What is Sales Forecasting
Sales forecasting is one of the most useful techniques a company uses and is an intricate part of a company’s decision-making. With the correct sales forecasting technique, a company can set goals, determine benchmarks, set budgets, and map out any other financial factors that can lead to the success of a company optimizing its business model.
What is challenging about sales forecasting is that since it is a predictive technique, a company’s sales team can often have a difficult time getting accurate results that replicate real-life revenue impacting factors. Despite what is said online, there is not a magic formula or software that can be used to get 100% precise sales forecasts.
Factors That Can Impact Sales Forecasting
No matter how expensive a customer relationship management (CRM) software is or how sophisticated the artificial intelligence technology is, it will be able to predict a company’s sales forecast with 100% accuracy, and here is why. Despite popular belief, sales forecasting is not a ‘guesstimation’ of revenue. It is, in fact, a complex science that factors internal data, economic indicators, global factors, industry trends, inflation, seasonal demand, and marketing efforts to derive a sales forecast.
A sales forecast is only as good as its data. Without clean and accurate data, the sales forecast will be useless. One common error that companies often experience is that sales reps do not update their sales pipeline. It makes sense because sales reps are busy selling and do not have time for menial busy work like filling in data.
This can greatly impact revenue prediction for a company and can deter future sales forecasts as well. Account executives, sales leaders, and or sales managers must ensure that their sales reps are updating the pipeline daily so the data is current. Any small changes in the sales pipeline need to be recorded to have precise data.
The Hard and Fast Truth of Sales Forecasting for Companies
More often than not, companies think that their sales forecasts are correct at the time and when the forecasts fall short of reality, companies blame the software.
Four things are true for most companies about forecasting:
Sales reps often do not know which data is relevant and should be recorded in a CRM software. Companies should make a point to coach their sales team and reps to make sure they understand which qualitative and quantitative data to record.
Data should be recorded in real-time. A sales manager can sometimes overlook this issue and therefore reps can fall behind with recording their sales data. Sales managers need to coach strategically so that reps know what data to prioritize.
When a sales forecast isn’t data-driven due to the lack of historical or current sales data, companies can have issues with their revenue forecasts.
Without proper sales, forecast companies are not able to take a correct course of action.
Common Mistakes Businesses Make When Forecasting Sales
Just to clarify, sales forecasting is not just quantitative data used by a company. It is, in fact, one of the most crucial processes of a company.
Sales forecasting is an innovative process and therefore companies should invest in good CRM software instead of relying on Excel spreadsheets.
The two most common forecasting mistakes companies make is over-forecasting or under-forecasting their sales.
Under-forecasting sales can impact the cost of goods sold which can lead to a decrease in gross margins and slow down both product development, and the growth rate of a company.
Under-forecasting sales can also mean more layoffs leading to a loss of employee morale and productivity.
Under-forecasting can also lead to a decrease in cash flow which can impact the overall business plan for a company. It can decrease investor confidence in the executive team and lead to negative brand implications with consumers.
Over-forecasting can negatively impact a business cycle because it can lead to inaccurate inventory counts. When this happens, companies do not have enough inventory to meet the needs of the business which can lead to investors questioning the CEO/CFO’s capabilities.
Over-forecasting can also lead companies to increase quotas for their sales team. Employees are unable to reach quotas which can lead to layoffs. Additionally, executive teams who are making these decisions can lose credibility with the board.
Things to Consider Before You Begin Forecasting
It is very easy to jump into a method and generate a sales forecast. However, whether that forecast is accurate is a whole other story. Aside from intuitive sales forecasting, every other forecasting method requires at least 12 months of sales data. So, what does that mean exactly?
A company could be around for a couple of years but to get precise sales data, the business needs to have 12 months of continuous data. Having random deals occasionally without continuous efforts will not help with the sales forecast.
This also means that the business needs to be actively perusing leads in addition to making sales for at least one year.
