12 min read

Beginner’s guide to sales forecasting methodology

Anaplan

The platform for orchestrating performance.

What is sales forecasting? 

Sales forecasting is the process of estimating future revenue by predicting the amount of product or services a sales unit (which can be an individual salesperson, a sales team, or a company) will sell in the next week, month, quarter, or year. 

At its simplest, a sales forecast is a projected measure of how a market will respond to a company’s go-to-market efforts. 

Why is sales forecasting important? 

Forecasts are about the future. It’s hard to overstate how important it is for a company to produce an accurate sales forecast. Privately held companies gain confidence in their business when leaders can trust forecasts. For publicly traded companies, accurate forecasts confer credibility in the market. 

Sales forecasting adds value across an organization. Finance relies on forecasts to develop budgets for capacity plans and hiring, and production uses sales forecasts to plan their cycles. Forecasts help sales operations with territory and quota planning, supply chain with material purchases and production capacity, and sales strategy with channel and partner strategies. 

These are only a few examples. Unfortunately, at many companies these methodologies stay disconnected, which can produce adverse business outcomes. If information from a sales forecast isn’t shared, for example, product marketing may create demand plans not aligned with sales quotas or sales attainment levels. This leaves a company with too much inventory, too little inventory, or inaccurate sales targets — all mistakes that hurt the bottom line. Committing to regular, quality sales forecasting can help avoid such expensive mistakes. 

Benefits of having an accurate sales forecast 

An accurate sales forecast process confers many benefits. These include: 

  • Improved decision-making about the future 
  • Reduction of sales pipeline and forecast risks 
  • Alignment of sales quotas and revenue expectations 
  • Reduction of time spent planning territory coverage and setting quota assignments 
  • Benchmarks that can be used to assess trends in the future 
  • Ability to focus a sales team on high-revenue, high-profit sales pipeline opportunities, resulting in improved win rates  

Bottom-up sales forecast or a top-down sales forecast? 

In general, there are two types of sales forecasting methodologies: bottom-up forecasts and top-down forecasts. Bottom-up forecasts start by projecting the amounts of units a company will sell, then multiplying that number by the average cost per unit. You can also build in the number of locations, number of sales reps, number of on-line interactions, and other metrics. 

A top-down sales forecast starts with the total size of the market (the total addressable market or TAM), then estimates what percentage of the market the business can capture. If the size of a market is $500 million, for example, a company may estimate they can win 10% of that market, making their sales forecast $50 million for the year. 

The idea behind a bottom-up sales forecast is to begin with the smallest components of the forecast and build up from there. The advantage to a bottom-up forecast is that if any variables change (like cost per item or number of reps), the forecast is easy to modify. It also provides fairly granular information. 

When making a sales forecast, it’s important to use both methods. Start with a top-down method, then use the bottom-up approach to see if your first estimate is feasible, or do the two separately and see how well they accord. To produce the most accurate forecast, companies should perform both types of forecasts, then tweak both until they produce the same number. 

How to accurately forecast sales 

To create an accurate sales forecast, follow these five steps: 

Assess historical trends

Examine sales from the previous year. Break the numbers down by price, product, rep, sales period, and other relevant variables. Build those into a “sales run rate,” which is the amount of projected sales per sales period. This forms the basis of your sales forecast

Incorporate changes

This is where the forecast gets interesting. After you have your basic sales run rate, you want to modify it according to several changes you see coming. For example:

  • Pricing: Are you changing the prices of any products? Are there competitors who may force you to modify your pricing schemes?
  • Customers: How many new customers do you anticipate landing this year? How many did you land the previous year? Have you hired new reps, gained quantifiable brand exposure, or increased the likelihood of gaining new customers?
  • Promotions: Will you be running any new promotions this year? What is the ROI on previous promotions, and how do you expect the new ones to compare?
  • Channels: Are you opening any new channels, locations, or territories?
  • Product changes: Are you introducing new products or changing your product suite? How long did it take for previous products to gain traction in the market? Do you expect new products to act similarly

Anticipate market trends

Now is the time to project all the market events you’ve been tracking. Will you or your competitors be going public? Do you anticipate any acquisitions? Will there be legislation that changes how your product is received?

