Avoid These Mistakes In Your Start-Up’s Financial Model
While investment bankers have perfected the art of creating detailed and consistent 3 Statement Models, DCF, and LBO models, the start-up landscape is quite chaotic. 3 statement models are obviously necessary for any company, yet the insights you can get from the income statement of a pre-revenue start-up are limited. What most early SaaS start-ups lack is a robust forecasting model, or what I like to call a “Growth Model”. This model should demonstrate to investors how much growth you can achieve with a given investment. Ideally, enough to justify a strong next funding round.
These insights are essential for answering the crucial question: “How will we achieve growth?”
In this short article, I will highlight some the main mistakes I have come across in templates and models. They are:
The 8 Mistakes:
Mistake 1: Making Month-on-Month Growth an Assumption
Mistake 2: Making Sales a Function of Revenue
Mistake 3: Not Understanding how Churn Works
Mistake 4: Mixing Up Revenue and MRR
Mistake 5: Neglecting Ramp Up Time
Mistake 6: Assuming your Sales Team is Going to Stay Around Long
Mistake 7: A (not so minor) Nit-Pick on Discounting
Mistake 8: A (surprisingly common) Nit-Pick on SeasonalityMistake 1: Making Month-on-Month Growth an Assumption
This is probably the most common mistake and renders the entire SaaS forecasting model useless. It might make sense when analysing large companies with a track record of year-on-year (YoY) growth to build a simple Discounted Cash Flow Model. For start-ups, where projected growth is the primary reason investors examine the financial model, growth should be the outcome of the model, not the starting point.
Mistake 2: Making Sales a Function of Revenue
To achieve growth, you need sales. While there may be a few companies worldwide that have successfully executed “product-led growth,” in most cases, your revenue growth will depend on your sales team. A subpar company with a strong sales team can grow, while a great company with a crap sales strategy will fail.
Unfortunately, most SaaS financial templates fail to establish revenue as a function of sales (i.e., if I invest X in sales, I should get Y in revenue). In fact, many financial templates model sales as a function of revenue (i.e., if the revenue is X, we must have spent Y on sales). This confusion often arises from linear, repetitive Excel formulas that make simplistic month-over-month growth assumptions to create a visually appealing “hockey stick” revenue graph. However, this approach is essentially useless for any meaningful forecasting.
Mistake 3: Not Understanding the Purpose of Churn
Churn is a vital metric for understanding a SaaS business and forecasting growth. While it may not be possible to accurately forecast your own churn in the early stages and assumptions need to be made, at some point, churn should be derived from comprehensive cohort analysis. However, it’s important to know when and where to apply churn in your model. When people confuse new MRR, cumulative MRR, and revenue, they often misapply churn in the model and sometimes apply the churn rate multiple times.
Here’s an example: Churn should only be applied to the MRR attributed to a specific account or cohort, gradually reducing the MRR to zero over the account’s lifetime (e.g., 10% of the initial MRR/ARR every year). It should be the MRR that tends to zero, not churn itself. For instance, if I have a 1000 ARR cohort and a 10% churn rate, it should be applied in such a way that after 10 years, the ARR is zero. Simply multiplying it by 0.9 each year would result in 340 ARR remaining after 10 years. This difference is significant and should be modelled correctly.
Mistake 4: Mixing Up Revenue and MRR
Each month, a salesperson ideally generates new sales and increases the company’s Monthly Recurring Revenue (MRR). For instance, if my team of five salespeople each has a quota of 1000 EUR, they should ideally generate 5000 EUR of MRR. However, it’s important to note that this does not mean the start-up has revenue of 5000 EUR. Many SaaS companies overlook past MRR and simply equate Revenue to (new) MRR. It may sound absurd, but I have seen this mistake in readily available SaaS templates found on Google and in numerous early-stage start-up financial models I have analysed.
It’s crucial to differentiate between cumulative MRR and new MRR, recognizing that salespeople don’t have to be consistently selling for the company to generate revenue. That’s the point of MRR. They are selling to grow MRR and to grow the company.
Mistake 5: Neglecting Ramp Up Time
Most SaaS financial models I come across completely disregard the concept of ramp up. In fact, among the publicly available templates, I have only seen the renowned Christoph Janz Financial Model take ramp up into account. However, even in that template, the ramp up inputs lack the desired level of dynamism and insight. When seeking investment for sales, it’s essential to have a deep understanding of how sales teams work. You can’t simply assemble a sales team and expect revenue to start flowing at max. Quota from day one. The larger your Average Revenue Per Account (ARPA), the longer your typical ramp up periods will be, which can even extend up to a year. The Janz template has a fixed 4-month ramp up, which, in reality, can be quite optimistic, as even the folks at Point Nine should acknowledge.
