Why your company always has 3 different revenue figures

Introduction

A familiar scene: at the monthly performance review of a ready-to-wear brand, the marketing director announces €2.3M of revenue. The CFO corrects him — by his figures it is closer to €2.1M. The head of e-commerce, meanwhile, insists that Shopify shows €2.4M.

Within seconds, doubt settles over the table: how can three teams have three different versions of the same reality? Who is right?

The short answer: everyone… and no one. This is not a question of competence or honesty, it is a structural problem affecting almost every retailer and e-commerce platform. Here is why it happens, why it is partly unavoidable, and how to get out of it.

Why your tools produce different figures

The first source of divergence — often misunderstood — is technical: the tools you use every day were never designed to measure the same revenue. Each tool in your stack measures it through its own lens.

Different perimeters, therefore different rules

• Meta attributes revenue to itself through its attribution model, but does not see the cancellations that happen after the order.

• Shopify records the order the moment it lands, but has no native handling of partial refunds or shipping waived as a goodwill gesture.

• Your point-of-sale software records what happens in store, with its own logic for closing the day.

• Your accounting tool only records money actually collected — an order placed but not delivered does not yet exist for it.

Key point One and the same order can be attached to radically different dates: placed, shipped, delivered or paid. In a month that is busy at the end of the period, that difference in dates can be worth several points of revenue.

Similar tools, different algorithms

Meta Ads and Google Ads both want to measure how their campaigns contribute to your revenue. Yet they never give the same figure: each has its own conversion windows (7 days, 28 days…), its own attribution model (first-click, last-click, data-driven…) and its own rules for deciding which campaign "deserves" the sale.

A customer sees a Meta ad on Monday, clicks a Google ad on Wednesday, buys on Friday: both platforms claim 100% of the sale. Add them up and you mechanically get a total above your real revenue. These tools are built to justify advertising spend, not to produce an accounting figure.

Why your organisation makes it worse

The technology explains part of the gaps. The organisation explains the rest — and usually the larger part. Each department defines revenue according to its own needs, and every one of those needs is legitimate.

PerformanceMarketing

The marketer wants to know whether the campaigns work. A €100 order is a success, even if the customer returns the product three days later. They often include shipping, which is part of the converted basket.

Unit of time: date of the click or the session

LegalAccounting

The accountant is after the legal and fiscal truth: only sales that are legally valid and invoiced count. Gift cards are a liability, freebies a cost, returns an immediate subtraction.

Unit of time: closing dates set by law

On the groundStore manager

They steer by the day: as soon as the customer pays, the sale is made. They often include gift cards to credit their sales team’s effort, while web returns handled in store weigh on their targets.

Unit of time: opening day, till closing

Physical flowSupply chain

The logistician triggers revenue at shipment or at delivery, on the product’s original price, without always including the promo codes applied at payment. A lost parcel is a straight loss.

Unit of time: processing date or dispatch note

Take product returns. Marketing often excludes them so as not to "pollute" its conversion rate. Accounting deducts them to the cent, but on physical receipt. E-commerce may count them as soon as the request comes in. Three different rules, three different figures — from the same base of orders.

The same goes for shipping, gift cards, exchanges, commercial discounts and giveaways. Every edge case is a potential source of divergence.

Worth remembering Even with a single, centralised data source, two teams will get different figures if they have not formalised the same calculation rules. The problem is not the data — it is the definition.

Silos first, then the legacy effect

In many companies, IT builds separate pipelines for each department, in answer to individual requests. With no cross-company view, calculation rules are born in silos and never circulate. Over time the layers pile up, contradict each other, and become unreadable to the people using them.

On top of that come rules established five or ten years ago, still running in Excel files or SQL queries nobody dares touch for fear of "breaking something". The result: two people starting from the same raw source unknowingly apply slightly different rules — and diverge at every extraction, without noticing.

Part of the gap is structurally unavoidable

Let us break a myth: you will never have one single revenue figure across your tools, but several revenue figures used according to need. And that is normal. Even though the original transaction is single and indisputable, each department aggregates and filters it its own way, for three reasons.

1. Timing An order placed on 31 May at 11:58 pm will be in May’s revenue for the web team, but probably in June for accounting, after the payment clears.

2. Perimeter Should VAT be counted? Loyalty discounts? Delivery charges? There is no wrong answer, only different contexts.

3. Algorithms Two tracking tools installed on the same site will never have the same capture rate: refused cookies, ad blockers, loading times.

So what is problematic is not that gaps exist: it is being unable to explain them. When two teams have different figures and know why within thirty seconds, that is data maturity. When they do not, it is a problem.

The goal is traceability: that every figure is defined, documented, and that the gaps between definitions are known and explainable.

How to align your organisation around shared data

The solution is to build a shared base on which everyone can then build their own view. In six steps.

1. Centralise the source data, not the aggregates Start again from the raw events: every order line, every transaction, every stock movement. It is the only way to be sure everyone starts from the same point.

2. Catalogue every case before aggregating Standard order, with a discount, with free shipping, mixed, paid by gift card, refunded, with a free product, with an exchange, cancelled… Each case needs its rule. This is usually where the holes turn up.

3. Bring every department around one shared definition A workshop with finance, marketing, e-commerce, logistics and leadership to settle it collectively: what counts as a valid order? Is shipping part of revenue? Are returns deducted on request or on receipt?

4. Document the rules formally Decisions must be written down, versioned and reachable by everyone: that is the data dictionary. Without documentation the rules get lost and the arguments start again from scratch with every new arrival.

5. Allow business views on the reliable base Marketing can still look at its revenue before returns — knowing that it is a steering indicator specific to its concerns, not the reference figure. Each team understands the other’s definition.

6. Put lasting governance in place Name an owner for each key definition: they validate changes, settle disagreements, maintain the documentation. Without formal governance, the silos rebuild themselves in under twelve months.

This is exactly what Biron does upstream of every data project: centralising the sources, making them reliable at the finest grain, aligning definitions across departments — so that every team works with figures that are consistent, traceable and explainable.

Reliable data is the first step towards AI that performs

Accepting that you navigate with three revenue figures amounts to admitting your data is not reliable. Yet most retailers believe the opposite: their tools are market leaders, their dashboards visually perfect. That is the great trap of technology — mistaking a tool’s power for the correctness of its configuration.

An AI plugged into your current tools with no precise frame will fail. It does not natively know what an "in-store return of a web order" is, nor how to treat an expired gift card. With no single reference it will apply a standard logic that distorts your margins. To reason correctly, an AI must not guess your figures: it must be guided by your business rules.