How many leads do we need to generate to hit goals? Do we need to hire more sales reps to boost our revenue? How much revenue will this new product generate? These are all important questions that sales forecasting methods can help your company answer.
Sales forecasting is pivotal to a company’s success and helps business executives make smart decisions when it comes to setting sales goals, hiring, launching new products and budgeting for the upcoming quarter or year.
In this guide we cover best practices of sales forecasting and eight different methods, along with the pros and cons of each.
Sales forecasting is a data-informed process that predicts how much revenue a company will make in a given time period — e.g., a weekly, monthly, quarterly or yearly basis depending on the needs of the company.
Sales forecasting takes into account various factors such as historical data, economic and industry trends and a company’s existing sales pipeline. Essentially, the goal of a sales forecast is to address these questions: How much money will a company make? And when is that revenue expected to come in?
Sales forecasts can help companies make strategic plans; however, they’re not rock-solid projections. Just like weather forecasts, sales forecasts are not 100% accurate and can be under or overestimated, so it’s important to understand that they fluctuate based on markets, economic trends and company changes like layoffs or hiring.
Sales forecasts are all about helping companies strategically plan for the future by determining:
For private companies, sales forecasting helps employees and leaders gain trust and confidence in their business. On the other hand, sales forecasting helps publicly traded companies gain credibility in the market.
While there are a number of different forecasting methods, most will fall under two umbrella categories: quantitative and qualitative.
Opportunity stages forecasting is based on the chance of closing future deals or sales that you have lined up in your pipeline.
Most businesses break their sales pipeline into the following stages:
Let’s say you have $1,500 in prospects typically close 10% of them, then you have $2,000 at the proposal stage which you’d typically close ¾ of your sales for, then your total opportunity stages forecasting suggests you have a total of $1,650 in deals for that given time period.
Length of sales forecasting assesses sales based on the age of the deal.
To calculate with this method, use the total number of days it took to close any recent deals, then divide that by the total number of closed deals.
For example, let’s say your company closed six recent deals:
You would then divide the total number of days (301) by six for an average of 50 days. Given this time frame, no matter how promising a deal might seem or how many deals you have in your pipeline, you’d still forecast any deals you have to close in roughly two months.
Also referred to as time series forecasting, historical data forecasting is a method that analyzes past data to predict your business’s sales based on the same period of the previous year. This method will often assume a growth rate from the past period.
For example, if you had $60,000 in sales last September and assume a 10% growth rate, then you’d forecast $66,000 for this September.
Regression analysis is an in depth, quantitative forecasting method that requires a solid understanding of statistics and the different elements that impact your company’s sales. At the most basic level, it involves looking at the different variables that influence sales and calculates the relationships between them.
Regression analysis uses the formula Y = a + bX. To run a regression analysis, you must first:
For example, if you want to forecast sales for the upcoming year and how to hire sales reps accordingly, you could look at the relationship betweens sales calls (your X variable) and sales (your Y variable) between the past five years.
Using the regression analysis formula, your equation would be Sales = a + b(sales calls), which you’d plot on a graph accordingly using a regression analysis software. Keep in mind that the a and b in the equation will be calculated automatically by the software. The software will help you generate a line of best fit showing how closely related the relationship between sales and sales calls was over your chosen time periods. Based on this data, you can then determine if you need to hire more sales reps for the upcoming year.
Pipeline forecasting analyzes each opportunity or potential sale sitting in your company’s sales pipeline and predicts its success to close based on a variety of factors like age, deal type and deal stage. It’s a very accurate and sophisticated method but relies on heavy amounts of data and custom tools, which may not be accessible for every company.
Intuitive forecasting is a method that relies on the opinion of sales reps on how confident they are that a deal in their pipeline will close. Because sales reps are the closest to their sales prospects and the products or services they’re selling, they tend to have the best insight.
However, intuitive forecasting is also very subjective, as sales reps tend to be optimistic and could give overly generous answers. Because intuitive forecasting doesn’t rely on sales data like many of the other methods discussed above, it only works if you have candid sales reps who you can trust.
Scenario writing is a forecasting method that focuses on possible extremes based on a specific set of assumptions. With this method, forecasters will draft several different cases for deals in a pipeline and conclude the best- and worst-case scenarios.
Most scenario writing follows an eight-step process:
Multivariable forecasting is what the name suggests — it incorporates different factors from the methods above like length of a sales cycle, opportunity forecasting, sales rep input and historical data. Forecasting based on multiple variables is generally the most accurate, but it’s also the most complex and requires an advanced analytics solution, which is best implemented by large-scale organizations with the budget to do so.
As you can see, there are many methods for forecasting sales. Regardless of which method your company uses, there are always some best practices to be followed:
It’s best to look at sales forecasting as something to build upon. Always aim to take learnings from previous forecasts to refine future forecasting methods. By using more advanced forecasting processes and tools and building upon previous forecasts, companies can outperform their competitors because they’ll have a deeper understanding of what drives their business and how to shape the outcome of a sales period before that period comes to a close.
Of course, producing consistently accurate sales forecasts is not without its challenges. Some common pitfalls that you want to be aware of include inaccurate data, inefficiency and total subjectivity.
While usually unintentional, inaccuracy is one of the biggest pitfalls of sales forecasting and can lead to mistrust amongst teams and stakeholders. There are a number of reasons why data in a sales forecast might be inaccurate, such as:
Inefficiencies are especially common when working with large sales forecasts and with large teams or across departments. In these cases, a forecast will often have multiple owners, which can leave more room for errors. Additionally, if a team is not aligned on the rules of the forecast, then there can be disputes and errors in how it’s produced, which can lead to multiple revisions.
While many forecasting methods are data driven, all rely somewhat on the forecaster making good decisions on how the data is used. Because highly data-driven methods tend to be more complex and time-consuming, many companies rely on easier, more subjective methods like opportunity stages and intuitive forecasting.
Which forecasting method is best will often be determined by your company’s needs, size and budget. However, data-driven forecasting methods are typically the most accurate. Multivariable forecasting in particular is the most accurate forecasting method out there.
Most sales forecasting methods will fall under the umbrella of quantitative or qualitative. Qualitative sales forecasts are subjective and rely on either sales teams or executive teams to make projections. Quantitative methods take a data-based approach to sales forecasts and tend to be more time and resource intensive for teams.
A simple example of a quantitative method is the length of sales cycle forecast, which takes the total number of days it took to close any recent deals and divides that by the total number of closed deals.
Sales leaders are almost always responsible for sales forecasting. In most cases, the VP of sales will be the one orchestrating the forecasting report.
Investing in a customer relationship management (CRM) system is an important part of giving your sales department accurate data to work with.
Even if you’re a startup and are just getting off the ground, having a CRM in place and ensuring your sales reps know how to use it will streamline work down the road when you put together your sales forecast.
When it comes to automating sales prospecting, tools like Mailshake can help your sales teams send out significantly more emails while still keeping personalization top of mind. Mailshake can also help support your sales forecasting by providing you with accurate sales prospecting data.