Forecasting can be done using historical sales data, economic trends, and more. Sales leaders and decision-makers often struggle to forecast sales accurately. Inaccuracies often lead to botched investments and missed opportunities.
Accurate sales forecasts are important because they help in decision-making, re-adjusting priorities, budgeting, and risk management. It also helps in setting revenue and sales goals for the next year.
Before you begin with the forecasting process, let's look at some basics of SaaS revenue and other top-line metrics that influence sales forecasting.
Bookings in SaaS
Bookings indicate the value of a contract signed with a prospective customer for a given period. Bookings estimate the revenue that is won by sales, including non-recurring bookings. And that's why bookings are a primary indicator of future revenue growth.
Monthly Recurring Revenue (MRR)
MRR is the predictable monthly revenue that is earned from active subscriptions. MRR is one of the most crucial top-line metrics and is an essential consideration in SaaS sales forecasting. An important point to keep in mind is that MRR is not the same as 'recognized revenue'. Only after successful service delivery, you can 'recognize' the revenue for that month, as per GAAP rules.
There are various components to the MRR that affect sales forecast:
It is the additional MRR earned from new subscriptions acquired in that month. Essentially, this MRR comes from new sales.
While forecasting new sales, various factors must be taken into accounts, such as your available pipeline, the strength of your sales team & resources at your disposal, and the competitiveness of the market you are in. A quick and dirty way to predict new MRR in a particular time period is to check the past sales data for a matching timeframe.
Expansion MRR is nothing but the additional revenue earned every month from your existing customer base. It includes Upsells (moving to a higher-priced plan), Cross-sells (purchase of other supplemental products), add-ons, and reactivations of canceled subscriptions.
While these are not new sales, they add significant value to the SaaS revenue stream. In this case, forecasting sales can be done using various factors such as the past performance of the sales reps and the customer's business viability. Forecasting these can be tricky, but constantly tracking the historical data from the MRR cohorts can give insights into which customers can be prospective sources of expansion MRR.
Contraction MRR or Churn MRR consists of MRR lost due to downgrades cancellations of subscription. This has a negative impact on the SaaS revenue. To avoid overestimating revenue while forecasting sales, churn needs to be kept in mind as well.
Now that you know the 'what' and 'why' of SaaS Sales Forecasting, let's go to the 'how'. The following checklist involves the steps involved in SaaS Sales Forecasting.
Centralize your Data
Your sales forecasts' accuracy depends mostly on the quality of the sales data you are working on. The most time-consuming part of any predictions is aggregating the rollup of estimates from all your sales reps. Moving between email tools and spreadsheets where your data resides can be cumbersome and prone to errors. Make sure you have a well-documented process to enter all forecast information into a single shared system. Having a centralized system not only saves you a great deal of time but also improves your forecast accuracy.
Analyze your Pipeline
It would help if you looked at how the opportunities you originally forecast to close at the beginning of a quarter evolved. Then, analyze the percentage of deals won in the quarter, the ones that got rolled into the next quarter, and the lost deals. The percentage of deals won will give your marketing and sales team a good idea of the pipeline coverage they will need to hit the next quarter's target for bookings. Focusing on improving the percentage of deals won in a quarter will enhance the business's overall efficiency.
Define your Sales Cycle
SaaS sale cycles are different because they involve a different set of touchpoints. There's self-serve SaaS, and then there's sales-driven SaaS. Depending on the product, define the funnel stages and document the steps in your sales cycle clearly. Factors like the average sales cycle and conversion rates are critical considerations for accurate forecasting.
Automate the Process
Invest in CRM. With all the information about your leads and customers in one place, everyone in the organization can get valuable insights about your sales pipeline's health. CRM solutions like Salesforce, Freshsales, or Hubspot streamline the sales process, give real-time information about opportunities, and help you identify top lead sources. Once you define your sales cycle and workflows, look to automate the process. Automating the process enables you to forecast further into the future, focus more on customers and respond better to changes in the competitive landscape.
The last and probably the most critical step is to choose a sales forecasting method depending on the stage of growth your SaaS business is in.
There are multiple sales forecasting techniques out there. Not all of them might fit into your business model and growth rate. For new businesses in the early stages, excel templates should suffice. But for rapidly growing businesses, looking at just past sales data is not enough. You need to consider other parameters, such as conversion rates, deal size, and other essential sales metrics. This involves deep analysis, market research, and demands a good forecasting tool.
Choosing the right forecasting model determines the accuracy of your cash flows. Some of the sales forecasting models are:
Length of sales cycle forecasting
Opportunity stage forecasting
Multivariable analysis forecasting
We have done a detailed analysis weighing the pros and cons of these sales forecasting methods here.
Various factors influence sales forecasting. Internal factors to be considered by sales managers and business leaders include the availability of resources, new product launches or change in pricing strategy. External factors such as market trends, competitive pressures and seasonality play a key role when forecasting sales for your SaaS as well.
Let's explore a few more factors you need to consider to get accurate forecasts:
Long-term economic conditions can have a significant impact on your company’s growth rate. When the economic outlook remains weak, the sales cycle takes longer than usual due to protracted decision-making processes. Factors like inflation can also influence consumer behavior. It can affect the purchasing power and consumer’s risk appetite. So, it needs to be factored into the forecast.
Increasing competitive pressures in a market can affect consumer behavior as well. Your market share can change based on your competition. In a bid to gain a larger chunk of the market, competitors can reduce prices, introduce new products, or invest more in sales and marketing efforts, all of which can affect your revenue inflow.
Regulatory factors involve the introduction of new trade policies or laws that could affect your operations globally. Taking these changes into consideration can help in accurate forecasting.