In addition, there should also be a detailed history of these sales. Metrics such as which time of year, any important global or economic impacts, the stage of the pipelines, and any contact and company information are crucial for an accurate forecast.
Who is the Fairest of them All? Which Sales Forecasting Method is the Best and Why You Should Use It
There are multiple sales forecasting methods out there and it can get overwhelming when deciding which one is the best method for your business. The reality is there is only one forecasting method that is universally used and it is called roll-up or bottom-up forecasting.
There are other anchoring forecasting methods that can also play a role in revenue prediction such as AI sales forecasting and historical sales forecasting. However, as mentioned above, they are considered supporting forecasting methods and should be used as such.
Sales Forecasting Methods
Now that we have gone over the cautionary tale for sales forecasting and talked about which sales forecasting method is considered to be the best, let’s dive into all ten methods to get a better understanding as to why roll-up, historical, and AI sales forecasting are the most common go-to forecasting methods.
Historical Sales Forecasting
Historical sales forecasting is the most well-known sales forecasting method because it relies on historical data to predict future revenue.It should be treated as a layering forecasting method, meaning, that it should be used in conjunction with other methods.
A company needs at least 1 year, if not more, of good reliable sales data. If the past data is not reliable, then this forecasting method is useless.Typically, companies use data from the past at the same current time to predict sales. The general assumption is that revenue will be the same as or be greater than the past data.
Do you see the problem with this forecasting method? It does not take into account anomalies and seasonality. The COVID-19 pandemic is a perfect example. In previous years around this time, companies would not necessarily have developed a contingency plan of action in their business plan for the pandemic.
If a company in 2020 was to use historical sales forecasting as its’ primary forecasting method, the data would be irrelevant. The best way to use historical sales forecasting is to use it as a guideline. Treat the data as benchmarks instead of as hard-set metrics that define your forecasts.
Historical Sales Forecasting Example:
Let’s assume, last year during the 3rd quarter, given that economic and market conditions were normal and constant, your company brought in $1,000,000 in revenue.
If your company was forecasting for this year, it would use the $1,000,000 as a benchmark and assume that given the same market and economic conditions as the previous year, this year’s third-quarter forecast will be about the same if not greater. Companies can also calculate the historical growth rate to analyze data in more detail.
To calculate the historical growth rate, subtract previous years’ numbers with the current year’s number. Take care to make sure they are from the same time period. Then divide the value by the current year and multiply by 100 to get the percentage.
Roll-Up Sales Forecasting
Roll-up forecasting is the most basic forecasting method because it relies on very little data. This is why it is the preferred forecasting method for many companies.
The cool thing about roll-up forecasting is that you can get into more sophisticated technologies. Unlike roll-up forecasting, all the other forecasting techniques depend on at least 12 months of data.
You might be wondering why data from the reps is important. Since reps are the ones that are closing the deals, they have the most reliable form of data. No matter how sophisticated a CRM software can be, it cannot account for human interaction and experience.
Many companies use Salesforce as their primary sales software. In Salesforce there are two ways to record rollup forecasting. Roll-up forecasting can be recorded individually or cumulatively.
Individual Roll-Up Forecasting:
Individual roll-up forecasting is when all the opportunities in a particular stage in the pipeline are added up together. Typically, the stages are: Pipeline, Best Case, Commit, and Closed.
Cumulative Roll-Up Forecasting
Cumulative roll-up forecasting is when opportunities in multiple stages of a pipeline are added together. For example, open pipeline is a summation of pipeline, best case, and commit.
This forecasting method is meant to show that even though some opportunities in a pipeline have not been closed yet, it is likely that they will be in the current forecasting period.
The benefit to this is that each rep can have an idea as to how much they are going to bring in for the sales period.
The downside however is that since this forecasting method relies on reps’ intuition, the expected value can be slightly inaccurate if not verified by a sales manager.
Roll-Up Forecasting Example
If a company is trying to figure out a commit forecast, they would add up all the opportunities that are in the committed stage of the pipeline in addition to all the opportunities that have been closed.
