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You have a quote on the table to build an internal tool, improve the platform you already use, or launch an MVP. The figure is reasonable, the team seems capable and the problem you want to solve is real. And yet, before saying yes, the usual question appears: will this pay for itself?
In many companies, that question is answered by gut feeling. There is a sense that the project will save time, reduce errors, or bring in more sales, but nobody can say how much or when. In others, it is answered with a spreadsheet full of optimistic figures that nobody looks at again after launch.
Neither option helps you make a good decision. The first leaves the investment in the hands of intuition. The second creates a false sense of security.
This article explains how to measure the ROI of a software project realistically: which costs to include, where the return actually comes from, how to calculate it step by step and how the logic changes when what you are building is an MVP. No complicated formulas and with examples you can adapt to your own case.
ROI (return on investment) measures how much benefit you get for every euro invested. The formula is simple:
ROI = (benefit obtained − total investment) / total investment × 100
If you invest 10.000€ and the project generates 15.000€ of benefit over the period you analyse, the ROI is 50%. Nothing new so far.
The problem is that in software, both sides of the formula are harder to pin down than in other investments. Buying a machine has a fixed price and measurable output. Software, not so much:
The cost does not end at delivery: after development come maintenance, infrastructure, improvements and support.
The benefit arrives spread over time: the first months are usually an adaptation period and the full return shows up later.
Part of the value is hard to quantify: fewer errors, happier customers, or a less overloaded team have a real impact, but it does not always appear on an invoice.
The alternative also costs money: doing nothing is not free. Keeping the current process has a cost that often nobody has calculated.
That is why the formula is the least of it. What determines whether the calculation is useful is the quality of the data you put into it.
You cannot measure the return of something that has no clear goal. And this is where many projects fail before they even begin.
A goal like "digitise the company" or "have an app" is useless for calculating anything. A useful goal describes a specific, measurable change in the business:
Cut in half the time the team spends recording orders.
Eliminate billing errors caused by copying data between spreadsheets.
Let customers book without having to call.
Validate whether companies are willing to pay a monthly subscription for the product.
Each of these goals comes with an indicator that can be measured before and after. That indicator is what turns ROI into something real.
If you struggle to make the goal concrete, that is an important signal: the project probably needs a definition phase before you start asking for quotes.
This is the step almost everyone skips and the one that shapes the result the most. Without knowing what the current process costs, any savings estimate is a guess.
Measuring the baseline does not require special tools. It is enough to track a few specific data points for two or three weeks:
How many hours each person spends on the task you want to digitise or automate.
How many errors occur and how much time or money it takes to fix them.
How long a process takes from start to finish (an order, a booking, an incident).
How many opportunities are lost because you cannot respond in time or do not have the information at hand.
With hours, the calculation is straightforward: multiply the hours spent by the real hourly cost of that person to the company (salary, social security and associated costs). The result is often surprising, because manual tasks spread across several people go unnoticed until they are added up.
A common example: three people who spend one hour a day copying data from WhatsApp into a spreadsheet do not look like a problem. Over a year, that is more than 600 hours of work.
The development quote is the visible part of the investment, but it is not all of it. If you only count that figure, the ROI will come out inflated and the surprise will arrive later.
Product definition: the upfront work to pin down what is being built, for whom and with what scope.
UX/UI design: wireframes, prototypes and screen design.
Development: building the software, the integrations and the testing.
Go-live: server configuration, domain, environments and deployment.
Maintenance and evolution: bug fixes, security updates and small improvements. It is an annual cost, not a one-off.
Infrastructure and services: hosting, databases, email services, payment gateways, or third-party licences.
Internal time: the hours your team spends on meetings, reviews, testing and feedback during the project.
Data migration: moving information from Excel, paper, or the previous system into the new software.
Training and adaptation: during the first months, the team works more slowly while getting used to the new tool.
For the calculation to make sense, add up all these costs over a three-year horizon. That is the period in which well-planned software usually shows its real performance and it avoids comparing an investment concentrated at the start with a benefit spread over time.
The return on a software project does not come from its features, but from what they change in the day-to-day running of the business. These are the most common sources, from easiest to hardest to measure.
This is the most direct one. If a process that takes 1.000 hours a year today drops to 300, the difference multiplied by the hourly cost is a real saving. That time does not disappear: it goes into work that adds more value, such as serving customers or selling.
