Everything you Need to Know About SaaS Sales Forecasting Methods

In 1962, Malcolm X, an influential human rights activist said in his speech that the future belongs to the ones who prepare for it today.  Several decades later, his words still ring true and its context has come to stretch across wider areas. For any form of decision-maker wanting a glimpse of the future to adjust their actions today, forecasting has always been the name of the game.

Christoph Janz in his article on ‘The 5 ways to build a $100 million business’ split SaaS customers into 5 different categories namely flies, mice, rabbits, deer, and elephants based on their revenue-generating capabilities. Even the slightest mismatch between your target segment and sales process can lead to a lost sale. Sales forecasting can help in seeking the right prospects, and gauge revenue impacting factors in different scenarios helping the sales managers, sales leaders, and their sales teams take the right step forward.

What is Sales Forecasting?

Why is a Sales Forecasting Model important?

A sales forecasting model is crucial for businesses as it provides a comprehensive guide on how to predict future revenue based on historical data and market trends. By using quantitative analysis techniques, businesses can accurately predict future sales performance, allowing them to create more accurate forecasts that lead to better decision-making.

Having a solid sales forecasting model in place helps businesses understand customer demand, calculate average revenue per product, analyze performance over time, and identify growth opportunities. It also allows companies to track their progress closely and consider factors that may impact sales in the future.

By utilizing advanced methods and software tools for forecasting, businesses can make informed predictions about their future sales performance. This not only helps in planning for the future but also enables companies to capitalize on new opportunities and stay ahead of the competition.

In summary, an accurate sales forecasting model is vital for businesses aiming to refine their strategies and enhance revenue potential. By integrating these forecasting methods into their planning process, companies can make better-informed decisions that will lead to sustained success over time.

“The forecasting process is so much more than just calling a number. It represents the entire operating rhythm of the whole company,” Kevin Knieriem, CRO at Clari

Kevin Knieriem, Chief Revenue Officer at Clari – a revenue operations platform – says, “The forecasting process is so much more than just calling a number. It represents the entire operating rhythm of the whole company,” How accurate you are with your forecasts determines the leg-room your business has. It tells you how the business is likely to react to the changing dynamics of the market. 

Types of sales forecasting methods and their applications

When it comes to sales forecasting, there is a plethora of Excel templates available for new businesses. However, as your company grows, finding the right sales forecasting method becomes crucial. The choice of technique you make will directly impact the accuracy of your future revenue predictions. To ensure a steady cash flow and achieve your sales goals, it’s essential to explore various widely-used sales forecasting methods in detail. These techniques not only guide you on how to predict future revenue but also help in creating comprehensive strategies for your business growth.

Lead-driven forecasting

Length of sales cycle forecasting

Opportunity stage forecasting

Historical forecasting

Multivariable analysis forecasting

Lead-driven forecasting

‘Relationship’ is the pulley that keeps the wheels of sales running. Lead-driven sales forecasting helps in understanding the relationship a lead has with your company and the actions they are likely to take based on that relationship.

Using historical sales data from each of the lead sources (like paid advertising or referral), this method helps you create a forecast based on the action taken by a similar lead. When a value is assigned to every action, you’d be able to assess approximate revenue that each lead can potentially bring.

For this, you would need data on,

• Leads per month for the previous time period
• Lead to customer conversion rate by lead source
• Average sale price by source

While lead-driven forecasting is a great starting point there are few factors that would affect your results.

• Naturally, the average sales cycle would vary depending on the lead source. For this reason, an extra layer of analysis on the sales velocity (how quickly a lead moves through the pipeline) needs to be taken into account.
• Other business initiatives like price changes or discounts would affect the sales process. Michale Pici from the Sales Hacker, one of the largest B2B sales communities, suggests looking at a moving average of lead value for each source on a trailing 90 day period to tackle this.
• Any change in marketing efforts can also have a direct impact on sales. This is one of the main reasons why sales and marketing are required to be in sync at all times.
• Macro changes in the environment can also have an impact on the forecast. We do not have control over this but when the environment is volatile it’s always better to re-forecast every six months.

The verdict:

Lead-driven forecasting is a great starting point if you have a steady stream of inbound leads and have a lot of historical lead acquisition data. However, this method may not be best suited for new businesses.

Length of sales cycle forecasting 

This model uses data to predict how long the deal will take to close based on where the lead is in the sales process. It also helps to forecast an individual rep’s sales per month or quarter. This method aims to eliminate the ‘gut-feeling’ of the sales reps and inject objectivity into the process.

The sales cycle forecasting can be tweaked according to the source of the lead. For example, based on past data, Marcus, a sales rep at Prodigy, a software company, figured out that a normal inbound lead might take 6 months to convert whereas a referral might take as less as one month. You can group these lead types to arrive at an accurate picture.

This method needs a continuous tracking of the ‘hows’ and ‘whens’ of a prospect entering the sales pipeline. Hence there should be sync in data between sales and marketing teams. To avoid manual tasks bogging the workflow, you would need a strong CRM system that is integrated with other functions in your organization. At Chargebee we understand that salespeople start and end their day in front of a sales automation platform. We have integrated with Salesforce to let your team manage their entire sales cycle from quotes to orders to billing and renewals without leaving the comfort of the world’s most popular CRM.

