Fernway DiarySM

Oct 31, 2022

Sales forecasting is the process of predicting future revenue over a specific time frame, taking into account internal data and external factors such as the overall economic environment. Accurate sales forecasts aid startups in setting revenue and sales goals, as well as informing decision-making, budgeting, and risk management. The unique features of the SaaS business model, in which customers typically have a high degree of flexibility to change or cancel a subscription with little notice, can make forecasting more challenging. Understanding the factors involved will help improve the quality of both short- and long-term forecasting.

Metrics to Consider

For a SaaS startup, bookings and monthly recurring revenue (MRR) are two important metrics that influence sales forecasting. Bookings refer to the value of a contract signed with a customer for a given period of time, and are considered a primary indicator of future revenue growth. Monthly recurring revenue is the predictable revenue earned from all active subscriptions normalized into a monthly amount. (Note that MRR is not the same as recognized revenue; per GAAP rules, revenue is only recognized after successful service delivery.)

In addition to top-line MRR, it becomes increasingly important to track factors that cause change in MRR as a SaaS startup grows. These include:

• New MRR—additional MRR earned from new subscriptions in that month
• Expansion MRR—additional revenue earned from existing customers who have upgraded to a higher-priced plan, purchased supplemental products, or reactivated canceled subscriptions
• Contraction MRR (or Churn MRR)—revenue lost through downgrades or cancelation of subscriptions

Churn rate must be considered in SaaS sales forecasting, due to the limiting effect it can have on a startup’s growth. While a certain amount of churn is to be expected for any SaaS startup, a high or increasing churn rate can be an indicator that pricing, product, or execution are not meeting customer expectations.

Data-Driven Forecasting

The foundation of an accurate sales forecast is high-quality data. Historical data, while not a guarantee of future outcomes, will indicate a startup’s current trajectory and provide information on what to expect. Real-time data should be aggregated into a centralized system to improve forecast accuracy; automating this process saves time and helps eliminate errors from manual information gathering.

In addition, different types of data analytics can be helpful in refining sales forecasts. These can include subscription analytics to assess churn, customer trials, and the lifetime value (LTV) of active users as well as marketing analytics to examine the effect of marketing activity on increasing subscriptions. Reviewing previous forecasts—for instance, comparing the percentage of deals forecast to close in a quarter to actual sales figures for that period—can also help to improve the accuracy of forecast models moving forward.

Different Sales Forecasting Methods

The type of sales forecasting method a startup chooses should be determined by their business model and growth rate; there is no single perfect method. Options include:

• Lead-driven forecasting—Analyzes each lead source and forecasts the likelihood of conversion based on what similar leads have done in the past.
• Length of sales cycle forecasting—Uses historical data to predict how long a deal will take to close based on where a lead is in the sales process.
• Opportunity stage forecasting—Predicts which opportunities are likely to become deals based on where every lead is in the sales process.
• Historical forecasting—Uses historical data to predict future revenue, assuming a standard rate of growth.

In cases where a SaaS startup is growing rapidly, past sales data may not be enough to create a useful forecast. Other information, such as conversion rates, deal size, and other sales metrics should also be considered.

External Factors

While much of the information essential for a sales forecast is internal, certain external factors should be taken into account to improve accuracy. For example, current fears of inflation and a weakening economy may extend the sales cycle. Revenue may also be affected by the behavior of competitors, should they reduce prices, introduce new products, or ramp up marketing efforts to increase their market share. Finally, regulatory factors, such as the introduction of new trade policies or laws affecting a startup’s operations in the US or elsewhere could impact sales.

SaaS sales forecasting allows founders to measure a startup’s performance against their long-term goals, enabling timely course correction if necessary to keep growth on track. Expert advisors can help a startup implement effective forecasting and gain essential insight for future planning. Fernway Solutions offers a full range of advisory, corporate structuring, tax planning, compliance, and accounting services to ensure your startup has what it needs at each stage of its growth.

For more information, please contact your US tax advisory team at youradvisor@fernwaysolutions.com or visit us at www.fernwaysolutions.com.

Disclaimer:

The above content is intended to support the marketing of professional services and should not be construed as written tax advice directed at the particular facts and circumstances of any person. If you are interested in the topics presented herein, we encourage you to contact us or an independent tax professional to discuss their potential application to your particular tax situation. To the extent this content may be considered to contain written tax advice, any written advice contained in, forwarded with, or attached to this content is not intended to be used, and cannot be used, by any person for the purpose of avoiding penalties that may be imposed under the Internal Revenue Code. Changes in tax laws or other factors could affect, on a prospective or retroactive basis, the information contained herein; Fernway Solutions assumes no obligation to inform the reader of any such changes.