Duplicate orders, invoices with wrong data, shipments to the wrong address, stock that does not add up. Every error has a cost: the time to fix it, the return, the discount to compensate the customer or, in the worst case, the customer who does not come back. If you have measured how many errors happen today, you can estimate how much reducing them will save you.
An online booking channel that works outside business hours, a faster checkout, or quicker responses to quote requests can translate into more sales. It is a powerful return, but also the easiest one to overestimate. It is best to use cautious figures and review them with real data after launch.
When a company grows, manual processes grow with it. If today you need one more person for every certain volume of orders, software that automates that part lets you take on more work with the same team. The saving here is the cost of the hires you no longer need to make.
Real-time information for decision-making, a less overloaded team, a better image with customers, less dependence on the one person who "knows how the spreadsheet works." These are real benefits, but you should not base the decision on them alone. Treat them as an extra that reinforces a calculation that already holds up with the figures above.
With the total investment and the estimated benefit over the same time horizon, you can now apply the ROI formula. But there is a second indicator that many SMEs and startups find even more useful: the payback period.
The payback period answers a very practical question: how many months will it take to recover what I invested? It is calculated by dividing the initial investment by the net monthly benefit (the monthly benefit minus recurring costs, such as maintenance).
The two indicators complement each other:
ROI tells you whether the project is worth it overall.
The payback period tells you how long that money will be tied up, which is key when cash flow is tight.
A project with a high ROI but a four-year payback period may not be viable for a small company. One with a more modest ROI that pays back in a year can be a much more sensible decision.
Let us see how all this applies to a common case. The figures are illustrative, but the reasoning is the same one we use to evaluate real projects.
A distribution company receives customer orders through WhatsApp and email. Two people on the team enter them into a spreadsheet, check stock by hand and prepare the delivery notes.
Each person spends about 1.5 hours a day recording and checking orders.
That is 3 hours a day over 220 working days: 660 hours a year.
At a real cost of 25€ per hour, the process costs 16.500€ a year in time alone.
Errors (duplicate orders, wrong references, incorrect shipments) add up to around 2.000€ a year in returns, shipping costs and correction time.
Definition, design and development of an ordering platform with a catalogue, stock control and delivery note generation: 6.000€.
Internal team time in meetings, testing and training: 1.000€.
Maintenance, hosting and services: 2.400€ a year.
The initial investment is 7.000€ and over three years the total cost comes to 14.200€.
Using cautious figures, the system reduces time spent on orders by 70% and errors by 60%:
Time savings: 11.550€ a year.
Savings from fewer errors: 1.200€ a year.
Total annual benefit: 12.750€.
Three-year benefit: 38.250€.
Three-year ROI: (38.250€ − 14.200€) / 14.200€ = 169%.
Net monthly benefit: (12.750€ − 2.400€) / 12 = around 860€.
Payback period: 7.000€ / 860€ = around 8 months.
If we take into account that the first two or three months are an adaptation period and the savings are not yet complete, a realistic figure is around 10 months to recover the investment.
In this case, the investment pays back within the first year because the project is well scoped and targets a very specific problem. In larger projects, such as a 15.000€ or 20.000€ platform with several roles and integrations, it is common for the first-year cost to exceed the benefit. Evaluating those projects over twelve months is one of the most common ways of ruling out investments that make complete sense in the medium term.
And there are benefits we have not even counted: the two people freed up can spend part of that time serving customers better and the company can grow its order volume without hiring a third person.
Everything above works well when the software improves a process that already exists. But if you are building an MVP, the logic changes: there is no revenue yet and no process to optimise. What you have is a business hypothesis you need to validate.
In an MVP, the first return is not money. It is information. The question is not "how much will I make?" but "how much does it cost me to find out whether this idea works?"
Imagine that building the full product you have in mind would cost 30.000€. A well-scoped MVP, focused on the main flow, costs 6.000€. If the MVP shows that users are not willing to pay, you have avoided spending the remaining 24.000€ on something the market did not want.
That unspent money is a real return, even if it never appears on an income statement. And if the MVP shows the opposite, you have data to keep investing with more confidence or to approach investors with more than just an idea.
For an MVP to have a measurable return, success criteria must be defined before launch. Some common indicators:
Sign-ups: how many people or companies register within a given period.
Activation: how many of those users complete the product's main action.
Retention: how many come back to use it after a few weeks.
Paid conversion: how many are willing to pay, even at an introductory price.