Monitor competitors

You’re likely doing this already but take into account the products and campaigns of competitors, especially the major players in the space. Also check around to see if new competitors may be entering your market. 

Include business plans

Add in all your business’s strategic plans. Are you in growth mode? What are hiring projections for the year? Are there any new markets you’re targeting or any new marketing campaigns? How might all this impact the forecast? 

Once you’ve quantified these things, build them into your forecast. You want everything to be itemized, so you can understand the forecast in as granular a level as possible. Different stakeholders in the company will likely want to understand different aspects of the forecast, so it behooves you to be able to zoom in or out as far as needed. 

Keys to success in sales forecasting 

Improving the accuracy of your sales forecasts and the efficiency of the forecast methodology depends on multiple factors, including strong organizational coordination, automation, reliable data, and an analytics-based process. Ideally, sales forecasts should be: 

  • Collaborative. Leaders should synthesize input from a variety of sales roles, business units, and regions. Frontline sales teams can be of great value here, providing a perspective on the market you hadn’t considered before. 
  • Data-driven. Predictive analytics can reduce the impact of subjectivity, which is often more backward-looking than forward-looking. Using common data definitions and baselines will foster alignment and save time. 
  • Produced in real time. Investing in the real-time capability to course-correct or reforecast allows sales leaders to quickly gain insight so they can make more informed decisions. This enables them to quickly and accurately update the forecast based on demand or market changes. 
  • Single-sourced with multiple views. Generating the forecast as a single source of data gives you great visibility into rep, region, and company performance, and helps align different business functions across the organization. 
  • Improved over time. Use the insights provided by an improved sales forecasting process to create more refined future forecasts where accuracy improves over time against a set of accuracy goals. 

Companies with more advanced forecasting processes and tools perform better than their peers because they more deeply understand their business drivers and can shape the outcome of a sales period before the period closes. 

Key sales forecasting challenges 

It can be difficult to produce a consistently accurate sales forecast. Some of the keys to success in sales forecasting include: 

Accuracy and mistrust 

When companies use spreadsheets for sales forecasting, they can run into issues with accuracy, which in turn creates a less trustworthy forecast. These issues with accuracy can be exacerbated by: 

  • Poor adoption of CRM across the company and employees not entering data in a timely manner 
  • Inconsistent data across teams, or salespeople not inputting complete data 
  • Company stakeholders using different methodologies to produce their forecasts 
  • Insufficient collaboration across product, sales, and finance teams. This lack of collaboration can be heightened when companies produce sales forecasts manually or using spreadsheets. 

Subjectivity

Although producing a quality sales forecast does rely to a small degree on the forecaster making good decisions about how to use the data, in general, companies rely more on judgement and less on credible predictive analytics than they should. For example, companies forecasting with simple arithmetic pipeline weightings may miss the nuances of the real drivers of accuracy, which may be headcount, pricing decisions, or route-to-market points of emphasis. 

Usability

When a sales forecast isn’t generated in a way useful for stakeholders across the company, it becomes far less effective than it should be. A good forecast should produce relevant and understandable data for multiple teams. 

Inefficiency

Sales forecasts can be especially difficult to produce when inefficiencies are built into the forecasting process. For example, when a forecast has multiple owners, or the forecast process is not clearly spelled out with a standard set of rules, there can be disputes about how the forecast will be produced. Similarly, if inputs into the forecast are not reconciled before the forecast is produced, the forecast itself may be subject to many revisions, which can reduce trust if versions are rolled out and then revised. 

Company forecasts across the enterprise 

To forecast across the enterprise, a company needs different elements from each business function. Here’s what different functions can contribute to the sales forecast: 

  • Sales: Provides the bottom-up view, using data from the CRM and PRM, building in judgment from sales leaders. Sales can manage this process through the sales operations function, using the right tools, and reporting. 
  • Finance: Provides macro-economic guidance and works with the product teams. Finance can help integrate the forecast with their financial planning software. 
  • Marketing: Provides macro-market guidance, especially in industries like telecom, retail, and CPG. Marketing can also provide finance teams with market data. 
  • Supply Chain: Provides input on supplies and production. 
  • IT: Assists sales forecasting by providing platforms, data, integration, and technical support. 

