The Art and Science of Accurate Sales Forecasting

Other than debating who has closed the best deals or who has the most challenging sales territory, few topics in sales will generate more discussion in a quarterly sales meeting than the topic of sales forecasts. What deals qualify to go in a forecast? What should go into upside? And even what is the difference between a sales forecast and a market sizing exercise? These points matter both to sales teams and the wider business, so let's explore them in more detail, the common issues of getting it right, and how the sales performance management (SPM) tech stack can replace guesswork with certainty and success.

Why Does it Matter?

Sales forecasting is a way of assessing the tempo of how a business is engaging with its prospects and customers. It shows what products are doing well and which need more sales focus if they are to be successful. It acts as an early warning system, helping managers understand what the next few months will look like for the business. Will the company grow faster than planned, or are there stormy waters ahead? Do we ramp up investment in new plant and distribution, or should we invest more in marketing and promotion? How well are our partnerships adding to the bottom line?

For managers in all parts of the business, not just for the sales and marketing team, accurate sales forecasts are a vital tool in running the business well and helping it meet its objectives in the short, medium, and long term.

Sales Forecasting: A Lucky Guess or an Informed Judgement?

Sales forecasting is both an art and a science. It depends on metrics, like the size of the addressable market - how many companies are ideally placed to buy your product or service or how many consumers might need that new smart device? It depends on the ability to capture leads, qualify them, convert them into opportunities, and manage them to a close. These numbers should almost be predictable for B2B companies, at least, with a third of opportunities being closed successfully being the norm.

That said, judgment needs to be applied. Are the numbers being presented at a sales meeting unrealistically optimistic or pessimistic? Should a sales manager be conservative or bullish? It depends on the context, as it may be in the interests of sales teams to be bullish to attract additional marketing support or conservative to maintain their credibility.

Common Mistakes in Sales Forecast Accuracy

Forecasting sales involves both objective and subjective aspects, with scope for mistakes in both. Let's see why errors get made.

1. Lack of Training

One issue, especially early in a salesperson's career, is a failure to understand how to forecast properly, whether it be over a quarter or a year. This can also be prevalent when new sales and marketing teams are created to promote a new product or when starting a new business, where there’ll be different perspectives on what forecasting can mean. A common reason for inaccurate forecasting is a poor understanding of the BANT/MANT acronym, where the issues of Budget/Money, Authorities, Needs, and Timings are critical. What is especially important are the authorities involved in closing transactions. One sales adage says that 90% of sales are lost on authority issues, perhaps where a salesperson can’t identify the final authority in a purchase or meet with them. If a salesperson doesn’t fully grasp this issue, and their sales manager doesn’t challenge and coach them, then that salesperson's forecast is flawed.

The same can be applied to market sizing exercises. The biggest challenge here is defining what a market is. Is it companies of a specific size - and if so, is it headcount or turnover? Does it relate to a specific geographical market, in which case, how do you know the data is accurate? Another problem, especially for inexperienced marketers, is to combine several prospect categories and call them the addressable market, when in reality, the chosen categories may have little in common.

2. Poor Data Management

Another source of error is the way that mistakes creep into forecasts because of poor data management. Market sizing projects often use multiple data sources with different definitions, formats, and other variables, making it easy for data errors to creep in if marketers aren’t careful. Issues around double-counting, conversions, and variations in definitions offer scope for errors that can lead to results that significantly over or under-estimate the opportunity available. It can often take an experienced eye to spot something wrong and find out where the problem lies.

In sales forecasts, data issues can often develop in the ways that sales forecasts are captured and consolidated. If spreadsheets are used, then data errors can be introduced during the rolling up process, where data is overwritten.

Another issue can be complex product definitions, where different products, services, and support options are available. These can confuse the unwary, perhaps causing them to omit products or services that are essential for an implementation or include something a customer doesn’t need.

3. Subjective Replenishment

Sales is a pressured environment, with the constant expectation of delivery and performance against target. When an individual – a salesperson or manager – has been put under pressure to contribute more, it’s all too easy to start seeing opportunities where there really aren't any, at least not yet. It’s easy to overestimate the size of a deal or how far it really is down the sales funnel.

For marketers, the challenge is similar. Under pressure to generate a pipeline for a sales team hungry for leads, again, it’s all too easy to propose a campaign that gets little traction and leads, or else pass over “leads” that are merely prospects showing some interest, but little commitment.

