The Data Says Sales Are Up. So Why Are We Making Less Money?

Imagine attending a monthly management meeting where the sales team reports a 12% increase in revenue, from $10 million to $11.2 million. Targets have been exceeded, several product categories are performing well, and the dashboard is showing plenty of green. Everything seems to suggest that the business is having a good month.

Then Finance presents another figure. Despite the increase in revenue, profit has fallen by 8%.

How can a company be selling more but making less money?

The discussion quickly turns to possible explanations. Perhaps operating costs have increased, customers are receiving bigger discounts, or the company is selling more lower-margin products. Someone might even suggest raising prices or cutting costs.

These are all reasonable possibilities, but there is a danger in jumping straight into explanations and solutions. We have two numbers telling us different things, yet neither tells us enough about what actually happened.

Before deciding what went wrong, we need to understand what changed underneath the sales number.

When higher sales don't mean better performance

Revenue is an important business measure, but it represents the combined effect of many transactions involving different products, customers, prices and quantities. Two companies can achieve identical revenue growth through very different circumstances, with equally different consequences for profitability.

Consider a distributor selling two broad categories of products. One offers relatively healthy margins, while the other is more price-sensitive and generates considerably less profit per dollar of sales.

Suppose the distributor increases its revenue by 12%, largely because demand for the lower-margin range has grown. At the same time, promotional discounts have reduced average selling prices, while delivery and fulfilment costs have increased because the business is processing more orders.

The sales result is genuinely positive in one respect: customers are buying more. But the additional business may not be contributing enough profit to offset the higher costs and reduced margins. Now imagine another distributor achieving the same 12% revenue growth, but through increased sales of higher-margin products, with little additional discounting or operating expenditure.

Both companies can report the same growth figure, yet their underlying performance may be very different.

Two companies can achieve the same 12% sales growth yet experience very different profit outcomes. What matters is not just how much sales increased, but how that growth was achieved

This is the limitation of interpreting a KPI in isolation. Revenue tells us how much business was generated, but not necessarily how valuable that business was. The figure is not wrong or misleading in itself. The problem arises when we assume that an improvement in one measure automatically represents an improvement in overall business performance.

The same issue appears in many organisations. A customer-service department may reduce average handling time while customers increasingly need to call back because their problems remain unresolved. A marketing team may generate more website traffic without producing more paying customers. In both situations, the reported KPI has improved, but the outcome the organisation ultimately cares about may not have improved with it.

The analytical challenge is therefore not simply to determine whether a number has increased or decreased. It is to understand how that movement relates to the business outcome we are trying to achieve.

Are we asking the right question?

This brings us back to something I emphasise regularly in our data analytics classes: before examining the data, we need to be clear about the problem we are trying to solve.

In our example, it would be easy to begin by asking why sales increased by 12%. That might lead us to examine successful promotions, growing customer demand or the performance of individual product categories.

Those findings could be useful, but they would not necessarily address management's concern. Sales growth is not the problem. The concern is that profitability has deteriorated despite higher revenue.

A more useful problem definition would therefore be: Why has profit fallen even though revenue increased?

That distinction changes the direction of our analysis. Instead of concentrating only on what drove sales growth, we begin examining how revenue, pricing, product mix and costs have interacted to produce the final result.

We might suspect that discounts have reduced margins, that sales have shifted towards less profitable products, or that the cost of fulfilling additional orders has risen disproportionately. These become hypotheses to investigate, not explanations to accept simply because they sound plausible.

This is why Define and Hypothesis come before Prepare and Analyse/Test in the FYT Analytics Thought Process. We first establish what needs explaining, then consider possible causes and determine what evidence would help us test them.

For our distributor, that investigation might involve comparing sales volume and average selling prices across product categories, examining changes in the mix of products sold, and understanding how delivery, returns and other relevant costs have changed.

Revenue growth can be driven by changes in sales volume, pricing and product mix, while costs influence how much profit the business ultimately retains. Understanding these factors helps explain what lies beneath the headline figure.

As the analysis develops, we may discover that sales volume increased strongly, but much of the growth came from products with lower margins. We may also find that promotional discounts reduced the amount earned per unit, while higher fulfilment costs further affected profitability.

We would now have a much clearer picture of what contributed to the decline in profit. More importantly, management could begin evaluating whether those developments were expected, temporary or indicative of a deeper problem.

For example, discounting may have been part of a deliberate strategy to enter a new market or attract customers with strong long-term potential. Lower margins might have been accepted temporarily to clear ageing inventory. Higher delivery costs could reflect a short-term capacity constraint rather than a permanent deterioration in efficiency.

