What are VC investors actually looking at in your early-stage financial model?

So, you’ve built a model, clunked around in Excel and now you’ve sent it over to a prospective investor to review. That prospective investor will likely send it further to someone in my seat, and say “review this, let me know what you think.”

For those who haven’t spent time as an analyst or in a modeling heavy role, it may seem obscure what a “review” entails. What matters most in these models? What are the red flags? What do investors actually care about?

From a top to bottom perspective, the answers vary greatly based on the type of business and model. For instance, metrics for a SaaS business should look a lot different than those of a CPG company. I won’t go into the weeds in this post on what top line numbers are “good” and what margin profile is “bad.” Instead, I am going to focus on the fundamentals of a model — what actually goes on behind the scenes when evaluating the model as a whole, what investors look for, and some tips on common mistakes.

Here’s my personal high-level review process in 5 steps:

1. I unhide all of your hidden tabs. This is perhaps the most dramatic part of the process. It is eye opening to see the things people leave in their workbooks in the hidden sheets. I often find old projections (or maybe the realistic projections…), internal questions and notes, random research tabs, sensitive data, the list goes on. Always assume someone will unhide your tabs and take a second to just delete them from circulated copies. The same goes for hidden or grouped columns/rows. I immediately reveal these.

2. No one sends me a model that shows an unprofitable business, so my first analysis step isn’t to look at top and bottom-line numbers. I look at your assumptions and evaluate them. In order to have any level of comfort with projections, I need to feel confident that the assumptions are thoughtful and based, at some level, on hard data. I get uneasy when assumptions aren’t clearly labeled, separated, formatted differently, and explained. The best models usually have a tab just for assumptions and inputs, with sources and notes.

3. After dissecting the model inputs, I’ll look at the top and bottom-line numbers. I do simple checks to make sure everything ties, and that the workbook is actually wired properly. I’m also judging your Excel skills — and not just to be snide. Hardcoded numbers, beyond the assumptions tab, are red flags. Scrappy formulas, weird calculation outputs (e.g., you’re not going to have 7.12951 customer support FTEs), random #REF!s and #DIV/0!s, signal the level of thought and review that you have put into the model.

While completing steps 1–3 above, I’m simultaneously trudging my way through your formatting, and cursing whatever odd things you’ve done in the way of color scheme, font, decimal points, etc. I implore you to take the extra 15 minutes to just clean things up — meaning, one font size, one font, deliberate use of highlights and colors, no random underlines, or internal nonsensical notes. This is controversial and personal, but I always prefer no gridlines.

4. My favorite part — scenario analysis. This is when I try to break your model. I’ll rip into your assumptions to see what happens if your revenue only grows 25% annually instead of 65%. Do you run out cash in a month if that happens? Can I delay the hiring of your extra dev team members by 2 months to fix the cash issue? I’ll tinker around with all sorts of possibilities to check on your top and bottom-line numbers along with the strength of your model. If I change one growth factor to a 0, does your model show me a thousand #DIV/0! errors? If so, yikes.

During this step, I will get frustrated if your model isn’t dynamic enough to handle basic adjustments. If I’m trying to change revenue growth, new hire dates, and market penetration numbers, and they don’t flow through the model, I’ll have to wire that up myself, and that can become arduous and frustrating. Models are supposed to be dynamic — stagnant numbers only belong in pitch decks!

5. Finally, after I’ve done all that I can to critique every cell of your hard work, I will look at your summary views and use those to report on my thoughts and add to our internal diligence docs. If you don’t have a summary view (either a quarterly or annual rollup) of your P&L, Income Statement, and Cash Flows, I’ll make it. I love Excel as much as the next analyst, but I appreciate when models arrive with these standard data cuts already included and save me the time.

Some final tips and side notes:

- Gut check what’s happening in the model in plain English — e.g., if you’re increasing FTE count astronomically but your office space costs remain constant, you might be missing something.

- Keep related data together in concise views — e.g., one sheet with hiring plan, salary data, department headcount.

- Show relevant metrics for your business — i.e., your model should show where your money is proportionately going/coming from, and how it aligns with your pitch story.

- Spend time building your model thoughtfully and cleanly, but not too much time, at the end of the day you need to be running a company. There’s no need for an early-stage model to be insanely complex, just functional. If you have existing investors, feel free to lean on their team to help develop and review your model. As you can see from my passion on the subject, I’m more than happy to help a portfolio company whip theirs into shape.

In short:

A model review evaluates far beyond top and bottom-line numbers. Investors are looking for assumptions they can be comfortable with, so make yours easy to understand and follow. Hardcoded numbers, formula errors, and sloppy calculations are red flags. Model functionality and overall thoughtfulness counts (and will benefit you as well). Lastly — don’t forget to delete your hidden tabs 🙂

— Kathleen Keegan, Analyst @ PJC