Multi-variable scenario analysis for biotech and deeptech startups: focus on Big Movers

Multi-Variable Scenario Analysis: A Survival Tool for Complex Modeling

Your financial forecast lives in a spreadsheet, a single column of numbers marching confidently toward a future you hope to build. But as a founder in biotech or deeptech, you know that path is not a straight line. It is a web of interconnected risks and opportunities, where a delay in the lab, a regulatory hurdle, or a key hire can change your cash-out date dramatically. Relying on a single forecast feels less like a plan and more like a guess. This is where you must learn how to model multiple variables in financial scenarios.

Multi-variable scenario analysis is not an academic exercise for mature corporations. For a pre-seed to Series B startup, it is a critical survival tool. It moves you beyond a single, fragile projection to a resilient understanding of what could happen. This analysis helps answer the questions that keep you up at night: What if our lead scientist takes three months longer to hire? What if a milestone payment is delayed? How do these events combine, and what is the true impact on our runway? By modeling these possibilities, you can make better decisions on fundraising, hiring, and R&D timelines, especially when your data is sparse and the future is uncertain.

Foundational Understanding: Beyond a Simple Forecast

Most founders are familiar with basic sensitivity analysis. You take one variable, like your monthly burn rate, and see what happens if it increases by 10%. This is a useful starting point, but it operates in a vacuum. The real world is not a one-variable problem. A six-month delay in your R&D timeline does not just increase your burn rate; it also pushes out potential revenue milestones and may force you to raise a bridge round at a lower valuation. These variables are interdependent, and treating them in isolation gives a dangerously incomplete picture of your true risk exposure.

Multi-variable scenario analysis acknowledges this complex reality. Instead of tweaking one number at a time, it involves changing a group of related variables simultaneously to reflect a plausible future state. This is a critical distinction from single-variable sensitivity analysis. For deeptech and biotech startups, where progress is non-linear and dependent on scientific breakthroughs, this integrated approach is essential for effective risk assessment. This method is specifically designed for decision making under uncertainty, transforming your model from a static report into a dynamic decision-making tool.

The key is to anchor your model in operational drivers, not just financial metrics. For instance, instead of starting with a generic 'R&D expense' line item, you start with the drivers: the number of scientists, the cost of lab consumables per experiment, patient enrollment rates for a clinical trial, and the capex for new equipment. The reality for most pre-seed startups is more pragmatic: linking your financial projections to these tangible, operational activities makes the model more intuitive and far more powerful. This grounds your forecast in the day-to-day reality of your business.

Part 1: Finding Your "Big Movers" - How to Model the Variables That Drive 80% of Your Risk

Your model could have dozens, even hundreds, of assumptions. Trying to model every single one is a path to analysis paralysis. The goal is to identify your 'Big Movers': the 3 to 5 variables that have both high uncertainty and a high potential impact on your business. These are the drivers that can fundamentally alter your company's trajectory and require strategic adjustments. To find them, ask yourself a simple question for each major assumption: "If this number were to double, or be cut in half, would I need to make a major strategic decision?" If the answer is yes, you have found a Big Mover.

These variables typically fall into three common categories for R&D-heavy startups in the UK and USA:

• Revenue and Growth Drivers: For a pre-revenue biotech startup, the most significant driver is often the timing of a major pre-clinical or clinical milestone. A six-month delay in completing a Phase 1 trial does not just mean waiting longer for a potential partnership payment; it adds six months of cash burn, directly impacting your runway and fundraising needs. Success rates at each stage are also a major variable, as demonstrated in BIO's clinical development success rates analysis. For a deeptech company with a hardware product, a key driver might be the length of the sales cycle for the first enterprise customers or the timing of securing a crucial distribution partner.
• Major Cost Drivers: In biotech and deeptech, primary cost drivers are often specialized R&D talent and capital expenditures (capex). The time it takes to recruit and onboard a senior biochemist, or a delay in the delivery of a critical piece of lab equipment, can have significant ripple effects on project timelines and budgets. For a biotech firm, another Big Mover could be the cost of a contract manufacturing organization (CMO) for producing clinical trial materials. For a B2B SaaS business, a common Big Mover is the sales hire ramp time, which is the period it takes for a new account executive to become fully productive and cover their own cost.
• External Dependencies: These are factors largely outside your control that can have a decisive impact. For a biotech company, a classic example is a potential regulatory delay from the FDA in the US or the MHRA in the UK. Even a simple request for more data can add months or even years to your timeline. For a deeptech hardware startup, these dependencies might be a supply chain disruption for a critical component or a sudden spike in shipping costs that erodes margins.