Acquisition cost: how much it costs to acquire each user who becomes active.
A useful criterion looks like this: "if in eight weeks we get 40 companies signed up and 10 of them pay, we move forward; if not, we rethink." That way, the outcome of the MVP becomes a decision, not an interpretation.
The most common mistake at this stage is building an MVP that is too big. The more it costs, the longer it takes to deliver information and the lower its return as a validation tool.
Calculating ROI is not only useful for new projects. It is also very helpful when you already have software that causes problems and you do not know whether to improve it or start from scratch.
In these cases, the baseline is the cost of staying as you are:
Hours the technical team spends fixing recurring bugs.
Time the team loses to slow processes or temporary workarounds.
Features that cannot be added because the technical foundation does not allow it.
Customers or sales lost due to failures or a poor user experience.
With that data, you can compare the ROI of two scenarios: improving the current platform step by step, or rebuilding it. Sometimes gradual improvement has a faster return and less risk. Other times, the cost of maintaining a weak technical foundation makes rebuilding the most profitable option over three years. What matters is that the decision is made with numbers, not out of frustration.
These are the mistakes we see most often when a company evaluates a project before commissioning it:
Counting only the development quote: maintenance, infrastructure and internal time can represent a significant share of the total three-year cost.
Estimating benefits without measuring the current situation: if you do not know what the process costs today, you cannot know how much you will save.
Using optimistic figures: assuming the software will eliminate 100% of manual work or double sales from the first month. It is better to stay on the low side and let reality improve the numbers.
Analysing too short a horizon: over twelve months, almost any software project looks expensive.
Not comparing against doing nothing: staying the same also has a cost and it usually grows over time.
Confusing features with value: adding more features does not increase the return if they do not solve a specific problem. It often reduces it, because it makes the project more expensive.
Not reviewing the calculation after launch: the estimated ROI is a hypothesis. If it is not compared with real data, nothing is learned for the next project.
Calculating ROI does not end when the software goes live. That is when you start having real data to check whether the estimate was accurate.
A simple way to do it is to review the same baseline indicators at three points:
At 3 months: the team has moved past the adaptation phase. This is the time to check whether time savings and error reduction are heading in the expected direction.
At 6 months: there is enough data to recalculate the payback period with real figures.
At 12 months: you can compare the actual benefit with the estimate and decide which improvements offer the best return for the next phase.
This review has an added advantage: it gives you a basis for prioritising how the software evolves. Instead of adding features on instinct, you can invest in the ones that have a measurable impact on the business.
Measuring the ROI of a software project does not require complex formulas. It requires honest data: what the problem costs you today, how much you will really invest (not just in development) and what benefit you can expect using cautious figures.
With those three elements, calculate the three-year ROI and the payback period. If you are working on an MVP, change the question: measure how much it costs to validate the idea and define, before launch, which result will make you move forward.
Software is not justified by how many features it has, but by the problem that stops costing you money. If that problem is well identified and measured, the return is usually much clearer than it seems the first time you look at the quote.
If you are considering a project and want an honest estimate of its cost and potential return, we can help you run the numbers before you invest.
Tell us what you want to build or improve and we will help you estimate its return before you start.
Subtract the total investment from the benefit obtained, divide the result by the investment and multiply by 100. The key is to include every cost (development, maintenance, infrastructure and internal time) and to base the benefit on measured data, not assumptions.
Beyond the development budget, you need to count definition and design, infrastructure, licences, annual maintenance, the time your team spends in meetings, testing and training, data migration and the adaptation period when productivity is not yet at full speed.
It depends on the problem it solves and the size of the project. Well-scoped software that removes repetitive manual work can pay for itself in less than a year. Larger platforms usually take longer. That is why it is best to calculate it over at least a three-year horizon, since the first year concentrates most of the cost.
In an MVP, the initial return is learning rather than money: validating whether there are users willing to use and pay for the product. It is measured against criteria defined before launch, such as sign-ups, activation, retention, or paid conversion and by the money you save if the idea does not work.
Measure it before deciding. Track for two or three weeks how many hours your team spends on the task, how many errors occur and how much they cost to fix. Without that baseline, any ROI calculation is a blind estimate.
Yes, with care. Customer satisfaction, fewer errors, or the ability to grow without hiring can be translated into concrete indicators. It is best not to base the whole decision on them and to treat them as an additional benefit.
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