Ideally, your model should incorporate a ramp up model that factors in the various product tiers/plans you sell and the different salespeople responsible for selling these products. Nevertheless, just starting with some assumptions about ramp up times will make your growth model more detailed and useful to an investor who wants to understand how long it will take for you to achieve specific growth targets.
Mistake 6: Assuming your Sales Team is Going to Stay Around Long
While the Janz model takes ramp up periods into account, it overlooks a crucial aspect that, in my opinion, is almost always missing and renders most of these templates practically useless. On average, salespeople stay at a company for around two years before seeking new challenges, and these durations are becoming shorter, not longer. This phenomenon is known as attrition. Mature companies can have attrition of around 35%, meaning that your sales team is losing 35% efficiency every year if it replacing the people that are leaving.
This reality implies that your growth will either plateau as you lose your entire sales team or you will have to hire new salespeople. Balancing sales team attrition with the ramp up periods of new hires is crucial for modelling your growth, yet I would estimate that it is absent on most if not all publicly available templates.
Mistake 7: A (not so minor) Nit-Pick on Discounting
One common mistake I still frequently observe in models and templates is how annual figures are converted into monthly figures, i.e. how people overlook discounting when breaking down KPIs into smaller periods and vice versa. It’s important to understand that 12% annual churn does not translate to 1% monthly churn. When you discount it over 12 periods, it actually amounts to around 0.95% churn per month. While this may seem like a small difference for one month, it can make a significant impact over the course of 12 months and beyond. (However, some metrics like MRR do get simply multiplied by 12 to obtain the annual metric, ARR. It’s crucial to know when to discount and when to divide/multiply).
Mistake 8: A (surprisingly common) Nit-Pick on Seasonality
This particular mistake might seem more technical, but it is a common error that I believe is worth highlighting. Seasonality is often included as an assumption in financial models to avoid linear repetition in formulas. It makes sense to consider seasonality for industries like fashion or food, yet many people struggle to understand how and when to apply the seasonality multiplier correctly.
In most models, revenue metrics for a previous period (-1) are multiplied by a Month-on-Month growth assumption to calculate the new revenue metric for the current period (0). This new metric is then multiplied by the seasonality multiplier specific to period (0), resulting in the seasonally adjusted, actual revenue metric for period (0).
The mistake then occurs when transitioning to the next period (+1). The model will take the seasonally adjusted actual revenue metric from period (0), multiply it by the Month-on-Month growth assumption, and consider it as the new revenue metric for period (+1). This new figure is then multiplied by the seasonality multiplier specific to period +1, leading to the seasonally adjusted, actual revenue metric for period (+1).
Did you spot the error? It is small, but significant. It lies in treating the revenue of period (+1) as a function of the seasonally adjusted revenue of period (0), when, in fact, we need to “de-seasonalise” the revenue of period (0) before forecasting period (+1) revenue. After all, January sales should be independent of how well the Christmas season went. Therefore, the Month-on-Month growth rate of 10% (for example) should be applied to the previous season’s revenue figure, of which we have removed the impact of the Christmas season.
To illustrate with a simple example: Let’s assume a 10% Month-on-Month growth rate. If I sold 800 in November and 1000 in December, I should still apply the 10% growth rate to 880 (the de-seasonalised December figure) to predict my January sales. This would result in a January sales forecast of 968. The mistake would be to predict 1100 for January, a mistake which occurs in almost all models I have seen that model seasonality.
Wrapping Up
While reading through these points, you may have thought, “There’s no way I would make these mistakes.” However, it’s highly likely that you have either used a template with similar mistakes or have overlooked certain details yourself, and this piece serves as a reminder of how crucial those details are.
I have started working on my own Excel sheet called the “SaaS Growth Capacity Model” or something similar to that. This model will incorporate the correct methods for incorporating churn, attrition, ramp-up, and all the other points I’ve discussed above. I will write more blogs in the future that build a strong excel model that takes into account all of these points discussed and more.
The goal of this model is to make it easy for you to forecast your SaaS growth and, specifically, plan the capacity of your sales team. With this capacity model, your future revenue growth will be expressed as a function how much you invest in your sales team, rather than simply guesstimated. You will have a robust Cohort Analysis model at your disposal, a CAC attribution model, Growth Cost model and the overall model will be easily integrateable into your 3 statement model. Most importantly, you will be able to demonstrate to future investors which metrics and assumptions can help you achieve specific revenue targets and which metrics and assumptions you need, to secure a strong next fundraising round.
By addressing these common mistakes and utilizing the SaaS Growth Capacity Model, you will have a comprehensive tool to drive your SaaS business forward and make informed decisions regarding growth and investment. It will also just be the best model around ;).
