What You Need To Know About SaaS Sales Forecasting

If you had the chance to look into the future and see how much you’re going to earn for the next year, would you take it? Of course you would. Having that foresight would help you reach that future revenue. Or avoid some complication you see down the road..

The good news is that it’s actually possible to do this for a business. It’s called sales forecasting.

Sales forecasting is one of the most important things for any business that wants to grow. It is crucial when you’re planning and structuring what your business will look like a few years from now. You could say it is the first step towards scalable growth.

SaaS revenue forecasting works by considering your current and possible future subscriptions. For most types of businesses, revenue is dependent on the deals they close for a period of time. But SaaS revenue is more consistent, especially if clients subscribe to long-term plans.

Why Sales Forecasting is Important

Sales forecasting is the key to making data-driven decisions and company growth. It enables you to predict your revenue and determine the factors that will bring in that revenue. It also helps you focus on your strengths and work on the areas that need improvement.

Moreover, sales forecasting exposes weaknesses in your sales pipeline. This gives you the chance to work on those weaknesses and maximize your strengths. Making your decisions around that data will lead to your company’s future success.

Things To Consider In SaaS Sales Forecasting

How does a SaaS company forecast revenue? As mentioned above, most of SaaS earnings come from subscriptions. So when you’re trying to do your sales forecasting, you’ll need to analyze your monthly recurring revenue or MRR. You can also use your annual recurring revenue (ARR) if you are forecasting sales for a whole year. Measuring both will depend on the scale of your sales forecast.

Now, to know how to calculate MRR or ARR, you need to consider a few factors that affect them. Among these factors are subscription renewals, new sales, and churn.

• Subscription renewals: to track this data, use metrics like Net Promoter Score (NPS) and Customer Satisfaction (CSAT) score. These will help you get an idea of how likely they are to renew.
• New sales: This factor has one of the biggest chunks in B2B SaaS sales forecasting. The first thing you need to consider is your bookings. “Bookings” refers to both recurring and non-recurring revenue that you can earn. For example, a new user purchases an annual plan that costs $100 per month. Then that booking is worth $1,200. This new revenue is called the new MRR.

Keep in mind that new sales don’t come only from your new MRR. You also get them from plan upgrades, add-on purchases, and reactivations. All of these contribute to the expansion of your monthly recurring revenue. You can predict the expansion MRR by monitoring your customers’ growth rate.

• Churn: This isn’t really your favorite word, is it? “Churn” is one of the most dreaded words in any SaaS business. It is the revenue you lose due to subscription cancellations or downgrades. While the thought of it is inconvenient, churn is a reality in SaaS. And it’s a crucial factor if you want to generate an accurate sales forecast. Still, forecasting gives you the chance to calculate and lower your churn.

Forecasting New Sales In SaaS

Predicting renewals and churn is relatively easy. You’ll just need to monitor and analyze customer satisfaction. But with new sales, it’s a different story. There are more elements and considerations at play. When it comes to new sales forecasting, here are some things to take into account:

• Historical data: To start projecting your future sales, you need to look at your past sales data. Try examining your MRR, ARR, and conversion rates.
• Changes in your company: Take into account any change that could affect sales. It could be new strategies for marketing and sales activity. It could be an expansion on sales and marketing teams. Or it could be new products, changes in pricing, or the adoption of software that has increased sales. If possible, quantify how much these changes affect your MRR or ARR.
• Sales analytics: Your past sales performance is an indicator of how much revenue you may generate in the future. You need to consider your conversion rates and sales velocity. Sales analytics is also a good way for you to identify strong and weak points in your sales pipeline. You can use that information to improve your sales strategy.

External Factors That Affect Sales Forecasts

Performance and resources are internal elements that you can control to some degree. But other factors that affect sales are external and are beyond your authority. Here are a few of these factors:

• Economy: Crises, recessions, and inflation are a few of the economic conditions that can affect your sales. A negative economic outlook may influence customers’ budgets for their SaaS needs. They may cancel their subscriptions or downgrade to lower-priced plans. In the same way, a positive economic outlook may also be a driving force that would bring in new sales.
• Market saturation: Competition is also one of the factors you need to consider in sales forecasting. Changes in your competitors’ products, pricing, and marketing strategy can affect customer behavior. This, in turn, can drive your sales up or down, depending on the situation.
• Regulations: This can affect your business, especially if you also have operations overseas. In the SaaS business, regulations are usually on data protection and auditing like the EU’s General Data Protection Regulation.

