How do we build a business case for a business with no history?

Most mature companies are good at forecasting. Their finance teams know the markets, the customers, the competitors and the cost structure, and they have years of results to check a forecast against. When a new product line or a capacity expansion is proposed, leadership expects a number, and it expects the number to be close.

That expectation follows new ventures into the review room. A team exploring a business that does not exist yet is asked for a business case: a revenue forecast, a margin and a payback date, and it is judged on how confident those figures look. The team can probably produce them, but not with any accuracy, because there is nothing to extrapolate from. And judging the model on how precise it looks misses what it is actually for in an exploratory context.

Why the core business expects precision

In the core business, a forecast exists to commit resources. It sets production plans, hiring, capital spending and targets that people will be held to. Being wrong is expensive, and the forecast’s purpose is to minimize risk and waste.

Precision is a reasonable demand there because it is achievable. Most of the inputs are known quantities, and the unknowns are variations on patterns the company has seen many times.

Why that does not transfer to innovation and exploratory growth

A new venture breaks those conditions. There is no history to extrapolate. The market may be unfamiliar, the customer different and the business model new to the company.

The decision is different too. Early investment in an exploratory venture should be small and staged, released as evidence accumulates rather than committed in one block against a plan. And so is the cost of being wrong. A wrong forecast in the core means excess inventory or a missed target. A wrong assumption in an exploratory model, caught early, is cheap to correct, and finding it is exactly what the early stages are for.

The expertise is different as well. In the core business, forecasts are built by a dedicated finance team with deep accounting and financial modeling experience. A venture team rarely has that support, and its members may never have built a financial model for a new business from scratch. The gap is real, and it is one more reason a spreadsheet built for the occasion tends to fall short.

Earlier in this series we described how a new venture inherits strategy choices from the core business without anyone deciding to. The forecasting method, and the expectation that comes with it, are inherited the same way.

What the model is actually for

Take the precise forecast off the table and the financial model still has at least four jobs, all more useful at this stage.

It compares strategy hypotheses. A venture might sell a subscription to many small firms or a license to a few large ones, through its own sales team or through partners. Those are different businesses, with different revenue timing, margins and funding needs. Modeled on the same basis, they can be compared before either is tried in the market. Our post on what-if modeling covers why comparing versions in parallel beats testing them one at a time.

It builds understanding. Specifying how the business makes money forces questions the team would otherwise skip: who pays, how often, how many can be reached, and what it costs to serve one more customer. Many of the first answers are guesses. Writing them down is how the team learns which ones it does not know.

It finds the levers. Often a handful of inputs account for most of the variation in the outcome. Knowing which ones, whether price, adoption rate or the cost of acquiring a customer, tells the team where its strategy choices matter most.

It exposes the assumptions that matter most. An input with a large effect on the outcome and weak evidence behind it is the next thing to test. Our post on turning uncertainty into action covers that prioritization.

None of those jobs needs a precise number. Each needs a model whose logic is visible and whose inputs can be changed.

What that changes about how you build it

Match the level of detail to the decision. An early question is whether the opportunity could plausibly be big enough to matter, and a quick model answers it. Start from the outcome the business would need to reach at maturity and ask whether the assumptions it implies are realistic. Add detail as evidence and investment grow, not before. Our post on strategy modeling in practice covers how fidelity rises across stages.

Use ranges, not single numbers. Where an input is uncertain, state it as a range. A single figure hides what the team does not know; a range shows it. Running the ranges through a Monte Carlo simulation turns them into a distribution of outcomes instead of one point. Our posts on modeling beyond the single-point forecast and modeling uncertainty cover how.

Build it from the venture’s own choices. Build the market up by segment, from how many potential customers there are, how many can be reached and how many are likely to adopt, rather than taking a small share of a large published figure. Our post on the 1% market-size slide and our guide to market sizing cover the method.

Then derive costs the same way. In the core business, operating costs are known and are often planned as ratios, such as sales and administrative expense as a share of revenue. A new venture has no operation for those ratios to describe. Its costs follow from its own choices: what the solution takes to produce, deliver and support, and how it is sold, which an earlier post in this series covers. A direct sales team, a channel partner and an online model imply very different headcount, timing and spending before the first sale. Borrowing the core’s ratios imports the core’s operation into a business that will not have it.

Why the usual tools leave gaps

Most business cases for new ventures are built with tools designed for something else.

Spreadsheets are the default. They can model anything, which is their strength, but every model is built from scratch for the occasion, its logic is hard for anyone else to follow or check, and it usually carries single numbers because ranges take expertise and add-ins most teams do not have. Changing the target segment or the sales model often means rebuilding it.

