How to create the assumptions for startup financial models
If you’re like 90% of the entrepreneurs I talk to, your first question when you start building your financial model is “what do I input as my assumptions?” Building forward-looking projections for uncertain businesses is by nature difficult: how do we predict how people will adopt or use something that doesn’t exist? And without good assumptions, isn’t my model useless?
There’s a couple ways to address this:
How do you use assumptions in a model?
In the Standard Financial Model (screenshot below), I organize all of the assumptions for the model on a single sheet, grouping them together by section, label and provide details about each assumption, and use a formatting convention (blue text color) to denote all assumptions. My goal is to easily signify where the assumptions are in the model and what they mean, so that anyone can figure out what’s going on as quickly as possible. ^{1}
I also usually create a separate “Key Metrics” section where summarize key inputs and outputs in the model. I’ll pull out key metrics that are calculated in other places in the models and report them in the Key Metrics section so that it’s easy to see them in one place. I will also create a Key Inputs section next to the Key Metrics to pull out important assumptions from the Assumptions sheet and place them next to the metrics, so that I can easily change key inputs and see how the metrics change, instantly.
In the Venture Fund Model (screenshot below) I have an assumptions page with all the assumptions in the model, but I’ve pulled out a couple key assumptions - fund size, check size, type of deal, etc. - and placed them next to the performance metrics so I can easily see how the inputs impact the outputs. That helps me understand how key assumptions impact the model and helps me quickly scan to see if a model is performing like I expect it to.
Structurally creating assumptions in a model is easy, grounding and justifying assumptions is much harder. Start with a good, clean struture, but don’t get hung up on grounding your assumptions perfectly when you start building a model. As you build your model you’ll change what assumptions you need, add new assumptions, and find out new data to use. Just focus on continuing to build and understand, and come back to your assumptions once you have the outputs of the model ready to evaluate.
You’ll notice that I don’t necessarily show my work in all places in this model. It’s partly because I’ve tried to make the models a little lighter visually and use less lines for calculations, but it’s also because many of the formulas use a mix of index and range functions for a lot of calculations, and it’s cleaner for users to see the final result rather than the many intermediate calcs involved in those types of formulas. ↩
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