Limited's portfolio environment

Commercial Analytics Lead

Advertising can be unsupported even when channel return looks strong if the relevant products cannot remain available or the move worsens inventory risk.

Decision

Which product, size, store, and channel combinations required replenishment, transfer, reduced buying, markdown, or another commercial intervention each week.

Contribution

Led the design of a decision system joining demand, inventory, inbound supply, size availability, product performance, and commercial constraints; worked with merchandising and planning teams to define intervention rules.

Scope

Four portfolio brands, 18,000 active product-size combinations, 180 retail and ecommerce demand locations, and a weekly cross-functional decision cycle.

Result

Manual weekly analysis time was 61% lower, availability in priority groups was 4.6 percentage points higher than the agreed baseline, and aged-inventory risk was flagged roughly three weeks earlier.

Selected work 03

Future customer value as evidence

At Chalhoub Group

Retail Analytics and Planning Lead; customer-value workstream

SpendProof does not use lifetime value as a rescue assumption. When a decision depends on repeat purchase, the evidence must support it.

Decision

Which acquisition segments justified continued investment when first-order economics were weak but repeat purchase was expected to create longer-term value.

Contribution

Built the cohort and customer-value analysis, separated new and returning demand, quantified uncertainty around repeat behaviour, and defined when longer-term value could responsibly support weaker first-order economics.

Scope

420,000 anonymized customer histories, a 12-month observation period, nine acquisition segments, and several market/channel combinations.

Result

Only four of nine segments retained a credible payback case after returns, discounts, and repeat-purchase uncertainty were included.

Supporting work

Analytical depth across the commercial system.

While at Mu Sigma Inc, on a vendor engagement supporting Microsoft

Forecasting demand before a consequential commercial window

How much demand to plan across markets and channels before a major commercial period, and where inventory or lost-sales exposure would emerge if the central forecast was wrong.

Measured forecast bias was 13% lower and the recurring planning cycle moved from roughly a day and a half to four hours.

While at Mu Sigma Inc, on a vendor engagement supporting Walmart

Separating promotional uplift from ordinary demand

Which promotions produced genuinely incremental demand, which shifted purchase timing, and where future support should be concentrated.

Twenty-seven percent of apparently successful promotions did not clear the agreed incrementality threshold after seasonality, availability, and purchase timing were considered.

Within Arvind Fashions Limited's portfolio environment

Dynamic pricing, markdown, and promotion decisions

Which products should remain at full price, enter promotion, receive a deeper markdown, or be withheld from discounting, and when.

Realized gross margin was 1.8 percentage points above the agreed comparison and end-of-season aged inventory was 11% below it.

Within Chalhoub Group's luxury and premium retail environment

Allocating scarce launch inventory under uncertain demand

How to allocate limited launch or seasonal inventory across markets, stores, and digital channels when history was sparse and signals did not agree.

First-month availability was 5.2 percentage points higher and residual inventory 10% lower than comparable launches using static allocation.

In independent consulting for unnamed fashion and beauty ecommerce businesses

Founder-level review using deliberately bounded evidence

Whether a founder could responsibly increase advertising for the next commercial window or should hold, test, or first resolve an inventory, contribution, or customer-quality problem.

The review did not endorse the 40% increase. It supported a 15% two-week step for in-stock, full-contribution products, with a holdout comparison, contribution threshold, customer-quality stop rule, and the larger move conditional on repeat-purchase evidence.

How the experience applies

Six capabilities behind one bounded recommendation.

Forecasting under uncertainty

Uses ranges, downside cases, and revision triggers instead of pretending the future is exactly knowable.

Demand and inventory planning

Tests whether the business can serve the demand a budget increase is expected to create.

Pricing and promotion economics

Separates revenue lift from margin, contribution, markdown, and inventory effects.

Marketing measurement and incrementality

Distinguishes attributed or correlated performance from evidence sufficient to support the next decision.

Customer quality and repeat purchase

Tests whether future value actually supports weaker first-order economics.

Cross-functional decision design

Reconciles marketing, finance, customer, product, commercial, and supply evidence into an actionable judgment.

Professional recommendations

How former managers and colleagues describe Swaroop's judgment.

These recommendations describe Swaroop's professional work and decision-making. They are not SpendProof customer testimonials.

Swaroop combined strong analytical discipline with a clear sense of commercial responsibility. He did not treat the delivery of a model or dashboard as the end of the assignment. He stayed focused on the decision the business needed to make and whether the available evidence was genuinely sufficient.

