Building Financial Models for Fundraising

Most startup financial models are beautiful lies. I’ve reviewed hundreds of early-stage models. And the pattern is clear: → CAC magically drops over time → Churn is “estimated” but never tracked → LTV isn’t calculated or worse, inflated → Headcount costs are wildly optimistic → There’s a “Misc” tab with $1.2M in it Why does this happen? Because founders treat models like investor theatre. Built to impress. Not to operate. The cost? → You raise capital with zero visibility on runway → You overhire and miss your margin targets → You make roadmap bets you can't actually afford → And worst of all? You realize too late that the business model doesn’t work Your model isn’t a pitch prop. It’s your decision engine. A good one should answer: → What happens if CAC jumps 25% next quarter? → Can we delay the next hire and still hit targets? → What’s real runway after expansion churn? If you can’t get those answers, you don’t have a model. You have a spreadsheet in a blazer. Here’s how to build one that actually works: 1/ Start with a clear purpose → What decisions should this model help you make? Hiring plan, pricing strategy, runway clarity? Be specific from day one. 2/ Ground it in real systems → Pull actuals from your CRM, accounting, and payroll. Your model is only as useful as the data it’s built on. 3/ Link your core financials → P&L, Balance Sheet, and Cash Flow should speak to each other. If they don’t, your forecast can’t be trusted. 4/ Segment revenue realistically → Break revenue down by product, customer type, or geography. Model retention, expansion, and churn by cohort — not hope. 5/ Reflect costs with accuracy → Include real team ramp times, founder comp, tech debt, and overlooked ops costs. This is where most risk hides. 6/ Run scenarios, add sensitivity → Best case, worst case, base case. Play with CAC, churn, and pricing levers. Your model should answer “what if?” 7/ Use and update it regularly → If your model isn’t revisited monthly, it’s already outdated. It should evolve with your business — not collect dust post-fundraise. Bottom line? If your model looks polished but doesn’t drive decisions.. Rebuild it. Your business depends on it. PS: Curious, what’s the one metric you check first when you open your model? ——— Need help making the numbers make sense? I’m Mariya. Fractional CFO for SaaS startups. I help founders get clear on what the numbers are really saying. 📩 DM me if your model doesn’t match your reality.

The Model That Knows When To Shut Up Most fundraising models rely on flawed assumptions: **Recency = readiness **Frequency=loyalty **Bigger gifts=bigger love This adds up to a reliable way to pick who gets your next fundraising appeal. But here’s what most models don’t ask: What happens when you actually market to someone? Did they give because of that last email — or despite it? The modeling every org needs doesn’t just track donation behavior, it tracks what donors received so it can learn which donors respond to heavier contact, which prefer light touches, and which ones quietly stop giving when the volume gets turned up. And crucially, it predicts how likely someone is to give in a particular month, based on when and how they’ve been contacted in the past. That makes possible something most models can’t do: A donor-specific pulsing calendar. Here’s what that looks like in its simplified form with individual donors rolled up into groups for ease of visualization. Green = solicit this month. Gray = hold off. Each row is a donor type with its own optimal cadence. Some donors are classic year-end givers. Others are mid-year responders. Some give like clockwork once a year, these are anniversary donors, each with their own preferred giving month. Our model recognizes those patterns and builds a custom cadence around them. This isn’t about who gave recently. It’s about who’s likely to give if we ask now and who’s better off left alone. Most “personalization” in fundraising is surface-level. Change the salutation. Swap the photo. Vary the copy block. But the cadence stays the same. Real personalization means: --Knowing who responds to outreach --Knowing who gives after silence --Knowing who needs a nudge --And knowing who needs a break That’s personalized pulsing, customizing the rhythm, not just the message. Is your agency doing this for you? Here’s your five-point gut check: 1. Does it include promotion history or just giving data? If the model only uses transactions, you’re modeling outcomes, not behavior. 2. Does it learn how each donor responds to being asked? If it treats marketing as background noise or assumes everyone reacts the same way, you're ignoring reality. 3. Does it model irritation and memory? If the model doesn’t learn that too much contact can backfire or that a well-timed appeal can have a delayed payoff it’s missing the behavioral nuance that drives real response. 4. Does it predict when not to ask? A good model isn’t green-lighting solicitations, it knows when to pause to avoid tune-out. 5. Does it personalize cadence not just content? Changing the message to match the person is mission critical. But if every donor is on the same calendar, you're still treating them like a segment, not a person. This approach, whether built internally or with a partner, respects your donors, saves you money, and gets better results. Because sometimes the smartest thing you can do isn’t ask more, it's know when to shut up.

Still spending 40+ hours building financial models? Here's what successful founders do instead... Last week, a founder called me in panic: "Logan, I've spent 80 hours in Excel, have 15 different versions of my model, and investors still aren't convinced..." She's not alone. Most founders waste weeks building complex models that investors hate. After helping 1500+ founders build investor-ready models, here's what successful ones do differently 👇 1. Automate the Basics • Revenue projections • Expense tracking • Cash flow analysis • Unit economics Manual calculations kill accuracy (and your time). 2. Focus on Assumptions • Market size validation • Growth rate justification • Cost structure analysis • Revenue drivers Investors care more about your logic than your formulas. 3. Build for Scale • Flexible scenarios • Easy updates • Clear documentation • Stakeholder-ready views Your model should grow with your business. 4. Prioritize Clarity • Simple interface • Clear assumptions • Key metrics highlighted • Easy navigation Complex models scream "amateur" to investors. 5. Enable Quick Updates • Real-time data sync • Dynamic forecasting • Market adjustments • Performance tracking Static models die fast. Want to build an investor-ready model in days, not weeks? Let's have a quick chat about your financial strategy: https://dub.sh/oetMBFT