4. Lack of a Clear Sales Process

Another reason forecasts go awry is because there is no sales process in place for the business, or it isn’t correctly followed, and the sales management fails to pick up on the problems. The ideal process, at least for B2B sales, is there needs to be a defined budget, it’s clear who the ultimate authority is, there is a defined and recognized need, and the timescales are clearly understood. The skill for a salesperson is to build rapport with the customer so that this information is willingly offered up. The really smart salesperson will review this information critically to spot someone who says they have the authority to buy, but in reality, does not. Customers sometimes overplay their hands when dealing with suppliers.

5. You Have Too Much Confidence in your CRM Systems

A well-managed and well-understood CRM system is a real asset to a sales function. However, a company needs to be fully aware of the strengths and weaknesses of their CRM environment. It’s all too easy to look at the numbers input by the sales team and treat them as “the truth.” Sales managers need to challenge their sales teams and, if necessary, get their salespeople to qualify harder, rather than relying on their “happy ears” to understand the chances of a proposal being successful. Another issue is that salespeople may put all their pipeline deals into a CRM system with little sense of how real these deals are. These make the salesperson look diligent but can inflate the value of their pipeline, which ultimately helps no one.

This is where proper market sizing information using current market data becomes valuable. It provides an objective view of the overall opportunity and where it’s likely to be found. It can provide a sales forecast at the market level, showing what is potentially achievable. Sales managers can use this insight to sanity check the information coming out of the CRM system.

The 6 Types of Sales Forecast Reporting

The term forecast can cover a broad range of predictions and plans. It can come from a range of systems, and it isn’t unusual for several reporting models to be used in creating a sales forecast, depending on the size of the company and the structure of its sales team.

1. CRM Reports

Data from CRM systems is increasingly forming the basis of many sales forecasts. CRM systems ideally contain all customer information – name, title, address, a record of most engagements, as well as proposals and, of course, deal forecasts where appropriate. Given this plethora of information, CRM reports are typically the first, but not the only, port of call when creating sales forecast reports.

2. Spreadsheets

Many organizations continue to use spreadsheets as their primary forecasting tool. There are benefits; it requires few skills to use or manage a spreadsheet, and it offers powerful functionality that makes collating information straightforward.

That said, spreadsheets have few controls. It’s easy to overwrite data without anyone realizing it. They are typically designed to be used using a desktop or a laptop, so no mobile computing here.

It isn’t unusual for sales management to use spreadsheets in their “final mile” sales forecasts as part of their process in applying their expert judgment to their team’s sales forecast. Again, while flexible, it’s prone to error and should be managed accordingly.

3. CRM + BI + Spreadsheets (Again)

CRM platforms offer enormous functionality and scope for development to meet the unique needs of the business. Indeed, there may be too much functionality, or else these platforms can be cumbersome to use or need scarce and expensive skills to utilize. Furthermore, it may be that the information management needs in the business to make investment decisions often resides in several applications or also in other CRM systems.

To overcome these limitations, it’s practical for senior managers to use business intelligence (BI) applications to help make sense of large, complex, and diverse data sets from multiple data sources. These applications use graphical tools to help synthesize and present the data to help managers make informed decisions.

Again, it isn’t unusual for the reports from the BI applications to be exported as Excel spreadsheets, again for ease of use, but again with the usual risks associated with using spreadsheets.

4. Verbal Reports

Reports fed back in meetings and sales reviews also form an essential part of the forecasting process. Sales managers like to see the “whites of a salesperson's eyes” when reviewing the deals they have in their pipeline, their upside, and their forecast. It’s an important part of the calibration of the sales process, providing an opportunity for direction and coaching.

5. Predictive Forecasting

An individual salesperson's forecast works well if they are responsible for a defined territory, whether it be some named accounts or the accounts in a specific geographic region. They’ll have a range of opportunities they can manage and close. In broad terms, this is the classic B2B sales model.

There are other situations where there are many transactions in many different places, as typically found in B2C sales environments. Here, it’s impractical to predict every transaction, so companies take a more predictive approach to forecasting. Here, one takes external factors - the time of year, the weather, the opening of new retail venues, for example - to assess the impact on sales. Predictive models will utilize these data points as well as previous sales records and other business data sources.

6. Automated Reporting

With the possible exception of when using spreadsheets, the complexity of the data and systems demands the use of automated reporting processes to help managers get the information they need, when they need it, in the correct format.

Modernizing Your Process to Improve Sales Forecasting Accuracy

Sales managers and marketing managers are both blessed and cursed compared to their predecessors. Big data capabilities mean they can understand their market as never before and engage their chosen audience in ways never before imagined.

These capabilities can help them, almost at a single click, to understand where the best opportunities are likely to be found, who the ideal audience is, and how to engage with them most effectively to maximize results.