SaaS Sales Forecasting Models

There are several approaches you can take in sales forecasting. And it’s important to know which forecasting method works best for your business. After all, it can determine the accuracy of your sales projections. Here are some of these methods that you can use:

Historical Forecasting

This is the fastest but one of the most inaccurate ways to project your future sales. It assumes a standard annual growth rate and adds it to your past MRR. Yes, it’s based on assumption and does not take external factors into account. But having experienced this pandemic, you probably know how external factors can impact your sales.

What’s more, historical forecasting doesn’t consider some internal factors that could skew your numbers. Things like changes in sales strategies, lead quality, and market movement do not affect historical forecasts. This is why it tends to be inaccurate.

Length of Sales Cycle Forecasting

This method predicts how long it will take for each of your opportunities to become deals. As a result, you can forecast how many sales you can make in the next month, or year, or whatever time frame you want.

Using historical data, this forecasting method establishes sales cycle length for various lead sources. These sources could be social media, Google AdWords, cold emails, or whatever channel you are using for prospecting.

This is a good forecasting model to use if you haven’t made any significant changes in your sales processes. But if you had, your historical data may not be a very good reference. Additionally, it might not provide accurate projections if your sales performance exhibits seasonality.

Opportunity Stage Forecasting

Similar to sales cycle forecasting, this method also looks at each of your leads. But rather than predicting the amount of time it would take to close the deal, it measures the probability of actually closing it.

This approach works by assigning different probabilities for each lead. For example, you may say that a lead that is halfway through the pipeline has a 50% chance of turning into a deal. But it’s not as simple as viewing your pipeline as a progress bar.

Each pipeline stage has a different difficulty and contribution to your sales. You need to identify which stages are easy for you and which are not.

That’s where historical data comes in. You need to look at your past performance to identify strong and weak points in your sales pipeline. For instance, your records show that you lose leads after the demo stage but keep most of the remaining ones all throughout the rest of the pipeline. Leads who are past that stage would have high probabilities of closing even though they are still in the early stages in the pipeline.

Opportunity stage forecasting is dependent on historical data. As such, you need to make sure that your records are accurate from the outset.

This method’s advantage is that it considers the possibility of losing leads. It is still an inevitable reality in the sales process. Considering this possibility is important if you want to forecast your sales accurately.
The downside is that it doesn’t say when you would close your deals. Because it does not consider the length of the sales cycle, it may even overlook leads that are stuck in their pipeline stages.

Lead-driven Forecasting

This one is like a mix between sales cycle and opportunity stage forecasting. With this method, your sales team assigns a value for each lead. This value would depend on its probability of closing and other data points.

Based on historical data, it predicts each lead’s probability of closing AND the time it would take. On top of that, it also forecasts the value of the deal based on the average sales price for each lead source.

This method’s strength is that it looks at many factors and can produce accurate results. But the downside is that it’s still reliant on historical data. It also doesn’t take external factors into account. What’s more, it would not be able to consider changes in your marketing and lead generation processes.

Intuitive Forecasting

Intuitive forecasting is another quick way to estimate sales. But it can be very subjective. It relies on your sales reps’ own assessment on how many deals they can close within a certain period.

This method’s disadvantages are pretty obvious. It’s basically guessing your future sales. Of course, we want our sales teams to be confident about their future performance. But overly optimistic forecasts tend to be inaccurate.

Still, intuitive forecasting has its uses. If you’re a start-up company without any historical data whatsoever, this may be your only choice. The same is true if you’re launching a new product or service. What’s more, unlike other processes mentioned earlier, this method can take external factors into account.

Multivariable Forecasting

This model combines various methods to come up with a holistic sales forecast. It considers a lot of factors, including rep performance and length of sales cycle. And that’s not all. It also looks at external factors like market share and seasonality.

The beauty of this method is that it does not rely on only one factor. For example, two of your sales reps have the same conversion rates, but one closes deals faster. Since this method uses the length of sales cycle, you can account for that too.

By using a lot of data, this method is able to produce more accurate sales forecasts. But it also comes with a cost. Its reliance on many data points makes it hard to perform on your own. Even just gathering all the data you need for it can take a lot of elbow grease. Most likely, you would need AI or analytics software to perform this kind of sales forecast.

Furthermore, you need to make sure that your historical data is accurate. Incorrect inputs and typos in the spreadsheets could easily jeopardize your evaluation.

Final Thoughts

Sales forecasting paves the way for you to make smart decisions for future growth. Not only does it help you plan for your budget allocation but it also helps you improve your sales and marketing strategies.

Getting sales analytics software can be a good investment, especially if you need to process a lot of data. Some customer relationship management (CRM) software also have built-in forecasting features.

One of the things you can do to drive your sales up is to improve your content marketing. Looking to revamp your campaigns? Check out our complete guide to developing a SaaS content marketing strategy.

And for more info and update on the SaaS industry, you can check our marketing blog here.