Reasonable estimates (also called assumptions) are critical to building a financial forecast that is credible, attainable and useful. Ideally, selecting assumptions would be based on lots of historical data for every component of each forecast item (revenue drivers, expense items, etc.). However, this is rarely practical. Historical data may be limited / non-existent or the company or market is growing and changing rapidly. So how do we make reasonable assumptions?
Industry benchmarks (or norms) are a great way to select reasonable assumptions based on limited data. Using data from a group of similar companies who’ve already been down the path helps to remove guessing. Benchmarks help to anchor, and justify, your assumptions with data. We’ve compiled benchmarks from a broad group of SaaS companies at various revenue stages. Data is drawn from surveys, financial filings, reports and studies from leading sources. You can also learn about our methodology.
SumSavy's template is pre-populated with benchmarks to save time and build a better forecast. Make changes to suit your company then a forecast is created instantly. If you’ve already built your forecast, you can compare your numbers to the benchmarks to see if they fall in a reasonable range. Each benchmark has a midpoint (median) and a range covering the middle 50% of values for that benchmark. This approach helps to adjust for outliers and create a better representation of the “typical” SaaS company. We've provided definitions about each benchmark for more detail.
Our benchmarks are comprised of financial data from self-identified SaaS companies. Data in the benchmarks was collected between 2013 to 2016 (March) primarily companies that are currently private or public companies that were private during the revenue stage that their metrics were included in the benchmarks. Data is primarily derived from the Pacific Crest (sample size = 305 companies), Openview Partners (sample size = 160 companies) and RCCF (sample size = 60 companies) benchmark studies given their larger sample sizes, research methodology and data granularity. This data was supplemented from additional sources (full list of sources below). For each benchmark, values for each company were sorted from smallest to largest. This dataset was then divided into four equal parts (quartiles). The median represents the midpoint or 50th percentile. The range bottom and top represent the 25th and 75th percentiles respectively. The range covers the middle 50% of values. We used this approach to prevent skew from outliers and present a more representative picture of the metrics of a "typical" SaaS company. Where applicable, companies were segregated into groups based on their last year's annual recurring revenue (ARR). We call this the "revenue cohort." For public companies with published historical financials, their respective historical metrics were included in each corresponding revenue cohort.