Jun 22, 2022
Keane Angle
Louise Saludo

The Secret Formula to Creating Accurate Financial Projections According to 100 Startups.

Startups and investors continuously search for the “sweet spot” of financial projections. Too high and founders risk coming across as unrealistic.

This article is an ongoing collaborative series with ProjectionHub where we analyzed a combined, anonymized data set of hypothetical financial projections from 107 startups. Read our first article on financial projections per business model here.

Startups and investors continuously search for the “sweet spot” of financial projections. Too high and founders risk coming across as unrealistic. Too low and the opportunity might look too small. But how can startups know where to begin with their financial projections? How high is too high? Even as we searched for answers, we only found more questions.

At STORY, we use data to answer burning questions about startups and pitch decks for our clients. In this study, we teamed up with ProjectionHub to figure out what the key is to projecting realistic financial projections.


Here is the breakdown of our data by business model:

  • 39 business-to-business (B2B) tech startups
  • 30 business-to-consumer (B2C) tech startups
  • 17 direct-to-consumer (D2C) product / eCommerce startups
  • 16 marketplace startups (includes a few web3 / NFT startups)

And here’s the data by round:

  • 16 Pre-Seed startups
  • 68 Seed round startups
  • 18 Series-A startups

The time frame of financial projections varied across all startups. Five-year projections had a smaller sample size. So, insights from years one, two, and three are the most statistically reliable.

Moreover, the first-year revenue projection dictates the trajectory of financial growth. Getting that first-year estimate right is quite important. For more information, check out our other article on First-Year Financials.

NOTE: In case it wasn’t clear, the financials we analyzed were projections and not actual results and all data was anonymized to maintain confidentiality.

Now, onto the secret formula.

A startup’s revenue growth rate can be used as a guide for financial projections. We calculated the average growth rates for startups based on their round (Pre-Seed, Seed, Series-A) and their business model (B2B, B2C, D2C, and Marketplace).

Here’s what our data set looked like.

By Round

Based on our research, Series-A rounds projected the highest growth rate from years 1-2, while Pre-Seed startups predicted the smallest year 1-2 growth.

By the second and third years, Seed rounds had the highest projected growth rate at 2.7x  followed closely by Pre-seed at 2.6x.

Across the board, these startups expected growth rates to drop significantly by the third year, with Pre-seed predicting the highest value at 2x.

Series-A is the only round to have estimated an increase by their fourth year. However, this may be caused by our sample size.

By Business Model

Before we delve into the data, let’s first define each business model. B2B Tech startups cater to other businesses. Companies such as Hubstaff and Zoho qualify as B2B Tech.

On the other hand, B2C tech directly provides services to consumers. A few examples are Paypal, Uber, and more.

D2C or Direct-to-Consumer startups streamline retail distributions by eliminating middlemen. Examples include Glossier, Fisher-Price, etc.

Just as the name suggests, Marketplace startups are platforms where online transactions occur. Airbnb and Amazon are prime examples of marketplace startups.

B2C Tech startups projected the highest growth rate for years 1-2 at 7.7x.

B2B and B2C Tech companies expected an increase in their growth rate by years 4-5 despite a slight drop in the prior year. This is either a) an issue with our data or b) speaks to how companies believe that some economies of scale or network effects begin to be realized at year 5.

D2C Product startups rank the lowest at a 2.8x growth rate for years 1-2. However, this can be attributed to our low sample size. By years 3-4, the projected growth rate for D2C products slows down to 1.4x

“One reason D2C Product companies tend to grow slower is that they face real-world limitations with supply chains, logistics, and manufacturing; whereas, software companies can sell the same software to 100 customers or 100,000 customers with relatively less real-world constraints.”
— Adam Hoeksema Co-Founder of ProjectionHub.com

Marketplace is the only business model that projects its growth rate in a decreasing manner over time. Its years 1-2 project a staggering 5.3x growth rate that drops to 1.5x by years 4-5.

The projected growth rate across B2B Tech, B2C Tech, and D2C Product fluctuated over time. It drops by years 3-4 and increases as years 4-5 come around.

Defining “Unrealistic” Revenue

The revenue growth rates that we’ve previously discussed can help guide founders in creating more realistic year-on-year revenue growth rates. However, we have yet to discuss where to start in terms of revenue projections. Starting too high will cause year 5 projections to look overinflated. On the other hand, starting too low will make year 5 meager. So, this section is about finding a good starting point.

But what about revenue?

