How to Validate Financial Forecasting Models

The Forecast Loop: Why Your Numbers Never Match Reality 🧪 Ever notice how your forecasts miss the mark? You're not alone. Often times when I'm building forecasts for a fast-growing SaaS company, we'll spend weeks building models, only to watch it become irrelevant and stale after just a few months. The solution? Stop treating forecasting as a one-time project and start seeing it as an ongoing cycle of testing and improvement. ➡️ EXPERIMENT This is where the cycle begins. This requires structured testing, not random assumptions: Take a financial assumption and isolate it Change one pricing strategy at a time Adjust a specific operational factor The key is controlling your variables. When testing a price increase, don't simultaneously change your sales commission structure. Keep it clean! ➡️ MEASURE Now comes measurement. This means thorough tracking, well beyond a quarterly P&L review. I'm talking about tracking BOTH financial AND operational results: Revenue impact? Obviously. Customer acquisition cost changes? Critical. Renewal rates affected? You bet. Most companies fall short here - they watch revenue but miss the operational indicators that explain WHY the numbers changed. ➡️ LEARN Learning is comparing what you thought would happen with what actually happened. Launching a new product line? Trying a new acquisition channel? Landing a new partnership? These all involve assumption that require validation. But don't just note the difference - understand why it happened. Was your conversion rate overstated? Did it take longer to ramp up that partner? ➡️ UPDATE FORECAST Finally, update your forecast based on what you've learned. Most companies get this backward - they tweak forecasts to match historical results without updating the underlying assumptions. Instead: Adjust the actual input variables Refine how your model weighs different factors Document what you've learned so forecasts get smarter each cycle === The forecast loop focuses on continuous improvement rather than immediate perfection. What's the biggest gap you've seen between forecast and reality? How did you learn from it? Comment below 👇

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.

How to master Forecasting as an Accountant #1 Use your accounting skills to identify business drivers You can prepare financial statements, so you have attention to detail. So, take a closer look at the financials. Dive deep into the variances from one quarter to the next and compare them to the same quarter of the previous year. Consider what happened in other parts of the P&L when revenue jumped or dropped suddenly? Did marketing expenses increase? Or did a new feature come online (R&D cost hitting operational costs)? #2 Understand your business drivers Now that you identified issues to talk about, take them to the budget owners in the commercial team. However, I’d recommend not to ask what they think will happen in the future. That’s because you haven’t yet built enough of an understanding to be able to tell when an assumption is overly-optimistic or pessimistic. Instead, ask them about why drivers changed in the past. Do we have a good understanding of the causes? If so, what are internal and external factors that contributed? #3 Create ranges Look at each major driver and create worst-case, best-case, and expected scenarios. Refer back to what you learned about how much things fluctuated in the past. While not perfect, taking possible ranges from historical data is a good starting point. #4 Consolidate Now, simply put the scenarios for each business driver together and calculate what the overall best, worse, and expected outcome is. Don’t forget to brainstorm probabilities for each case so you can arrive at the average of all scenarios. #5 Ask business partners for their forecast Congratulations, you are now ready to ask your cross-functional business partners about what they think the forecast should be for their respective areas of expertise. #6 Compare business partner forecasts to your version Combining methods is a fantastic way to remove bias. See, your business partners will have likely given you highly conservative figures. That’s because they want to “beat” the forecast. And your top-down approach may lean optimistic since you are farther away from the details. #7 Discuss the differences Most value is generated in this final step. That’s because the second you finalize a forecast it becomes outdated. So, make sure you have enough time to talk to your business partners about the differences between their forecast and yours. Again, it’s not about who is right but about what the risks and opportunities are and - crucially - how to mitigate or use them to your advantage. ❓What would you add? Comment below to help others. -Christian P.S.: If you’d like to learn more from me: Subscribe to my weekly newsletter! Join 20,000+ Finance & Accounting professionals and get: ➢ 3 FP&A ideas from me ➢ 2 insights from others, and ➢ 1 infographic in your inbox ...every Tuesday. 👉 Subscribe at (free): https://lnkd.in/dredP3d5