The numbers help establish what happened. Understanding the business context is necessary before deciding whether what happened was good, bad or simply part of an intentional trade-off.

When the measure becomes the objective

There is another dimension to this problem that is easy to overlook. KPIs do more than report performance; they can also influence how people behave.

Suppose the distributor's sales team is rewarded primarily for revenue growth. Naturally, its members will focus on generating additional sales. Promotions, discounts and large orders can all help achieve that objective, even when the resulting business contributes relatively little profit.

From the team's perspective, it may be doing exactly what management has asked. The difficulty is that optimising an individual KPI does not necessarily optimise the overall business outcome.

This does not mean sales targets are inappropriate or that every employee should be measured directly on profit. Different functions have different responsibilities, and some important objectives cannot be captured by a single financial measure.

It does mean that we should understand how the measures we choose influence decisions and behaviour. If the organisation wants profitable growth, rewarding revenue growth without sufficient attention to margins and costs may encourage activities that work against that broader objective.

A similar tension arises when a call centre focuses heavily on reducing call duration. Shorter calls may indicate greater efficiency, but they may also reflect unresolved customer problems. Without examining resolution rates or repeat calls, management cannot confidently determine which interpretation is correct.

The lesson is not that every KPI needs another KPI to police it. Rather, we need to understand what each measure encourages, what it leaves out and how it connects to the outcome that matters.

Do we need more KPIs or better questions?

One possible response is to add more measures to the dashboard. If revenue alone is insufficient, perhaps we should also display profit, margins, selling prices, product mix, fulfilment costs, returns and customer-acquisition costs.

Some of these measures may indeed be useful. But adding every available metric creates another problem: the dashboard becomes increasingly crowded, and management may struggle to distinguish what matters from what is merely interesting.

The objective should not be to measure everything. It should be to identify the measures needed to understand performance and support the decisions facing the business.

For our distributor, revenue and profit provide two important views of performance. Product margins and sales mix may help explain the relationship between them, while selected cost measures can identify where expenses are changing. Together, these provide a more meaningful picture than revenue alone, without requiring management to monitor every transaction-level detail.

The choice of measures should follow the business question. If profitability is deteriorating, we need information that helps explain that deterioration. If the concern is customer retention, the relevant measures will be different. The fact that a metric is available, easy to calculate or attractive on a dashboard does not automatically make it useful.

This is where good problem definition becomes so important. It helps us decide not only what data to analyse, but also what data we do not need.

From a green dashboard to a better decision

Let's return to our management meeting. We began with an encouraging 12% increase in revenue, followed by the uncomfortable discovery that profit had fallen by 8%.

Initially, those two figures appeared contradictory. But after examining pricing, volume, product mix and costs, management now has a clearer understanding of how the two outcomes could occur together.

That understanding does not automatically tell the company what to do. It may need to reconsider its discounting strategy, improve fulfilment efficiency or place greater emphasis on more profitable products. Alternatively, management may decide that lower short-term profitability is acceptable because the business is investing in future growth.

The appropriate decision depends on the evidence, the business objectives and the trade-offs management is prepared to make.

Good analysis goes beyond reporting what changed. It seeks to understand what drove the change, so that management can make decisions based on evidence rather than assumptions.

This is ultimately what distinguishes useful analysis from simply reporting performance. A dashboard tells us what the numbers are doing. Analysis helps us understand what is driving them, why those changes matter and what decisions the evidence can support.

The distinction applies well beyond sales and profitability. We encounter it whenever organisations celebrate increased productivity without examining quality, rising customer numbers without considering retention, or improved service speed without understanding customer outcomes.

None of these measures is necessarily wrong. They simply represent different parts of a larger picture.

Perhaps the more important question is not whether our KPIs are moving in the right direction, but whether the direction we are measuring is actually taking the organisation where it wants to go.

The next time a dashboard shows a KPI turning green, it may be worth asking one more question before celebrating: What did we actually improve?

Mini-Appendix: A Few Useful Terms

KPI (Key Performance Indicator) — A measure used to track performance against an important business objective. Its usefulness depends on how well it represents the aspect of performance we want to understand.

Revenue — The income generated from sales before associated costs are deducted.

Profit — The amount remaining after the relevant costs and expenses have been deducted from revenue. Different profit measures include different categories of costs.

Margin — A measure of profitability relative to revenue or selling price. The precise definition depends on which costs are included.

Product mix — The relative contribution of different products or services to total sales. Changes in product mix can affect profitability even when overall revenue increases.

Hypothesis — A possible explanation that needs to be tested against evidence before it can be accepted as a finding.