Pinpointing these Big Movers is the first critical step in building a financial model that reflects the complex reality of your startup. It allows you to focus your attention on the variables that truly matter for forecasting multiple outcomes and making proactive decisions.

Part 2: Connecting the Dots - Building a Dynamic Financial Model for Startups

Once you have identified your Big Movers, the next challenge is building a model where they are linked. Changing one input should automatically and logically update everything else. This is the core of dynamic modeling. It addresses a major pain point for founders: creating a model that connects interdependent drivers like milestone payments and headcount ramps without creating a mess of broken formulas. The most effective method for this is the 'Driver-Based Chain' technique.

This technique involves structuring your model so that core operational drivers dictate financial outcomes, not the other way around. Instead of manually inputting a 'Salaries' number on your P&L, you input the 'Number of Scientists' on a separate assumptions tab. Your financial statements then pull from this single input. For example, changing the 'Number of Scientists' from 4 to 6 on your assumptions tab would automatically trigger a chain of calculations:

1. Salaries and Benefits: The payroll line item on your P&L and cash flow statement increases based on a predefined average salary.
2. Operating Expenses: Costs for software licenses, lab consumables, and other per-employee expenses update accordingly.
3. Capex: The model might flag a need for new equipment or lab space once headcount crosses a certain threshold you have defined.
4. Project Timeline: The R&D timeline might accelerate, pulling forward a future milestone payment and its associated revenue recognition.

What founders find actually works is keeping this structure simple, especially in Google Sheets or Excel. You do not need complex macros or expensive scenario modeling tools. A well-organized assumptions tab with clear labels, where all financial statements link back to these core drivers, is sufficient for most pre-seed to Series B startups. The ICAEW offers 20 principles for good spreadsheet practice that can provide a useful framework. For example, a simple model might have one sheet for 'Inputs' where you define drivers like headcount and project timelines. Your 'P&L', 'Balance Sheet', and 'Cash Flow' sheets then use simple formulas like VLOOKUP or direct cell references to pull those values. The entire model updates from one central location.

A Biotech Case Study: The Cascading Impact of a Single Delay

Consider a detailed biotech case study to see the cascading impact. A startup is developing a novel therapy and anticipates a $1.5M milestone payment from a corporate partner upon completing a key animal study. The 'Driver-Based Chain' shows the impact of a delay. A three-month delay in acquiring a specialized cell line (the operational driver) triggers the following:

• Operational Impact: The study completion date is pushed back by three months.
• Cost Impact: The company incurs an additional three months of full-team cash burn (e.g., $150k/month), totaling an extra $450k in unplanned expenses.
• Funding Impact: The $1.5M milestone payment is now delayed by a quarter, creating a significant cash flow trough in the financial projections.
• Strategic Impact: The cash-out date is pulled forward by several months, forcing the founder to either cut costs aggressively or seek dilutive bridge financing. The timeline for their planned Series A fundraise is now misaligned.

This demonstrates how a single operational shift creates deep financial and strategic consequences. By modeling these interdependencies, you move from simple startup cash flow projections to a powerful tool for strategic risk assessment.

Part 3: Building Scenarios That Tell a Story, Not Just Numbers

With a dynamic, driver-based model in place, you can move beyond generic 'best, base, worst' cases. These labels are often unhelpful because 'worst case' can mean anything from a minor setback to total failure. Instead, the most useful approach is to build narrative-driven scenarios. Each scenario is a short story about a plausible future, defined by a specific, logical combination of changes to your Big Movers.

This shifts the conversation from abstract percentages to concrete strategic discussions. Instead of asking, "What if revenue is 20% lower?" you ask, "What happens if a new competitor launches and our sales cycle for new enterprise clients lengthens by two months?" The second question is far more actionable. In practice, we see that most effective models for startups in the UK and USA use three to four core scenarios to guide decision making and investor communications.

Let's illustrate with parallel examples for a SaaS company and a biotech company.