Corporate planning and budgeting systems are built for the core business’s operating plan. They work from history, expect precise figures and follow the structure of the existing business, which is the frame this post argues against.

Business plan templates and startup forecasting apps are quicker to fill in, but they assume a standard business model and ask for point estimates, and they are not connected to the strategy choices the numbers are supposed to reflect.

What is missing is a model built from the venture’s own strategy choices, carrying ranges throughout, where changing a choice means changing an input rather than starting over.

There’s an app for that: how Growth Forge® Software addresses this

Growth Forge supports the business case from a quick first read to a full P&L. The Back of the Envelope P&L gives the first read. The detailed financial modeling tools then work as one model, each drawing on the inputs of the one before, so nothing is re-entered as the case deepens. Because the structure of the model is built into the tools, the team does not have to design it from a blank spreadsheet, which lowers the modeling expertise needed to produce a sound case.

Back of the Envelope P&L is the triage. It works in reverse: start from the mature-state outcome the business would need to deliver, derive the assumptions that implies, and judge whether they are realistic. It uses ranges and statistical analysis, and it highlights the highest-impact assumptions and where the evidence behind them is weakest.

Market Segmentation builds the market as a tree of segments, top down or bottom up, in consistent units. Each segment carries low, expected and high values, a growth rate and an evidence level, and tree and treemap views show where the market actually sits. Monte Carlo and sensitivity analysis show which segment assumptions matter most, and the same segments carry through to the needs and competitive work, so the market the team sizes is the one it has researched.

Market Opportunity Sizing turns those segments into a revenue forecast. Offerings are bound to segments and run through a funnel from total to serviceable market to expected share, under a sales model such as subscription, usage, licensing, physical product or services, with one-time, recurring or hybrid pricing. Reach, share and repurchase are modeled explicitly rather than folded into a single percentage. The result is a distribution of revenue outcomes, with the inputs ranked by how much they move it, so the team can see whether the forecast depends on price, adoption or reach.

Unit Economics Analysis layers per-unit costs onto that model by production unit, sales model and market, without re-entering anything, including direct costs, headcount and capital depreciation. It shows gross margin by product, sales model and market in waterfall, trend and distribution views, and its sensitivity analysis, including two-factor grids, shows which cost and price drivers decide whether each sale makes money.

Operational Expense & Cashflow Analysis adds operating expenses, fixed or driven by activity, that start and stop on triggers such as project start or first revenue. It produces the full P&L, discounted cash flow, cumulative cash flow and the funding requirement, and it identifies when the venture reaches market and when it turns profitable.

Monte Carlo simulation and sensitivity analysis run through the detailed tools, so outputs are distributions rather than points, and the inputs that drive them are ranked. Those are the assumptions that go to the Assumptions Manager to be prioritized for testing. And because every input is explicit, changing the target segment or the sales model means changing an input, not rebuilding a spreadsheet.

The output is not a forecast to defend. It is a model of how the venture would make money, with its uncertainty visible and the assumptions that matter most identified.

What no software can settle

A range is only useful where the organization can receive it as a range. In many companies a number in a deck becomes a promise the moment it is presented, and the person who presented it is measured against it. Under those rules a team will either produce false precision or avoid producing numbers at all, and neither helps anyone decide.

Changing that is a leadership decision: what a venture review expects to see in the financials at each stage, and whether a wide range early on is read as honest or as weak. It has to be set and held by the people who run those reviews, usually the executive sponsor and the person running the innovation function. An outside perspective can help define those stage-by-stage expectations and make the case for them, but the people who run the reviews have to hold them.

Putting it into practice

When a new venture’s business case goes to review, a few steps keep the model doing its job. Build the market up by segment, and derive costs from the venture’s own solution and sales model. State uncertain inputs as ranges and read the distribution, not a single number. Identify the few inputs that move the outcome most, and check how much evidence sits behind each one. These steps can be worked through with a spreadsheet and a framework, facilitated by an experienced consulting firm, or with purpose-built software such as Growth Forge, where the financial modeling tools provide the structure, the ranges and the analysis. The financials still matter. At this stage their value is in what they reveal about the business, not in the precision of the bottom line.

If the harder question is whether your review process can read a range as a range, our Innovation Capability Assessment is a short scored read on where that stands.

Next in this series: how to find out whether our own organization will reject a venture before the market has the chance.

BRI Associates helps companies grow by drawing on decades of practitioner experience in corporate innovation and new business development — practitioners, not pundits or academics — through direct consulting, training workshops, and Growth Forge® Software, built for the unique requirements of corporate innovation and growth organizations.