Investors do not fund projects. They fund evidence.
A funding round, a due diligence process or a negotiation with a bank for financing all have something in common: the other side is going to ask questions about the business that you have to be able to answer with data, not with slides.
"What is your customer acquisition cost?" "What is your 12-month retention rate?" "What is the real margin per product line after direct and indirect costs?" These are questions any competent investor asks in the first few meetings. Being able to answer them with solid, verifiable numbers is what separates a serious conversation from a pitch deck.
What an investor looks for in your company's data
Beyond the specific numbers, an investor looks for two things when reviewing a company's data: coherence and traceability.
Coherence means the numbers tell a story that makes internal sense. If you say sales grew 40% but operating costs grew 80%, that needs an explanation. If the gross margin you present does not match the figures in the income statement, it creates instant distrust.
Traceability means you can show where each number comes from. Not just the result, but the methodology. If you say LTV (customer lifetime value) is X, you have to be able to explain how you calculate it, what data you use and why that methodology is the right one for your business model.
A company that can present its key metrics with coherence and traceability signals that it has the business under control, whatever its size.
The metrics you cannot skip before a round
The list varies by sector and type of investment, but these are the ones that come up in practically every conversation with investors at mid-sized companies:
- MRR / ARR (monthly or annual recurring revenue) if the business model has it
- CAC (customer acquisition cost) by acquisition channel
- LTV (customer lifetime value) and the LTV/CAC ratio
- Churn rate monthly and annual, with the calculation methodology explained
- Gross and net margin by business line, not just the aggregate
- Burn rate and runway if the company is not yet profitable
- Product metrics relevant to the model (DAU/MAU, NPS, conversion rate)
If you cannot calculate any of these from historical data over the last 12-24 months, it is a red flag the investor will pick up on.
The difference between a company that raises and one that does not is rarely in the product. Often it is in the ability to prove the business works with numbers, not with narrative.
The most common mistake: preparing the data for the presentation
The mistake I see most often is preparing the data specifically for the investor presentation. The numbers get consolidated, the spreadsheets get cleaned up, an ad hoc dashboard gets built for the due diligence process, and it all gets presented as if it were the normal way the company manages its information.
The problem is that investors notice. When they ask something outside the prepared script, the company cannot answer on the spot. When they request access to the raw data, there are inconsistencies with what was presented. When the due diligence process drags on, keeping the "prepared" data coherent becomes impossible.
The right way is to have the data well structured before the process even starts. Not as preparation for the investment, but as a normal part of how the business is run. An investor who sees that a company already has a real view of its metrics does not need to be convinced the business is well managed. They see it on their own.
Where to start if the round is 6 months out
Six months is enough time to get the data in order if you start now. The process has three phases: first, define which metrics are key for that specific business model and how they are calculated; then centralize the data sources and automate the calculation of those metrics; finally, build the visualization that lets you present them clearly and answer questions about them in real time.
The result is not just a better presentation. It is a system that stays useful after the round closes, when you have to report to investors, when you have to make resource allocation decisions, and when the next round comes around.