Achieving predictable revenue growth with more accurate forecasting in sales

It wouldn’t be a stretch to say that accurate sales forecasting is the bedrock of a company’s success.

Here’s why…

With accurate forecasts, you will get ‘true’ visibility into how much new business your company can close in a certain time period. With this number in place, you can do better budgeting and planning. 

Specifically, accurate forecasts can help create realistic budgets aligned with projected recurring revenue streams and customer acquisitions. This also translates into more informed decisions on resource allocations across the board, ultimately helping sales teams meet their targets. 

Sales forecasting also facilitates strategic planning by setting achievable targets for new logo acquisitions and expansions within existing accounts. 

What is sales forecasting?

Sales forecasting is the process of gauging future revenue by accurately predicting the demand for a company's products or services over a specific period and the number of deals it can expect to close during that period. 

Sales forecasting plays a crucial role in business strategy and decision-making in several ways:

• Revenue projection: Sales forecasts give insights into expected revenue streams, enabling better planning and resource allocation. Companies can thus manage cash flows and set realistic financial goals.

• Demand planning: Accurate sales forecasts help companies better anticipate market demand for their offerings. In SaaS, sales forecasting also helps companies, as a downstream activity, do better product feature prioritization mapping based on customer requests and feedback. 

• Strategic planning: Sales forecasting is critical in long-term business strategies, including product development, market expansion, staffing requirements, and capital investments aligned with projected growth.

• Budgeting and resource allocation: Forecasted sales figures are the foundation for creating realistic budgets and resource allocation across various departments. 

• Risk management: Sales forecasting can help businesses identify and mitigate risks related to market volatility, economic conditions, or competitive landscapes by anticipating potential fluctuations in demand. 

What is the difference between sales and revenue forecasting?

In traditional ‘one-time’ sales models, sales and revenue forecasting are more tightly aligned, as a sale typically translates directly into recognized revenue. Say, in manufacturing, a unit sold is revenue earned. 

However, in a SaaS context, it is important to distinguish sales forecasting, which is focused on new business and expansions, from revenue forecasting, which is focused on recognized recurring revenue. 

Let’s dig a bit deeper:

Sales Forecasting in SaaS

• In a SaaS context, sales forecasting predicts the number of new customer acquisitions (new logos) and expansions/upsells within the existing customer base over a given period. 

A simple thing to remember here is that a ‘sale’ in SaaS ideally results in recurring revenue for many quarters/years. If things go well, the ‘sale’ could also be upgraded to a new pricing tier, generating more revenue. Thus, SaaS sales forecasting involves forecasting the price and timing of a new sale, renewal, or potential upsell.

Revenue Forecasting in SaaS

• Revenue forecasting is projecting the actual recurring revenue streams that will be recognized on the income statement.
• It considers sales forecasts and factors like churn rates, contract terms, and billing cycles. 
• Revenue forecasting is tied to financial planning, budgeting, and providing visibility into the company's top-line performance.

The bottom line is that accurate sales forecasting feeds into revenue forecasting, but revenue forecasting additionally considers the many nuances of recurring revenue recognition. 

Who is responsible for producing the sales forecast?

The CRO, RevOps head/team and the CEO play crucial roles in sales forecasting. 

Chief Revenue Officer (CRO) / Head of Sales

• Plays a central role in driving the sales forecasting process. S/he coordinates with ground-level sales folks to get each of their potential sales numbers. 
• Provides insights and inputs based on the sales team's activities and pipeline.
• Responsible for setting up the sales process and forecasting structure along with RevOps.

Revenue Operations (RevOps) Head/team

• Drives the process around forecasting by setting up the entire structure around the sales process.
• Provides data-driven analysis and a "gut check" on the forecast. By gut check, we mean the RevOps team looks at data to determine how accurate the forecasts are. One can think of them as a soundboard that gives the sales team a reality check. 
• In most companies, RevOps is also responsible for consolidating inputs and producing the actual forecast. Sometimes, they work closely with the CFO and finance team on this. 

Chief Executive Officer (CEO)

• The CEO is the one with the ‘question’. The need for a sales forecast starts with the CEO as s/he relies on accurate forecasts to make strategic decisions and set targets.

The CFO and FP&A team are involved in the later stages of integrating the sales forecast into the overall financial forecasting and budgeting process, while the primary sales forecasting activities are largely driven by the CRO, RevOps, and sales teams.

Common sales forecasting methods

Now that we know what sales forecasting is, let’s look at some of the common sales forecasting methods. 

