Know Your Numbers
Turning Ideation into a Progress Report
Steve Heuring
A startup can raise its first outside capital with plenty of unknowns.
Pricing may still be evolving. The ideal customer profile may be sketchy. Sales cycles, acquisition costs, retention rates, and gross margin may have little or no operating history.
That is normal at angel and pre-seed stages.
But the expectations around financial projections change considerably as a company moves toward institutional venture capital. The financial model must evolve along with the business. Early on, its primary job may be to establish how much cash is needed, how long it will last, and what can reasonably be accomplished before the next financing. As customer acquisition and business performance data accumulate, assumptions can begin giving way to actual results. By Series A, the model should connect enough of the company’s financial and operational drivers to demonstrate how additional capital can turn what has already been established into a materially larger company.
The objective isn't to predict exactly what the business will look like three years from now. No credible investor expects that level of precision from an early-stage company. The model needs to demonstrate what has to be true for the projected outcome to occur and whether the growth strategy, financial projections, and capital requirements support it.
Pre-existing Assumptions
Even at an early stage, decisions about hiring, growth, and fundraising carry financial assumptions that must eventually be reconciled.
A $2 million raise intended to carry the company to its next financing contains assumptions about hiring, expenses, cash burn, timing, and what the company needs to accomplish before approaching investors again.
A plan to hire salespeople contains assumptions about compensation, start dates, onboarding and ramp time, pipeline requirements, conversion rates, and expected revenue.
A financial model forces those assumptions to work together. Revenue growth is an output. A useful model explains what produces it: additional customers, sales capacity, conversion, pricing, retention, expansion, channel contribution, or some combination of those factors.
As the company matures, more of those relationships should become measurable.
Angel / Pre-Seed Requisites
At the earliest stages, most of the financial model will necessarily be based on assumptions. There may not be enough customer or operating history to support sophisticated unit economics, and adding more detail doesn't make speculative numbers more reliable.
A relatively simple cash model is usually enough to start.
It should include current cash on hand, expected revenue and expenses, planned hiring, monthly burn and runway, and reasonable assumptions for pricing and the direct costs associated with delivering the product or service. The capitalization table should also be current enough to establish who owns what before additional securities are issued.
The model should show how much capital the company needs, how it will be used, and how far it will take the business.
More importantly, the financing should connect to the next meaningful state of the company. Capital may fund a production release, initial customers, a technical milestone, initial sales activity, or some other evidence of the endeavor's likely success. Whatever the objective, the amount raised should follow the business plan rather than an arbitrary round number.
At this stage, investors are largely underwriting assumptions. The numbers need to be reasonable and internally consistent, not artificially precise.
Institutional Seed Requisites
Once a company has demonstrable operating data, the financial model can more directly project the performance milestones needed to support the next round of investment.
Historical revenue and expenses can replace estimates. Customer purchase patterns can begin informing pricing, gross margin, customer acquisition, retention, and other unit economics. Hiring and operating-expense schedules can become more specific.
Revenue forecasts should also begin moving away from percentage growth and toward the drivers producing that revenue. Key indicators would be sales pipeline, conversion, sales-cycle length, sales capacity, contract value, and retention rates.
Scenario planning also becomes more useful at this stage. A base case establishes the plan of record, while reasonable upside and downside cases show how changes in variables affect hiring, burn, and runway.
The model is beginning to move from an expression of what management believes could happen to one informed by what has already happened.
Post-Seed Requisites
As a company approaches Series A, more attention shifts to whether the emerging patterns are repeatable.
The model should now incorporate actual-versus-plan performance and more developed measures around customer acquisition cost and payback, retention and expansion, sales-cycle and conversion trends, sales or channel capacity, gross margin, and headcount productivity.
More importantly, changes in those measures should flow through the rest of the model.
If an enterprise sales cycle originally modeled at four months is consistently taking six, the impact extends beyond the revenue forecast. Pipeline requirements change. Sales productivity changes. Hiring assumptions may change. Cash flow slows, affecting burn rate and potentially the timing of the next financing.
That relationship is important. An increasingly sophisticated model isn't simply one with more metrics. It is one in which the major operating assumptions are connected well enough to show the financial consequences when one or more don't perform as expected.
Series A Requisites
By Series A, these pieces should come together in a linked operating and financial model.
Historical performance, revenue and GTM drivers, customer trends, headcount, operating expenses, and cash requirements should reconcile. The P&L, cash flow, and balance sheet should also be connected rather than maintained as independent projections. Financing itself becomes part of the model, including the required capital, runway timing, expected milestones, and the impact of the proposed round on ownership.
At this point, investors are underwriting scale.
A large increase in revenue should be supported by enough pipeline and capacity to produce it. Additional capacity should account for compensation, ramp time, productivity, and the corresponding effect on cash flow. Gross-margin assumptions should increasingly reflect actual delivery costs.
A Series A financing is more than another 18 or 24 months of expenses. The capital needs to move the company to a materially different position before the next round: greater revenue scale, demonstrated sales productivity, a larger installed base, significant product advancements, or some combination appropriate to the strategic plan.
Raising the Bar
The progression from angel capital to Series A is clearest in how assumptions are treated.
At angel stage, an estimated $1,000 customer acquisition cost may be a reasonable planning assumption based on market research and an initial go-to-market strategy.
At seed, there should be enough customer activity to begin testing it.
Approaching Series A, the company should increasingly know the customer acquisition cost, how it differs by segment or channel, whether it is improving or deteriorating, and what historical results support a reasonable forecast assumption.
The same progression applies to pricing, pipeline metrics, retention, gross margin, hiring, customer growth, and other material drivers.
Early financial models are necessarily forecasts about how the business might work. As operating history accumulates, they should increasingly become forecasts based on historical data.
Beyond the Pitch
None of this means the pitch deck should become a financial report.
The presentation needs only enough financial information to make the investment case understandable: current performance, the key economics, the amount being raised, and what the capital is expected to accomplish.
The underlying model should contain considerably more.
Once diligence begins, investors will want to understand where the projections originate, how they reconcile with historical data, which variables materially affect the outcome, and what happens when performance differs from the baseline.
A forecast showing $10 million in revenue is considerably less useful than a model that can explain the customers, pricing, acquisition capacity, retention, headcount, and cash required to produce that return.
A company raising $5 million should be able to show why the operating plan requires $5 million rather than $3 million or $8 million, how each scenario would change the plan, and the expected results before further investment.
Never an Exact Science
Early-stage financial models will be wrong. Precision isn't the standard.
As a company matures, more operating evidence becomes available to explain why actual performance differed from the forecast. Every customer, sales cycle, new employee, renewal, and month of operating history provides additional information about how the business behaves.
A useful financial model absorbs that information. Assumptions get replaced with actuals, cause-and-effect relationships become clearer, and future financing becomes easier to defend. The company has changed, the available evidence has changed, and the proposition investors are underwriting has changed.
As the business progresses, the numbers should demonstrate more than a plausible opportunity. They should demonstrate that executives get it on an economic level and are the right team to invest in moving forward.