Revenue forecasting: The sales operations leader’s playbook for data-driven success

Ask a group of SalesOps leaders for their ideal superpower and it's a guarantee someone says, “The ability to see into the future.”

And it's no surprise: revenue forecasting is a core component of the job.  

It’s on you to reasonably estimate how much your sales organization will bring in over future periods, but you don’t have a crystal ball. With a lack of organized, connected data and external conditions outside of your control, revenue forecasting can often be challenging—but, with so many downstream functions relying on your figures, it’s important to continually refine your methodology nonetheless.  

Whether your organization needs to pursue further investment, hire new employees, or expand into new areas—these decisions are all highly dependent on accurate revenue projections.  

Below we'll share how to approach forecasting revenue for sales organizations generally, with guidance on how to improve revenue projection accuracy and enlist help from key stakeholders.

The essential role of the sales operations team in revenue forecasting

Revenue forecasting is the analytical exercise of estimating future revenue based on historical sales data, current trends, and market conditions.

It’s a fine balance between art and science, and ultimately helps sales leaders predict how much revenue a given team or company is likely to generate within a certain period, enabling strategic decision-making.  

Sales forecasts have a critical domino effect on other aspects of managing an efficient sales organization. They impact:

• Sales strategies: Bleak revenue forecasts might highlight the need to expand into new territories, incentivize retention to maintain market share or combat churn, or prompt the implementation of new incentives (like proactive sales spiffs) to motivate pushing through despite a period of tough market conditions.  

• Resource allocation: An uptick in expected or untapped revenue opportunities may require more sales resources to meet demand, or redistributing reps across different territories that are expected to drive substantial revenue over the coming months.

• Quota setting: An accurate prediction of revenue enables fair and equitable targets (vs. overly ambitious/demotivating goals). Ultimately, sales is looking to you to know which targets sales reps need to meet at minimum to qualify for variable commission or bonuses.

• Sales compensation plans: How much do you have to spend on commission that incentivizes reps after taking revenue projections and expenses into account?  

And it’s not just sales that model strategy based on revenue forecasts; projections have a major impact on the wider organization too, notably:  

Strategic business decisions
Revenue projections can change the broader direction of a business, like whether you’ll build out new product lines, expand into new markets, or hire new talent to meet demand or mitigate risks.

Financial planning and cash flow
If you’re anticipating a dip in revenue over the coming year due to competitors releasing new products, for example, you may look at how you'll secure investment, or otherwise invest in innovation and development.  

In manufacturing, inventory's on the line
Businesses that manufacture or source physical goods need to ensure a continuous supply, but both inventory shortages and overproduction are costly. Revenue projections anticipate how much inventory is required, so carrying costs or stockout risks can be avoided.  

The 5 steps in a sales ops leader’s key revenue forecasting workflow

Revenue forecasting is a team sport. The onus, however, is on sales operations leaders to bridge the gap between sales and finance for forecasting accuracy. You and your sales ops team are ultimately responsible for driving revenue and meeting those projections.

The typical sales forecasting framework looks like this:

1. Data collection. This is the initial ingestion of data from multiple sources, including your CRM & sales pipeline, ERP, and external market data. In your initial data collection, you'll incorporate long-term initiatives from your executive team into account, like any planned promotions or product launches that may impact revenue. Stakeholders should have visibility of this raw data, ideally with automated data feeds that funnel information towards a central repository, like a full-stack SPM solution.  

2. Data segmentation. Where you'll break down your data by product line, sales channel, or customer segment for more accurate and detailed forecasting.  
   

3. Conducting the analysis. Here, you'll determine meaningful conclusions drawn from your datasets. You may set simple hypothesis based on interpretation of related or interconnected data, like: “Historical sales data shows that revenue for the subscription service tends to trend upwards at year-end when mid-market companies have excess budget to spend....therefore, (and based on modelling data) we can reasonably assume <action> will lead to <result>.”
   

4. Create the forecasting model. Sales organizations typically create three scenarios: 'best-case' with optimistic projections, 'worst-case' with conservative assumptions, and 'most likely' based on data analysis. The latter is the most useful, though worst- and best-case scenarios help you prepare for either outcome if your most likely assumptions are incorrect.  

5. Review and adjust. Once you’ve got your forecast, you’ll take it to other stakeholders, including finance, HR, and the sales team for further adjustment. In this phase you'll incorporate feedback, tweaking revenue projections based on insight you hadn’t already considered.

Data collection: the backbone of reliable revenue forecasts

As you'll well know, clean, well-structured data in sales forecasting is the backbone of accurate projections.  