The challenge for everyone is there is no instruction manual that shows you how best to do this.

So, let's take a look at how you might use big data capabilities to help you enhance your understanding of the size of your addressable market so you can better forecast and manage the opportunity you have in the short, medium, and long term.

1. Construct a Reality Check

The first step is to practically define your market to provide clarity on which companies, industries, or consumer segments are the ideal audiences for you.

With data as your friend, the trick here is to capture and synthesize multiple data sources to identify which segments have the greatest propensity to buy your product. For B2B opportunities, this may be based on headcount, turnover, geographic spread, growth rate, or a vast range of other variables. B2C opportunities may relate to the size of the population in a geographic region, a change in season, events like students returning to school or college, or broader social changes, for example.

You can combine this data with your own CRM, e-commerce, and sales data to enrich the dataset to provide a rich and sophisticated model of your target market's scale, scope, and dynamics. This gives you your starting point in understanding your market and directing your sales and marketing effort to achieve your objectives.

2. Examine the Present and Future Pipelines

With your baseline in place, you can begin to review your pipeline to see if it’s going to hit your target. Given that pipeline typically needs to be three times the target, you can begin to see if your sales team's numbers stack up compared to your baseline model. This capability can be extended from three months to the next 18 months, allowing you to develop and refine your sales and marketing campaigns.

3. Conduct One-on-one Coaching Sessions Based on Data

With the insight and comparisons gained, it’s possible to identify areas for both sales and marketing teams to delve deeper into the data. This can help validate the insights and help them draw up plans to meet any sales shortfalls or accelerate the growth of the sales pipeline and the deals closed.

4. Engage your Sales Operations Team

This type of sales analytics shouldn't be a one-off or annual exercise. Your target market will change and adapt, and your competition won’t be idle either. They’ll be trying to win the same customers and prospects you’re targeting. Your analysis should be regular and ongoing, ready to find that new insight that may point to new opportunities.

Your sales operations team is well placed to manage the ongoing review of your forecast model and circulate the new insights that emerge.

The Benefits of Sales Forecasting Software

1. Decision-making and Preparedness

A sales strategy is very much based on actioning a constant flow of decisions, so it makes sense that implementing enhanced sales forecasting as a methodology will improve that process. Whether it's a greater knowledge of when to enter new territory, making a call on discontinuing a product line, or weathering the storm of a global crisis, the level of preparedness (and subsequent resilience) can only be bettered when partnering with robust sales forecasting software.

2. Accuracy

While there are no assurances that every tactical and strategic move will naturally end in more profit, the likelihood is certainly greater when accurate and reliable data supports your decision-making. When your sales forecasting predicts potential growth, that can lead to you recruiting more staff, allowing you to pursue new territories. Conversely, if there looks to be a dip, you can mitigate for that without too much collateral damage.

What Should You Think About When Selecting Your Sales Forecasting Software?

It’s difficult to overstate the importance of selecting the right sales forecast software application for your business. Here are a few considerations to think about:

1. It Needs to be SaaS-based

With staff working from any location these days, your sales forecast application should be easy to use from anywhere, whether it be your offices, a home office, or on the move so that those new insights don’t have to wait until you’re back in the office.

Using SaaS-based applications also means that you don’t have to wait until your IT department has the time and budget to get on with this important project. Start your forecasting when you need to, not when someone else has the time.

2. Easy Data Integration

The data integration needs to be straightforward. More data – supported by the right tools - drives better insights. Data sources – whether your own business applications or external data sources – need to be easily accessible. Use straightforward data integration tools that work at the click of a mouse, without the need to wrestle with scripts or coding to get the results you need.

3. Leverage AI Capabilities

Big data projects don’t need to mean having to recruit a team of analysts and data scientists to make sense of everything. The development of AI, in parallel to big data capabilities, means that you can leverage AI capabilities to explore your data sets while you get on with the day job.

These capabilities can proactively monitor your data, highlighting issues and themes that help you make better-informed decisions, or pursue new lines of investigation that can help build your pipeline and take your business forward.

4. Integrate Your Forecasting System into the Rest of Your Sales Process

Accurate forecasting should be a mainstay of running your business, but it needs to be fully integrated into the rest of your sales process to deliver the best results.

Use the same data set to design and optimize your sales territories to maximize your coverage and create the ideal motivation for your sales teams to succeed.

Use the same infrastructure to manage your incentive compensation management program so that your sales team can see how they’re performing against quota and how they’ll be paid on the deals they close.


Want to know more about what's new in sales forecasting? Check out blog, The Next Big Thing in Sales Forecasting to learn more.