Beginner’s guide to sales forecasting

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What is sales forecasting?

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

Why is sales forecasting important?

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

Sales forecasting adds value across an organization. Finance, for example, relies on forecasts to develop budgets for capacity plans and hiring. Production uses sales forecasts to plan their cycles. Forecasts help sales ops 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 processes 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 that don’t align with sales quotas or sales attainment levels. This leaves a company with too much inventory, or 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.

What are some benefits of having an accurate sales forecast?

An accurate sales forecast 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

How to accurately forecast sales

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

  1. 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.

  2. Incorporate changes
    This is where the forecast gets interesting. After you have your basic sales run rate, you want to modify it according to a number of changes that 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 up any new channels? New locations? New territories?
    • Product changes. Are you introducing new products? 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?

  3. 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?

  4. 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.

  5. Include business plans
    Add in all of your business’ strategic plans. Are you in growth mode? What are hiring projections for the year? New markets you’re targeting? New marketing campaigns? How might all of these impact the forecast?

    Once you’ve quantified all of these things, build them into your forecast. You want everything to be itemized, so that 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.

Should you do a bottom-up sales forecast or a top-down sales forecast?

In general, there are two types of sales forecasts: 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. 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.

A top-down sales forecast starts with the total size of the market (the TAM—total addressable market), 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 that they can win 10 percent of that market, making their sales forecast $50 million for the year.

When making a sales forecast, it’s important to use both of these 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.

Keys to success in sales forecasting

Improving the accuracy of your sales forecasts and the efficiency of the forecast process depends on multiple factors, including strong organizational coordination, automation, reliable data, and analytics-based methodologies. 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 pulse 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 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 better forecasting processes and tools perform better than their peers because they better understand their business drivers and have the ability to shape the outcome of a sales period before that period is closed.

What are some key sales forecasting challenges?

It can be difficult to produce a consistently accurate sales forecast. Some of the most common challenges 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
    • Stakeholders across the company 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. Companies forecasting with simple arithmetic pipeline weightings, for example, 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 that makes it useful for stakeholders across the company, it becomes far less effective than it should be. A good forecast should produce data that’s relevant to multiple teams, and understandable by them.
  • 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 in the forecast if versions are rolled out and then revised.

How can a company forecast 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 inputs 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 following capabilities:

  • 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.
  • “What if” scenario modeling and analysis
    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 that 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
    Create snapshots of Salesforce CRM accounts and opportunities and 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 (e.g., 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, etc.). 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. In a webinar, Abe Awasthi, Senior Manager at Deloitte, presented a short example that explains 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 that could be feasible for many companies.

Why 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 that 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.

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