As the saying goes, “numbers don’t lie.” In our experience, however, the financial models that use them often do. It’s particularly the case for ones built for new, disruptive innovations where there are no profitable competitors against which to benchmark.
These models aren’t lying in the nefarious sense of the word. Rather, they unwittingly distort how the business actually works. Once line items on a spreadsheet are disconnected from the underlying commercial logic of how a venture credibly performs key functions—like acquiring and retaining customers, and blocking competitors—a financial model stops being a tool for probing, stress testing, and designing a business model, and one used to simply justify and rubber stamp it.
You can see evidence of this in the high discount rates applied to startup revenue projections by investors. For early-stage startups this can range from 40% to 100%: the models look great on paper, but are rarely trusted at face value.
In modeling and simulation terms, these financial models often have “high resolution” but “low fidelity”: meaning they may be highly detailed, but poorly represent the final venture and how it will actually operate.
At best, the models may have detailed numbers about operations and their costs, but are still disconnected from the fundamental commercial logic of what the venture requires.
It’s easy to spot breakdowns in fidelity in established industries, as these deep commercial fundamentals are intuitively understood. No analyst would think it credible that Ford or Honda could boost profits by closing down dealerships and moving all their sales online direct-to-consumer, no matter how good the numbers looked on paper. And no manager would even think to model it.
But with startups trying to create new markets, there are no deep commercial fundamentals to provide these “modeling guardrails.” And a breakdown in fidelity doesn’t just jump off the page.
That’s because commercial fundamentals aren’t individual line items on a profit and loss statement or cash flow statement, much less individual cells on a spreadsheet that go into the calculations for a line item.
Rather, they cut across a venture. For example, the deep commercial logic for acquiring customers will manifest in the choice of target market and how segments are modeled, the design and cost structure of the product, the go-to-market channels selected and the margins paid, and the skills level and cost of human resources and the training they require.
In the quest to generate five-years of profit and loss and cash flow statements built on hundreds of variables and line items on a spreadsheet, the forest is easily lost for the trees. When that happens, the financial model becomes a hindrance to the innovation process.
In order for disruptive innovators to unlock the power of a financial model, our research shows that they need to approach the process the same way as systems engineers do when innovating new, complex systems.
In engineering, unlike the financial modeling described above, a good model is one that has “high fidelity” and “low resolution”—it captures the essential logic of how something works with the least amount of detail. That lets you simulate and stress test the design’s core strategies under different assumptions.
The key to building a financial model with high fidelity is by doing what systems engineers call “requirements tracing”—assigning performance conditions to a solution’s more detailed parts and operations based on how the core system architecture works.
For example, if a business’s core architecture is built on a theory of change that the best way to overcome customer doubts about a novel product is through one-to-one interactions with people knowledgeable about the product, requirements tracing would define the conditions necessary for the personal interaction to work as planned.
Those requirements are then subjected to simulation to probe for the necessary performance levels for the overall venture to succeed commercially. What market penetration rate do we need to succeed? How effective do our sales team and lead generator engines need to be? What price do we need to charge customers to generate a positive return? And if any of those answers aren’t compelling, what can we build or change to make them credible?
When done this way, the financials become a powerful design tool, rather than an exercise to persuade investors.
We’ll cover more about how to build models with FIT Startup and how to run simulations in the future, but for now, the core takeaway is that rather than constructing financials to paint a rosy picture of your venture, instead use modeling and financial simulations to architect the most robust venture you can.
Focus on high fidelity for true representation, low resolution to minimize detail, and build real financial confidence in that rosy future you hope to bring about.
Want to learn more?
Stay tuned for upcoming webinars and workshops in the lab where you can dive deeper. Or reach out and get in touch!