If the commit stage has four deals, each valued at $100, and the closed stage has five deals valued at $200, the total commit forecast would be $1400.
Opportunity Stage Sales Forecasting
Opportunity stage forecasting is a sales forecasting method that takes into account various stages of the sales process and where each deal is in the pipeline. The entire premise around opportunity stage forecasting is that the further along a deal is in a pipeline, the more likely it is to close.
For this forecasting method, a company develops various stages of a pipeline and then assigns them a probability. The probability is indicative of the likelihood that the deal will close. To use this forecasting method, simply multiply the size of the deal with the probability and that will give you the forecast.
Like with any forecasting method, this forecast is only as accurate as the data that is being used. Having clean data is essential for this forecasting method.In addition, some companies like to also consider the size of a deal, which is something that this forecasting method does not take into account.
This forecasting method is best used as an anchoring method in conjunction with other sales forecasting techniques.
Example of Opportunity Stage Forecasting
Let’s take a sample pipeline. Imagine that there are five opportunities, each valued at $500,000 in the pipeline, and they are all at the negotiation stage of the sales pipeline. In our example, we gave the negotiation stage an 80% probability. This means that each deal is worth $400,000. Since there are 5 deals in the pipeline at that stage, the total value is $2,000,000.
Artificial Intelligence Sales Forecasting
Artificial Intelligence (AI) is being incorporated into every aspect of a business, so why shouldn’t it also be in sales forecasting. Most companies, including small businesses, want to go ahead first and spend money on an expensive CRM software that uses AI. When companies purchase this software, they are often left unsatisfied because the data is not accurate.
Here’s the truth, with any kind of CRM software that uses business intelligence tools like Artificial Intelligence, it is important to have years worth of sales data. Years meaning three or more years of sales data because AI technology is reliant on accurate, detailed data sets.
The more data a company has, the more accurate the AI prediction will be. So as a golden rule of thumb, unless your company has three years of good clean sales data, AI forecasting is not the forecasting method for you.
AI forecasting is also an anchoring forecasting tool meaning that it should be used in conjunction to other forecasting techniques.There are some ethical challenges that come with AI forecasting, such as employee privacy which is why some companies may be reluctant to use it.
Scenario Writing Sales Forecasting
Scenario writing forecast is one of those forecasting methods that does not require company sales data because it is a contingency forecasting method.What this means is that this forecasting takes into account internal and external factors, both expected and unexpected, to create a plan in case one of these scenarios were to occur.
Example of Scenario Writing Forecast
The COVID-19 pandemic is a perfect example of scenario writing forecasting. Companies are now going to plan ahead for a future pandemic and use COVID-19 as a benchmark. This pandemic has brought unforeseeable impacts onto companies, greatly impacting their business plan.
Pipeline Sales Forecasting
Pipeline forecasting is another forecasting method that relies on sales reps and intricate calculations. This forecasting method is heavily dependent on sales data. This is why if a company is using this forecasting method, it should invest in a good CRM software.
Since this forecasting method is heavily dependent on data, inaccurate data can skew the results of the forecast.The way pipeline forecasting works is that it takes opportunities in the sales pipeline and factors in metrics such as the value of the opportunity and the rep’s win rate to calculate the total value of the pipeline.
Example of Pipeline Forecasting
If a sales rep is known to close most of their deals within 45 days, has a close rate of 80%, and typically each deal is worth over $10,000, then the rep will have a higher likelihood of closing a deal and so all the values in his or her pipeline will be used to forecast revenues since they are likely to close.
Intuitive Sales Forecasting
Intuitive sales forecasting is a forecasting technique that relies on estimations. Typically, a sales rep will tell his or her manager the likelihood he or she is to close a deal. This forecasting method is subjective and doesn’t involve a quantitative method, which is why companies shy away from it.