Key features of effective forecasting software 

Best-in-class sales forecasting software should be able to immediately improve the accuracy of your forecasts and make the forecasting process more efficient. It should therefore offer the ability to: 

  • Execute sales forecast simulations and outcomes. Make changes to drivers and execute sales forecast simulations to project future impact on sales performance. 
  • Analyze trends, changes, and seasonality of the sales forecast over time. Develop time-based dashboards and key performance indicators (KPIs), such as velocity calculations, trending analytics, and seasonality fluctuations. 
  • Model and analyze “What if” scenarios. Create “what-if” scenarios and modeling to analyze the impact to the sales forecast if a specific business, economic, or competitive situation were to occur. Prepare for challenges you might encounter in upcoming deal cycles. 
  • Build sales forecasting calculations with familiar formulas. Apply an easy-to-use formula builder to configure sales forecast benchmarks using familiar formulas and syntax. 
  • Snapshot Salesforce CRM accounts and opportunities to compare period-over-period. Compare week-over-week, month-over-month, and year-over-year changes to current periods. 
  • Compare forecasts based on multiple modeling techniques. Create sales forecasts based on qualitative, time series analysis and projection, and casual modeling techniques while determining the degree of uncertainty with the sales forecast accuracy and predictability. 
  • Forecast across geographies, products, and accounts. Develop sales forecasts by geographic locations, product lines, and accounts, or change any of these dimensions to analyze the sales forecast at any granularity of these hierarchies (by state/city, a specific set of product SKUs, or a group of accounts in a selected vertical). 
  • Analyze performance with data visualization. Built-in dashboards, reporting, and analytics with data visualization (charts, graphs, maps, and more). Dashboards and reports are updated immediately. Analyze sales forecast and sales performance metrics to make better decisions with actionable insights. 

The future of sales forecasting: predictive analytics 

Predictive analytics is already transforming many areas of business and sales forecasting is no exception. Even so, terms like “predictive analytics” and “machine learning” can still be intimidating. Abe Awasthi, Senior Manager at Deloitte, shared a short example explaining how predictive analytics can improve forecasting: 

A tech company asked Deloitte to produce a predictive model to improve sales forecast accuracy. To create their model, Deloitte leveraged the company’s pipeline data from the previous few years with customer and employee names removed. Deloitte then used machine learning to extrapolate from historical trends and fill in the gaps in the data. 

Deloitte then used this data to build two predictive forecasting models: one calculated the probability that any given deal would close, and the other predicted the time frame in which that close would happen. When combined, these models provided highly actionable, very specific recommendations to the company’s sales team: “push opportunity number five to qualified within the next 10 days or you’re going to lose it!” 

Importantly, Deloitte was able to build these predictive forecasts in 8-12 weeks — a timeline feasible for many companies. 

Why use Anaplan for sales forecasting? 

The Anaplan platform is uniquely configured to improve sales forecasting. By putting all relevant employees—salespeople, sales leaders, ops teams, finance, supply chain, marketing, and executives—on the same platform, companies can do the following: 

  • Increase accountability and ensure the sales team reports sales pipeline activity more accurately. Identify sales deals at risk, eliminate “sandbaggers,” and reduce overcommits. 
  • Standardize sales forecasting and pipeline management. Provide a single line of sight across the entire organization so everyone has a view into revenue projections, sales projections, and operational insight. 
  • Create accurate and trusted sales forecasts. Allow functional leaders to make better and more informed decisions by providing accurate and trusted sales forecasting to all business units, including sales, finance, operations, HR, and marketing. 
  • Access data-driven sales benchmarking and trend analysis. Enable sales leaders to use historical and current sales performance as a benchmark to predict future sales results. Make changes to functional plans and implement these changes across all other business models. 

By adopting a Connected Planning approach, bringing together people, data, and processes from across the enterprise, companies can produce an accurate sales forecast that connects teams throughout the company, keeping everyone better prepared for the future. 

Watch an on-demand webinar with Anaplan and Deloitte, Feeling the Heat? Five ways to improve sales forecasting, to learn the five ways to improve your sales forecasting in turbulent times and focus on ready-to-use models and customer examples. 

 

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