Bottom-up forecasting by sales rep

Bottom-up forecasting is the most common approach used in sales forecasting. As the name suggests, the forecasting is built from the ground up, starting with individual sales representatives' inputs and projections.

The bottom-up forecasting process is pretty straightforward: 

1. Each sales rep analyzes their current sales pipeline, which includes prospective customers at various stages (lead, qualification, proposal, negotiation).
2. For each opportunity in the pipeline, sales reps evaluate the likelihood of closure  based on factors such as product-customer problem fit, customer interest, competition, and the current stage of the sales process.
3. Sales reps then estimate each opportunity's potential revenue or contract value and assign a probability percentage based on their assessment of the deal's likelihood of closure. 
4. Each sales rep calculates their individual forecast by summing the weighted revenue values (deal value multiplied by the probability of closing) for all opportunities in their pipeline.
5. These individual forecasts from each sales rep are then consolidated by the sales head to generate the overall organizational sales forecast for the company.

The sales leader works closely with individual sales reps to help them bring more accuracy to their individual forecasts. It is not a good practice for sales reps to ‘underpromise and overdeliver’ or vice versa. 

It can sometimes be hard for sales reps to accurately predict their pipeline potential. Some may be overly optimistic while others may not be able to judge its full potential. This is where the guidance of a sales leader becomes critical for ensuring accuracy in their forecasts.  

The bottom-up forecasting process is also often supplemented with historical data analysis and input from stakeholders like RevOps to improve accuracy. In some cases, even market analysis (competitor research/industry benchmark) helps bring much-needed accuracy. Additionally, revenue forecasting software helps organizations track and organize sales forecasts. 

Once you get your bottom-up forecast, you’d want to triangulate with a few other approaches to get a more accurate forecast. Let’s look at those now.  

Stage-weighted forecasting 

Also referred to as opportunity stage forecasting model, this approach assigns a probability weight to each stage of the sales pipeline. This basically means that deals in earlier stages are given lower probabilities, while those closer to closure are assigned higher probabilities.

The weighted deal values are then summed across all stages to arrive at the overall forecast. While statistical in nature, this method may not fully capture the sales team's insights and judgment about specific deals. It is however, useful to help calibrate results from the bottom-up forecasting model. 

Pipeline-based forecasting

In this method, historical data is used to calculate conversion rates between each stage of the pipeline. These rates are then applied to the current pipeline to project how many deals will progress and close.

With this method, you can also account for seasonality by using historical data from the same period to calculate conversion rates. This helps seasonal businesses to calibrate their forecasts. 

Pipeline-based forecasting also allows the RevOps team to incorporate qualitative inputs from sales reps, such as their confidence levels (worst case, base case, best case) for each deal in the pipeline.

Funnel forecasting

The funnel forecasting method starts with a sales target and works backwards to marketing to see how many leads it has to bring in for the sales team to meet its revenue target. 

This is an inverted way of looking at a sales forecast, in which you’re back-calculating the number of leads you need based on the number of deals you need to close to reach your target. 

It provides one more way to calibrate and because every sale begins with a lead, it can also help inform your marketing activities by identifying gaps in the pipeline or lead generation efforts. 

Which method is best for your business?

The truth is that there is no one-size-fits-all when it comes to choosing the sales forecasting method for your business. Before deciding on the best method, one must account for the company’s growth stage, industry, market dynamics, and the nature of the sales cycle. 

Most companies start with the bottom-up forecasting method. As they mature and have enough historical data, they tend to incorporate additional triangulation methods, such as stage-weighted, pipeline-based, funnel forecasting, or a mix of these methods, to refine their forecasts.

Here are a few tips to enable more accurate forecasts:

• For businesses with shorter sales cycles, pipeline-based or stage-weighted forecasting may be more suitable as they leverage historical data and trends more effectively when sales cycles are relatively short. 
• On the other hand, for companies with longer and more complex sales cycles, bottom-up forecasting might be better, as individual deal assessments and sales team insights will lead to more accurate forecasts. 
• Mature businesses that have a well-established and consistent sales process will find it easier to leverage stage-weighted or pipeline-based forecasting methods, as they can rely on better historical data that can inform their present cycle.

Challenges in sales forecasting

One of the biggest challenges in sales forecasting is that some people tend to be overly optimistic while others may be too conservative. Ensuring realistic assessments is pivotal for accurate forecasting. 

This said, there’s an important nuance to consider here – providing conservative estimates in the pursuit of accuracy is not inherently wrong if the judgment and reasoning behind it is sound.

