Financial modelling cook book for a SaaS B2C product: real template attached – guest column

Building a successful SaaS B2C product requires not just a great idea but also a solid financial model. Founders often struggle with forecasting revenue, managing costs, and ensuring long-term sustainability. Oleksandr Ulytskyi, CFO at Mate Academy, has developed a structured approach to financial modeling that helps early-stage ventures navigate these challenges. 

In this column, AIN shares his insights and a real, usable template that any founder can adapt to their business.

What is a financial model?

Even if you are just getting started with a B2C SaaS startup, it is highly recommended that you build a financial model of your product. Guess why?

No, do not cheer on your VC lords and masters. Simply put, a financial model is an attempt to look into the future of your product. 

Why do I need a financial model? Not convinced yet, right? Four reasons:

1. Decision Making: A financial model is one of the most powerful decision support tools. Modeling the economics of your SaaS product helps to identify hidden imbalances or risks early on.
2. Key Metrics: Sometimes as a founder you can think of what are important metrics, but modeling actually helps to stress test different operational drivers and keep in mind how changing A affects the results in B and C.
3. Business Case: The best way to assess the monetization prospects of a SaaS product is to model cash inflows and outflows. This will help you understand when and under what conditions your project will become cash positive.
4. Credibility: Last but not least. Having a proper financial model of your SaaS product along with a well-polished pitch deck adds credibility in the eyes of potential investors.

What to model? Ok, I hope you are getting a sense of the value of modeling, here are 4 things to model for your SaaS product:

• Users
• LTV
• Returns
• Cash flows

How to model? Before we dive into actual modeling, here are some housekeeping rules:

Flexible: 

• Keep all manual input assumptions on a separate tab.
• Do not use hard-coded numbers in formulas.
• Have mappings and use formulas such as SUMIFS, INDEX & MATCH to get data from one place to another.

Dynamic: 

• Model for long time periods such as 3-5 years.
• Model at granular levels such as months to analyze how metrics perform in a dynamic environment.

Integrated:

• Connect related sections of the model so that outputs from one section serve as inputs to another.
• Avoid duplicate manual input of the same assumption/driver in different sections of your model.

Intuitive and clean:

• Use color coding for manual inputs vs. calculated, primary output columns vs. secondary columns.
• Use grouping instead of hiding for columns and rows.
• Label and name everything, always give units of measurement. That way, every user can clearly understand what's in front of them.
• Slice model into homogeneous sections. You can model users, monetization, and costs separately.
• Use a top-down approach. Have key spreadsheet outputs at the top of the spreadsheet.
• Have a "summary tab" that summarizes key outputs from all sections on one page. Have key manual input assumptions on this page so users can play with them and see the effects immediately without having to go through the entire model.
• Use checks!

Ok, with the above housekeeping rules in mind, let's dive into modeling your user base as this is a core for both monetization and costs.

Start the model with a total number of users. This can be MAU or DAU, depending on which is more relevant to your case and the time granularity you are using (days or months).

Use a benchmark or analytical approach.

Benchmark approach: Find an existing peer SaaS product and use as input their user base growth dynamics or an expected portion (%) of that growth.

Analytical approach: To fully model growth, you can use the S-curve technique. The only assumption you need to make is a final target number of users in, say, 5 years. The S-curve divides your product lifecycle into 3 phases: (1) Early - slow growth at the beginning and typically when you reach ~5% of your final target, (2) Growth - rapid acceleration as your product gets attention and at this stage you get to 95% of your final target, (3) Maturity - when your product gets to run-rate state and growth slows down and eventually you get to 100% of your final target. This is where you play with the length of the Early and Growth stages.

Once you have the total number of users at, say, a monthly level. Break this down into relevant categories that affect the economics of your SaaS product, such as free or paid.

Once you've modeled users, it's time to get a sense of what revenue you can expect and what your expected LTV is.

Make assumptions about the distribution of your paid user base by pricing plans.

Make assumptions for chargeback rate.

With your user distribution, churn rate by user category, pricing, chargeback rate, and rebilling rate, you can model LTV. LTV tells you how much you will earn for the user's entire lifetime with your product.

For example: Say you have 1 user, initial price is $1, rebill is monthly, rebill price is $3, churn rate is 30%, chargeback rate is 1%. LTV= (1*$1+1*(1-0.3)*$3+0.7*(1-0.3)*$3+0.49*(1-0.3)*$3….)*(1-1%).

Knowing your LTV is critical to getting a sense of the unit economics of your SaaS product, i.e., what cost base would make it profitable.

But revenue is worthless if you do not understand how much your product can make. You must include costs in your model.

Typically, performance marketing is the main cost for B2C SaaS products. A simple and powerful metric is ROAS - Return on Ads Spent. ROAS=LTV/CAC-1.

CAC. You can approach it in two ways:

1st, have it as a manual input target CAC at different stages (Early, Growth, Maturity). This way your ROAS will be the resulting number of the two.

2nd, have the ROAS rate as a manual input target at different stages (Early, Growth, Maturity). This way you reverse engineer what CAC you need to stick to get to the target ROAS levels.

Which one you choose really depends on how much input information you have. If you already have data on your CAC, then you're better off going with option 1, if you don't have enough data, then go with option 2, and once you evaluate your marketing campaign, you can check your model to see if the pass is right.

With ROAS over different time frames 3-6-12 months. This will allow you to understand how long you need to keep your user to get returns. And the longer it takes, the more risk you are taking. In the early stages, look for 6 months of positive ROAS.

The model is taking shape, you have user base dynamics, revenue and ROAS expectations. But sustainability is tight and modeling your burn rate is critical.

Add assumptions for overhead costs: like R&D costs, cloud expenses, admin personnel, rent. Typically these are semi-fixed costs that do not scale with your SaaS product revenue, so you can keep them really fixed in your model or have a small scaling rate at different stages.

Consider any variable costs, such as payment provider fees that would be charged on each transaction, typically around 3.5%.

Now that you have modeled your inflows - revenue from subscribers - and outflows, such as marketing costs and overhead, you can track your monthly net cash, whether in or out.

Track Your Monthly Cash Balance Beginning cash + inflows - outflows = ending cash.

Congratulations you are all set, but what if some of the assumptions can go south, what would it mean for your burn? For this stress test your model with sensitivity analysis and a few scenarios.

Sensitivity analysis: 

• Identify critical assumptions e.g. churn.
• Identify critical outputs e.g. ROAS
• Using Excel data tables, perform sensitivity analysis, e.g. showing how ROAS changes at different churn rates.

Scenarios: 

• For critical assumptions, have at least 3 possible values and analyze model performance in dynamics under these different values.
• Think in terms of Base, Worst, and Best cases - this will show you how much burn and investment you may need at different points of product adoption.

Is there a good template for the model?

Yes, here you go [link to Gsheet]

Author: Oleksandr Ulytskyi, CFO at Mate academy