The last thing you want is to base crucial, long-term business decisions or sales strategies on a bias, or gut instinct.

Balancing both quantitative and qualitative data to get a well-rounded view of how much revenue your sales team is anticipating driving, you'll initially want to collect the following inputs:

Collect customer data

Shifts in consumer preferences can throw off your revenue forecasts. You'll want to create a habit of talking to your customers proactively to identify any forthcoming changes in future demand. For example:

• Are inflation concerns causing them to cut budgets? How might their customer trends and cashflow be changing willingness-to-pay benchmarks for pricing?

• Do they anticipate more disposable income to spend on products or services like yours?  

• How much untapped potential is there across each one of your customer types? (Here, you can do a potentialization exercise)

Consider market research and industry trends

‍You may enlist the help of market research firms to conduct large scale studies to uncover trends. If market research shows that the market is expanding by 10% year-over-year, for example, you can use similar figures to benchmark performance in your projections.

Run (even a quick) competitor analysis

Keeping an ear to the ground, make note of any funding rounds, product expansions, or territory focuses that could see customers spending on competitor products as opposed to yours.  

Monitor supply chain disruptions

Your ability to make revenue lives and dies upon your ability to supply products or services. As you put together revenue projections, Incorporate supplier lead times, material costs, inventory turnover metrics, production capacity, and supplier reliability. 

Stay up to speed on planned new offerings or expansions

If a new product has been in development, you can work to incorporate it into your revenue projections using historical data from previous go-to-market strategies.

Understand legal and regulatory changes in advance

Data protection laws, import regulations, and compliance requirements can impact your ability to drive revenue. Get legal counsel prior to making a revenue forecast to account for these changes.

Remember to identify and account for seasonality
Economic cycles and fluctuations mean revenue can differ dramatically throughout the year. You can extract meaningful insights—and subsequently, more accurate revenue projections—by identifying these seasonal trends from historical data and accounting for them in your upcoming projections.  

Leveraging your historical sales performance insights to improve forecast accuracy

After you've considered the above items (some running external to your business), it's critical you incorporate historical performance data into forecasts. This helps you assess how likely it is that deals will close and how much revenue they’ll generate.

For predictive forecasting, you'll typically pull the following sales performance metrics from your CRM, SPM, or otherwise:

• Pipeline health: How many active deals do you have in the pipeline at any given time? Understanding how leads move from one stage to the next helps you anticipate and fix any issues that could prevent a deal from progressing, therefore negatively impacting revenue.  

• Quota attainment: If reps consistently hit or exceed their quotas, it suggests that future revenue forecasts should factor in strong performance. Conversely, if quota attainment has been low, the forecast may need to be more conservative.  

• Sales cycle length: If your sales team meets quota, how long will it take for revenue they’ve generated to come into the business? B2B sales teams typically experience longer sales cycles. If they hit 100% attainment for a quota of 100 new accounts each month, it might take four months before they turn into revenue, data you can now account for in your predictive models.  

• Sales by product: Insight into how much revenue comes from upselling and cross-selling to existing customers helps estimate additional revenue streams. If 25% of a subscription company’s revenue comes from upselling extra seats on the plan, your sales comp strategy might evolve to incentivize reps with extra commission on these upsells. You’d need to take these strategy shifts into account as they directly impact revenue.  

• Retention: Revenue doesn’t just come from new customers—repeat business drives 44% of the average company’s annual revenue (despite accounting for just 21% of their customer base). Use retention metrics like churn rate and customer lifetime value to anticipate how much you’ll need to subtract from sales to get an accurate view of future revenue.  

• Sales compensation: Incentives motivate sales reps to achieve performance targets, but they can be expensive if not planned well (amounting to the right behaviors and, thereby, revenue). You can use predictive modelling within platforms like Forma.ai to simulate the effect of your sales compensation plan designs on revenue and profit.  

On data hygiene & prioritizing data quality

From data manipulation and human error when inputting data, to external factors, there are so many ways poor data hygiene hinders revenue forecasting accuracy.  

As organizations reach a certain size, Excel spreadsheets aren’t robust enough to manage data properly for forecasting. They’re prone to human error—and accidentally adding an extra zero to a figure can throw off the entire forecast.

Ultimately you need to be on top of data hygiene as the function owning forecasting.

Sales performance management (SPM) software, particularly tools incorporating forecasting capabilities, mitigate the risk and assist with accurate data collection and automations at scale.  