For example, if a sales head can say, and back it up with data, that Salesperson A usually overestimates. Then, reducing that salesperson’s forecast by some percentage probably makes good sense, as it will help make the forecast more accurate.  

The best way to solve the problem of biased forecasting, is to maintain a forecasting accuracy journal. You can use this to track the sales teams’ forecasts and identify individuals who consistently underestimate or overestimate, allowing for necessary adjustments. 

Bear in mind that this isn’t a policing mechanism; it’s about helping the sales executives improve their forecasts. Doing so ultimately helps the company set realistic expectations, which benefits its salespeople. 

Another challenge in sales forecasting is the data quality. Forecasting methods like pipeline-based or stage-weighted approaches rely on high-quality and consistent data. Given this, inconsistencies in pipeline management, deal staging, or data capture can undermine the accuracy and hence make these methods less effective. 

Finally, given the rapid evolution of market conditions, economic shifts, and competitive landscapes (thanks to AI), historical data and trends might become less relevant depending on the market in which the product functions, thereby impacting the reliability of forecasts.

Best practices for effective sales forecasting

Regular review and adjustment of forecasts

A common question is, ‘How often should I update the forecast?’ In an ideal state, it should be weekly. However, this would just apply to the main method (say bottoms-up), and not the triangulation.  

The triangulation can be done on a quarterly basis. 

Make people accountable for their numbers

As mentioned above, it is prudent to create and track a metric called forecast accuracy. This will hold people accountable to their numbers and solve the problems of being overly optimistic or overly conservative.

Training and development for sales teams in forecasting techniques

Sales and RevOps leaders must develop material that clearly explains the organization's forecasting process, stages, and criteria for assigning probabilities. And every new sales rep should be required to read it. 

It’s also good to conduct a few training sessions led by experienced sales leaders to cover methodologies, deal assessment, probability estimation, and best practices. 

An added bonus would be to implement a mentorship program where new or less experienced sales reps are paired with seasoned team members to learn from the best, if you will. 

Keeping up with industry trends and incorporating external factors

Smart sales and RevOps leaders are always one step ahead of the market. They do this by regularly monitoring industry trends, market conditions, and external factors that may impact their forecasts. 

It is a good habit to incorporate relevant data points and insights you’ve gathered into the forecasting process to account for potential shifts and disruptions.

The role of technology in sales forecasting

Technology plays a huge role in sales forecasting. Customer relationship management (CRM) systems, forecasting software, revenue planning software, and data analytics tools can all help automate the forecasting process (to varying degrees). These tools help consolidate data from multiple sources and provide real-time visibility into pipeline metrics. 

Advanced technologies like machine learning and artificial intelligence can further enhance forecast accuracy by identifying patterns and trends in the sales data. 

The role of CRM and sales enablement tools in forecasting

CRM and sales enablement tools capture and consolidate important deal data, pipeline metrics, and sales activities, providing a comprehensive ‘hawk-eye’ view of the sales process. 

CRM tools can be integrated with forecasting models, enabling accurate projections based on real-time pipeline data. Sales enablement platforms streamline the forecasting workflow, ensuring consistent data entry and adherence to forecasting methodologies across the sales organization.

Predictive analytics and its application in forecasting

Predictive analytics tools leverage historical data to identify patterns, trends, and correlations that can influence deal outcomes. These tools can also help predict the likelihood of closing deals based on factors such as sales activities, customer engagement, and deal characteristics. 

RevOps software like Boostup and Clari help organizations establish and rigorously follow a defined forecasting methodology, thereby saving time and increasing efficiency. 

They also provide deal-level insights, enabling a "gut check" on which opportunities will likely close based on data-driven predictions. Additionally, these platforms offer integrations with CRM systems. 

Machine learning techniques

By gathering and analyzing historical data on sales activities, customer interactions, and deal characteristics for both won and lost opportunities, tools that incorporate machine learning models can easily identify patterns and predictive signals. 

These insights can then be applied to the current pipeline to uncover the probability estimates for each deal's likelihood of closing. 

Accurate sales forecasts can make or break a company

To succeed in today's competitive market, businesses must adopt a systematic approach to sales forecasting, embracing continuous improvement and leveraging the power of technology. 

Organizations can enhance forecast accuracy, optimize sales strategies, and drive revenue growth by combining robust forecasting methodologies, data-driven insights, and advanced analytics into a single platform like Drivetrain for full pipeline visibility in real time. 

With robust revenue forecasting capabilities, multiple forecasting templates, and more than 200 integrations, leveraging Drivetrain with other technologies makes fast, easy, and accurate revenue forecasting in reality. 

Check out Drivetrain to learn more!