The verdict:

If your business has a tightly knit marketing and sales team, and the sales reps are diligent in tracking how every lead enters the pipeline, then this method is a great option.

Opportunity stage forecasting

The opportunity stage forecasting predicts which opportunities are likely to become deals based on the various stages of the sales process that every lead is in. The further along the sales pipeline a lead is, the more likely they are to become a successful deal. For example, a new prospect could be 10% closer to signing the deal (this is the close rate) whereas a prospect who has gone through the user demo would be 90% closer to turning into a deal.

With this method, you can also predict the expected value of the deal. Once you calculate the potential close rate, you multiply the prospect’s potential value with their position in the pipeline. For example, if you assign the likely-to-close percentage as 10% to a lead who has finished the initial call and if the deal is worth $1000, the forecasted amount for the deal would be $100.

The role of the leads in decision making or their previous experience with the organization can also affect the fate of an opportunity.  For this reason, the sales and marketing teams work together and set up a lead scoring system through which the reps can identify and prioritize important opportunities. 

The limitation of this method is that it doesn’t take into account how long a lead has been at a particular stage. So, a lead who is at the product demo stage for the past week or the past month is assigned the same likely-to-close percentage. So, it is vital for the sales reps to refresh the pipeline regularly. 

The verdict:

It is more useful for businesses that have a lot of historical data about their lead’s activities. Since it doesn’t consider the time that leads spend at a particular stage, it might not give an accurate picture and just an aerial view of the sales activities. 

Historical forecasting

It is quick, it is dirty and it can be done by anyone who has a record of the past sales data. As the name suggests, it takes historical sales data and adds an assumption that your business will grow at a standard rate year-on-year to estimate your future sales. For example, if your MRR was $7000 last year then this method assumes that you’d make an MRR of $7000 the next year as well. Then adds an average year-on-year growth rate of 5% to forecast an MRR of $7350 for the next year.

However, after facing a pandemic and the uncertainties that come with it, we know that any forecasting that relies on historical data could lead to inaccuracies. It is important to take into account trends and seasonalities. That is why we recommend you use this data to set your sales benchmarks and not take it as the holy grail of forecasting.

Recently after the pandemic hit, Germain Brion, VP of Global Sales at Chargebee spoke with Troy Conquer, CRO at GoNimbley and Dailius Wilson, VP of Sales and Growth at GetAccept on the changing state of sales in a post-COVID world. You can take a look at the conversation here.

The verdict:

Looking at historical sales data alone might not be the best idea to decide the future but it would be ideal to set benchmarks.

Multivariable analysis forecasting

The multivariable analysis forecasting uses predictive analysis and combines some of the best features of other forecasting methods such as average sales cycle length, probability of closing based on opportunity type, and individual rep performance. This method is heavily reliant on multiple sources of data and provides an accurate sales forecast. However, due to this heavy dependency on data, the multivariable analysis needs an advanced analytics solution which could be very expensive.

To give you an example of how this works, let’s take a look at two sales reps, Steve and Martha are working hard to get to respectively close their deals.

Jonas

Jonas has a deal worth $10000 to whom he has just given a product demo. Based on Steve’s personal win rate, the size of the deal, and the days left in the quarter, he’s 40% likely to close the deal in this quarter. The multivariable forecast provides a predicted revenue of $4000 (40% of $10000) from Steve on this deal.

Martha

Martha on the other hand has a deal that is smaller in size (worth $2000) compared to Jonas’ and is having her first call with the prospective customer. Even though it is way before in the sales cycle, considering her win rate is impressive she is also predicting the same 40% chance of closing the deal in this quarter. Here the multivariable analysis predicts revenue of $800 (40% of $2000) from this deal.

Hence the total sales forecast for the quarter would be $4800.

With two sales reps it was easier to predict the numbers, however in real life it is a bit more complex. Since this analysis relies on data for accuracy, unless the sales reps are diligent in tracking and recording their progress with deals, the analysis could lead to inaccurate sales. Due to its complexity, it is more suitable for larger businesses with sophisticated systems for tracking and storing data than early-stage companies or even new businesses.

The verdict:

The multivariable analysis generates accurate forecasts but would involve an advanced analytics setup that may not fit into the sales budget of a small business. An additional requirement is that the sales reps need to be very good at recording and tracking the deal’s progress.

Which Sales Forecasting method is accurate for you?

The method you choose to incorporate would depend on various factors such as the market share of the company, the stage your business is at, your business model, the size of your sales team, the quality of data in their hands, and the diligence of sales reps in recording data. Taking into consideration the macro factors can also help you zero in on the right sales forecasting tool. However, it is not necessary that you should have only one sales forecasting model. It is always better to have complementary models to help with data accuracy.

Data plays a huge role in forecasting activities. At Chargebee, we understand that each of your decisions needs to be insight-driven and backed by data. For this reason, our Revenuestory feature gives you a 360-degree view of your business to help you stay on top of your metrics. Not only this, but it gives you real-time notifications when business metrics violate their expected threshold and drives alignment within the organization.

Get in touch with us now if you’d like to know more about how Chargebee can help you accurately forecast sales.