To answer this question, we analyzed the standard deviation from the average annual revenue of each startup by business model and by round. This allowed us to calculate the “upper bound” and “lower bound” which allowed us to answer the question of “how high is too high?” Projections around or above the upper bounds can be thought of as too high or objectively unrealistic.

Before we delve into the data, it’s important to note that Pre-Seed companies are likely in their first year of operations. Once Pre-Seed companies reach their second year, they could be deemed Seed startups. Moreover, by the third year, they could be Series-A, and so on.

Let’s review the upper bound by round.

Based on our data, Pre-seed companies that are projecting above $1.3M in their first year of operation could be considered ‘unrealistic.’

In the second year, Pre-Seed startups that project beyond $5.6M are considered to be unrealistic. By the third year, the upper bound is at $14.7M. This trajectory continues in Year 4 as Pre-Seed capped at $29.9M and a $44.2M upper bound by Year 5.

On the other hand, Seed round startups that reached $2M in Year 1 could be considered unrealistic. By Year 2, Seed startups aren’t expected to make $11.6M in annual revenue. By Year 3, the upper bound becomes more lenient with a $31.4M cap. Further growth is expected until Seed rounds reach a cap of $52.5M by Year 4 and $84.7M by Year 5.

Post revenue startups under Series-A could be more lenient when it comes to upper bounds. The data suggested that $3.1M could possibly be “unrealistic” for Series-A Year 1 revenue. Despite having initial revenue and good traction, we believe Series-A startups are still in the growth phase by this year. Year 2 shows a jump with an upper bound of $22.2M. This growth may be expected as Series-A startups will continue scaling operations post-funding. Revenue is expected to grow rapidly by Year 3 as the data dictates a $48.8M upper bound. This is closely followed by a Year 4 upper bound of $86.7M. Series-A is expected to have enormous growth by Year 5 with an upper bound of $240.8M.

For Series-A startups, growth can be easily and quickly obtained as they have experience in scaling the startup. Yet generally, the upper bounds show us that Year 3 is a transformational year in terms of revenue and the momentum is basically unstoppable until Year 5.

By Business Model

The startup’s business model obviously plays a part in its revenue projections. Some business models propose slow and steady growth. Others are fast in generating revenue vs others. The chart below shows the upper bound by business model.

Let’s dissect each.

The highest Year 1 upper bound can be seen at Marketplace startups which capped at $11.1M. Why? Its business model generates more revenue per transaction. Marketplace startups charge both vendors and buyers on their platforms. They also have API opportunities and if necessary, ads can also be integrated. Another factor to consider is the inclusion of web3 and NFT startups in this data set. This momentum continues until Year 5, as Marketplace startups have a cap of $436.1M.

Of note, the Marketplace categories are highly speculative. They have the highest overall upper bound in Year 1 which, understandably, affected their five-year trajectory.

B2B Tech follows closely with a Year 5 upper bound of $377.1M. Similar to Marketplace startups, B2B Tech generates more revenue than other business models as they typically charge per seat. This means, that one enterprise client can produce 50+ seats or more.

Meanwhile, the only time B2C tech overtakes B2B Tech is in Year 2 with a $47.5M upper bound. In comparison, B2B Tech has a cap of $40.6M. Despite this minimal difference, we think that this is due to the sales cycle of both business models. B2B Tech usually takes longer to close a sale vs B2C Tech.

D2C startups are understandably the lowest with a $3.1M Year 1 cap. Most business models have tremendously increased their upper bounds except D2C startups. Why is D2C lagging behind? Overhead is typically higher for D2C products. However, keep in mind that our data can be unreliable due to the low sample size for D2C startups.


A startup’s round and business model can help guide its financial projections into accuracy. Two things can help startups project reasonable financials: a reasonable starting point and a reasonable growth rate. These two variables rely on the startup’s round and business model.

"Your revenue projections should be based on data-driven assumptions whenever possible. You should research to understand typical customer acquisition costs, churn rates, and lifetime value of a customer in your industry to help ensure that your revenue projections stay within industry norms.”
— Adam Hoeksema, Co-Founder of ProjectionHub.com

The first-year revenue projection always affects the trajectory of revenue over time. Moreover, the growth rate shows how big the jump will be year by year.

Despite the lack of a universal formula, being guided by these data points will help startups arrive at reasonable projections that show a huge opportunity without seemingly being overinflated.

Need help with financial projections? Check out our partner, ProjectionHub.com.

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