He was dependable in high-ambiguity situations, communicated risks plainly, and was willing to recommend a smaller or more controlled action when the evidence did not support a larger commitment.

Abhinav SinghFormer Head of AnalyticsNamshi.comFormer manager

Swaroop consistently looked beyond the headline metric. A marketing or revenue result would often appear attractive in isolation, but he would connect it with product availability, discounting, returns, customer mix, and margin before recommending an action.

He was also very good at working across teams with different priorities. He could challenge an assumption without making the discussion adversarial, and he translated complex analysis into decisions that commercial and operational stakeholders could act on.

Mohammed RFormer Director, Ecommerce OperationsFashion and Beauty, Chalhoub GroupFormer cross-functional colleague

We asked Swaroop to help us assess whether a recent improvement in paid-media performance justified increasing the budget. Instead of simply validating the reported return, he clarified the exact decision, reconciled the evidence from different sources, and identified the assumptions carrying most of the risk.

His recommendation was useful because it led to a bounded action rather than a vague analytical conclusion. We understood what we could do immediately, what exposure to limit, what to monitor, and what evidence would justify a larger commitment.

Tanmay SharmaFormer Growth ManagerNamshi.comFormer cross-functional colleague

Career context

From analytical models to commercial decisions.

Swaroop Rao is the Founder of SpendProof and a commerce decision professional with ten years of experience across enterprise analytics, fashion, luxury retail, and ecommerce. His work has spanned demand forecasting, inventory and supply planning, pricing, promotion, customer value, marketing measurement, and commercial growth. Across these settings, the recurring challenge was not a lack of reports; it was deciding what to do when demand, stock, customer, channel, and margin evidence did not agree cleanly. Swaroop created SpendProof to apply that cross-functional judgment to one live advertising-budget move at a time. Every review is personally completed by him.

  1. July 2015–December 2018

    Mu Sigma Inc — Bangalore, India

    Enterprise analytics, including vendor engagements supporting Microsoft and Walmart.

    Work themes: Demand forecasting, promotion incrementality, scenario planning, metric reconciliation, and decision communication.

  2. February 2019–July 2020

    Arvind Fashions Limited — Bangalore, India

    A multi-brand fashion environment whose portfolio included U.S. Polo Assn., Flying Machine, Calvin Klein, and Tommy Hilfiger.

    Work themes: Demand and supply planning, inventory allocation, replenishment, pricing, markdowns, and margin.

  3. August 2020–December 2022

    Chalhoub Group — Dubai, United Arab Emirates

    A luxury and premium retail environment that included Swarovski, Lacoste, Michael Kors, Tory Burch, and Level Shoes.

    Work themes: Launch allocation, customer value, omnichannel performance, promotion, and contribution-aware growth.

  4. December 2022–January 2025

    Namshi — Dubai, United Arab Emirates

    Work inside a major Middle East fashion ecommerce environment.

    Work themes: Marginal advertising investment, growth decomposition, returns-adjusted economics, customer quality, inventory, and market allocation.

  5. June 2025–present

    Independent consulting

    Work for unnamed fashion and beauty ecommerce businesses.

    Work themes: Founder-level growth, inventory, pricing, customer-value, and advertising decisions using deliberately bounded evidence.

Accountability and evidence

Personally completed. Explicitly bounded.

Every review receives a separate critical QA pass, and the performer is recorded. SpendProof does not describe that pass as independent human review unless a different person actually performed it. Swaroop separately approves every live written review.

SpendProof does not manage the customer's advertising or replace the agency or internal team. It independently pressure-tests one proposed investment decision and supplies a recommendation, questions, guardrails, and evidence conditions for the people responsible for execution.

Screenshots and exports are sufficient only when the decision is tightly defined, the relevant periods and definitions can be reconciled, and the agreed evidence can support a responsible conclusion. SpendProof narrows or declines cases that do not meet that standard.

Customer evidence is used only to assess fit, complete the accepted review, perform critical QA, and address one bounded correction when applicable. Customer-level personal data should not be supplied.

Claims refused

What SpendProof will not pretend to know.

  • A guaranteed revenue, profit, or campaign-performance outcome.
  • One true attribution number when the available evidence cannot establish one.
  • A claim that historical average return proves the return on the next increment of spend.
  • A claim that reported revenue growth automatically means profitable growth.
  • A claim that expected repeat purchase is evidence when it has not been observed sufficiently.
  • Independent human review unless a second human actually performs it.
  • A claim that SpendProof replaces the agency, internal team, finance adviser, or decision owner.
  • Certainty where the evidence supports only a bounded judgment.