Aside from having robust systems in place to account for this, some best practices to maintain clean data pipelines inside your SPM solution include:

• Data validation and governance. Establish who owns data collection and management within your sales organization. This person is responsible for maintaining accurate data, conducting regular audits, and fielding data-related questions from your team.

• Regular data audits. You'll want to ensure you have complete traceability across the entire SPM process and multiple levels of data validation to highlight errors before they’re incorporated into your revenue forecasting models.

Collaborating with key business stakeholders for a unified forecast

Creating accurate sales forecasts is a team effort. While you’re responsible for anticipating sales and allocating resources to meet projections in sales ops, you need to work in lockstep with marketing, finance, and product teams to align on forecasting inputs.  

Cross-functional communication helps you create a consistent and realistic revenue outlook for anticipating future events. For example, you might not have realized that looming regulatory changes would impact revenue in a specific territory without counsel from your legal team, or that tensions with an existing supplier would impact your ability to sell a specific product in the long term.

Vince DaCosta, Global Sales Compensation Strategy Director at Databricks, adds that involvement from other stakeholders can act as an extra set of eyes:  

“Many companies lack internal expertise, and given the ever-changing nature of data, it’s essential to have someone with an external viewpoint who can analyze the data and reveal opportunities that may go unnoticed.”
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Fortunately, a full-stack SPM solution integrates all of your data and stakeholder workflows into one place. This to give complete visibility over your business and sales strategies. Forma.ai’s reports reference data from multiple sources, including HR and finance software, helping you automate data collection to accurately predict demand and plan accordingly.

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Best practices for building a sales ops-led revenue forecasting culture

Because everyone from sales and marketing to finance and the C-suite needs to come together to build an accurate projection of performance, the job is made easier with a data-driven company culture that values accurate forecasting across all departments.

So how should you step up to lead, all while enlisting help from key stakeholders?

Here’s what that looks like in practice:

• Value transparency and accountability across the organization—and lead by example. I.e. How might your sales ops team be actively reaching out to other teams in the org, facilitating buy-in, and leading the conversation about inputs to sales strategy early and often?

• Communicate the importance of data to your reps to get buy-in, particularly for those who don’t find data collection exciting. For example, “We need accurate data to forecast revenue and set fair quotas that let you meet or exceed on-target earnings”.

• Set KPIs related to data management, such as data accuracy or completeness (i.e. 100% completeness for CRM fields like the customer’s name, deal stage, and estimated value)

• Embed revenue forecasting into your daily sales operations workflow. This prevents last-minute scrambles to collect data at the start of a projection period

• Encourage experimentation with a test-and-learn approach. Be sure to record the results of the experiment in a central repository where they can be referenced and shared.

Continuous improvement: iterate on your revenue forecasting models

After putting together your model, remember that the data you’ve inputted today might be outdated next quarter, especially if you’re forecasting revenue for a business that is greatly impacted by seasonal fluctuations.  

Unexpected changes can also throw revenue projections off course with minimal notice.

Take the COVID-19 pandemic, for example. Nobody anticipated that a global pandemic would disrupt over $2 trillion in global trade until a mere few weeks before lockdowns put a halt on consumer spending and business’s ability to supply products and services.

And so it's critical you iterate over time instead of a set-it-and-forget-it approach.

That’s not to say you need to account for every worst-case scenario when forecasting revenue. More so, it highlights the importance of continuous data reviews and forecast adjustments that you’ll base important business strategy decisions upon. This simplest approach is incorporating real-time data synchronization with ERP, CRM, and SPM software to maintain more dynamic projections.

The data you uncover post-forecast analysis might require adjustments to your business and/or sales strategies. And this is where feedback loops are mission critical: your forecast might show a dip in revenue over the coming month—but do product teams, who plan to invest significantly in a new feature, know about the dip? Can HR and payroll support the incentives you’re offering sales reps to meet the projections?  

Final thoughts on forecasting future revenue growth

As a sales ops leader, you have a responsibility to deliver reliable, actionable forecasts. The onus is on you to collect data and draw meaningful insights, including financial projections that act as the basis for other business decisions.

While nobody can truly predict the future, continuous improvement, data quality, and cross-functional alignment go a long way in anticipating how much revenue your sales organization will drive over the coming months.  

Along with accounting for many of the factors above in your initial assessment, SPM software like Forma.ai can be the glue that connects these components together in one unified system—so you can spend more time doing what you do best: running an effective sales org that meets (or exceeds) revenue projections.

If you're looking to get all of your data talking together in one place—from territories, quotas, and incentives (everything surrounding sales performance management), we'd love to talk.

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