There is not a way to verify a rep’s intuition aside from sophisticated and expensive AI technology. Typically, small businesses that have been around for less than a year will use this forecasting method.
Example of Intuitive Sales Forecasting
Let’s assume that there is a company that’s brand new and has little to no sales data. The company’s sales team is comprised of a single person and that person has to forecast sales for the next quarter.
Based on the rep’s intuition or gut feeling, the rep believes that his sales forecast will be around $10,000 based on his current market research and past conversion rate.
Regression Analysis Sales Forecasting
Regression analysis sales forecasting is one of the most complicated forecasting methods out there. It is the most data-intensive of all the forecasting methods. It uses data to create a regression analysis that companies can use to create an action plan for its revenue projections.
Most CRM softwares do not have the capability to perform regression analysis and unless a company has a statistician or a data analytics person on its sales team, this forecasting method can be difficult to interpret.
Length of Sales Cycle Forecasting
Length of sales cycle forecasting method relies on sales and business cycles. It uses the age of each opportunity to predict the likelihood of it closing. This forecasting method relies on quantitative data rather than a rep’s intuition, which is why this forecasting method is likely to produce realistic forecasts.
The data for this forecasting method needs to be carefully monitored, which is why reliable CRM software is needed for this forecasting method.In addition, this forecasting method, like opportunity stage forecasting, doesn’t take into account the size of the deal which can be a hindrance for some companies.
Multivariable Sales Forecasting
Multivariable sales forecasting is expensive because it uses predictive analytics in conjunction with other forecasting methods to predict sales.
This forecasting method is heavily dependent on quantitative data to determine revenues which is why it needs an extensive analytics solution and forecasting tool. Typically, this can be very expensive, which is why companies do not use this method very often even though it is one of the most accurate forecasting methods.
Example of Multivariable Sales Forecasting
This is a very basic multivariable sales forecasting example. Let’s say there is a company that has two sales reps.The first sales rep is expected to bring in $300,000 this quarter given similar economic and market conditions as the previous year and given his close rate of 30%.
The second sales rep is expected to bring it $275,000 for the quarter and she has a close rate of 30% as well. Combine those forecasts and we get a quarterly forecast of $575,000 for the company.
How to Get Started with Sales Forecasting
If you or your company is new to sales forecasting, then don’t worry, there are techniques that can be implemented to ease the forecasting process.
First, to start things off, based off the description of the various forecasting methods, determine which one is the best for your company. Remember, not all forecasting methods can be used for every company.
A sales team need to establish consistent sales processes that can be adopted by everyone on the sales team.
A company can establish consistency by coaching and monitoring the sales pipeline. A company is better off if they establish an easy to adapt structure for recording opportunities and keeping track of sales in the pipelines.
Setting personal quotas for each rep can help divide the sales goals amongst the sales reps.
Sales goals should be divided up based on a rep’s track record and territory. If a rep has a good territory for the company’s product and is known for exceeding his or her quota consistently then he or she should have a higher sales quota than other reps.
Monitor and Record
Invest in a reliable CRM software and keep track of the sales team. It is crucial to monitor deals and be in constant communication with the sales reps. The reason behind this is because this way any issues that occur can be caught early on and you can ensure that your sales data is clean and reliable.
Sales Forecasting Summation
This article has a lot of information and it can get overwhelming for someone outside or new to the enterprise selling world.
Some key things to remember are:
- Do not depend on a single forecasting method for accurate revenue prediction
- Do not invest in expensive technology, especially if your company doesn’t have years of clean sales data
- Be in constant communication with your sales team and monitor your sales pipeline closely to catch mistakes early on.
- Coach your team and set guidelines to ensure consistency in the sales pipeline. Remember, the more detailed you get, the more accurate the forecast will be.
- Sales forecasting is a necessity for the success of any company. Weigh your opinions and do your research before investing in an expensive CRM software. There are plenty of budget-friendly alternatives that can be better forecasting tools than their expensive counterparts.