Scenario 1: The Plan (Base Case)

• SaaS: This is your operating plan, built on optimistic but achievable assumptions. You assume a consistent 8% month-over-month new logo growth, 110% Net Dollar Retention (NDR), and a six-month ramp time for new sales hires.
• Biotech: This is your projected timeline for R&D. You assume your pre-clinical studies will complete on schedule in 12 months, patient enrollment for your Phase 1 trial will meet projections, and your milestone payment will arrive in Q3 of next year.

Scenario 2: Headwinds (Plausible Downside)

• SaaS Narrative (Recession): The story is that customers scrutinize budgets. The enterprise sales cycle lengthens from six to nine months, impacting new logo velocity. Existing customers downsize contracts, causing NDR to fall to 95%. It also becomes harder to hire experienced talent, extending your sales ramp time.
• Biotech Narrative (Regulatory Delay): The story is that regulators request additional data from your pre-clinical package. This adds six months to the timeline before you can start your clinical trial. This delay burns an extra six months of cash, pushes back your next milestone payment, and may require a small bridge round to avoid a cash crunch.

Scenario 3: Aggressive Growth (Upside Case)

• SaaS Narrative (Viral Traction): Your solution gains unexpected traction. New logo velocity doubles. However, this good news has costs. You must rapidly hire more customer support staff, increasing opex. You may need to invest in more robust server infrastructure ahead of schedule, increasing capex.
• Biotech Narrative (Breakthrough Data): Your early clinical data is exceptionally strong, attracting a major pharma partnership offer much earlier than expected. This brings in non-dilutive capital but also requires you to rapidly scale your team and manufacturing capabilities to meet the partner's aggressive timelines, creating new operational strains.

By building scenarios that tell a story, you transform your financial model from a forecasting spreadsheet into a strategic playbook. It allows you to pre-emptively identify triggers for key decisions, such as when to start a fundraise, when to freeze hiring, or when to accelerate investment in manufacturing capacity.

Practical Takeaways for Decision Making Under Uncertainty

Implementing multi-variable scenario analysis does not require a dedicated finance team or expensive financial modeling tools. For a founder-led startup, the focus should be on practical application over perfect, complex accounting. The goal is not to predict the future with absolute certainty but to understand the range of potential outcomes and build a more resilient company.

To begin, focus on these four steps:

1. Start Small. Do not try to model every variable. Run a short workshop with your leadership team to identify the 3-5 'Big Movers' that pose the most significant risk and opportunity to your business. Focus your energy there.
2. Use Operational Drivers. Anchor your model in the real-world activities of your startup, like headcount, experiment timelines, or customer acquisition funnels. This makes the model more intuitive for the whole team and far easier to maintain.
3. Build Chains, Not Silos. Structure your spreadsheet, whether it is in Excel or Google Sheets, so that a change in a core driver automatically ripples through the entire model, from operating expenses to cash flow. Avoid hard-coding numbers directly into your financial statements.
4. Tell Stories with Scenarios. Move beyond 'best, base, worst'. Create 3-4 plausible narratives that combine your Big Movers in logical ways. Use these stories to stress-test your strategy and identify key decision points for fundraising, hiring, and resource allocation.

By adopting this approach, you can turn your financial projections into a dynamic tool for navigating uncertainty. This will enable you to make smarter, faster decisions and significantly improve your ability to manage your runway effectively. To explore this topic further, continue at the Scenario Planning hub for templates and tools.

Frequently Asked Questions

Q: How often should we update our multi-variable financial scenarios?
A: For early-stage startups, review scenarios quarterly and update them significantly after major events like a fundraise, key clinical data release, or a major change in strategy. The model should be a living document that informs your current decisions, not a static report that sits on a drive.

Q: What are the best scenario modeling tools for a startup?
A: Most pre-seed to Series B startups can effectively manage this analysis in Google Sheets or Excel. The key is a well-structured, driver-based model, not complex software. Dedicated financial planning and analysis (FP&A) tools exist but are often unnecessary before a company has a full-time finance team.

Q: How do I present these complex scenarios to investors without causing confusion?
A: Focus the conversation on the Base Case but introduce the other scenarios to demonstrate you have identified key risks and have contingency plans. Use visuals to show the impact on cash runway across scenarios. This builds credibility and shows you are a strategic, risk-aware founder.

Q: Can this analysis be used for operational decisions?
A: Yes, this is one of its primary functions. Use scenarios to set hiring triggers (e.g., "we hire the next engineer when we hit X milestone"), decide on capital expenditures, or determine when to start a new R&D project based on your cash runway in different plausible futures.