Sales forecasting is a crucial part of the sales goal and decision-making process. It can give insight into a company’s sales performance and is critical for sales projections because it helps companies make informed business decisions.
Sales forecasting is also an integral part of a company’s sales process because it can highlight a company’s internal weaknesses and help sales managers mitigate these internal issues, optimizing a company’s sales forecasting model.
Keep in mind, the forecasting process is an internal method. There is no equation or algorithm that can accurately predict external factors impacting a company or its sales team because external factors are always changing and are often unpredictable.
As a general rule of thumb, the type of forecasting technique a company chooses should be the best use of the available sales data. Typically, companies rely on historical sales data to outline future sales trends.
If a sales forecasting method such as regression analysis, intuitive forecasting, pipeline forecasting, opportunity stage forecasting, or multivariable analysis forecasting, can be applied with acceptable accuracy, then that is the method that should be used.
Traditionally, most established companies that have been around for more than a year use historical sales data provided by the sales team to predict current and future sales forecasts. Historical forecasting is an essential part of the forecasting process because it can outline a company’s sales cycle
A simple way to predict how much a company is going to sell during a given period is to match and reference the historical data from a previous corresponding period of time and use it to forecast sales.
This forecasting technique, also known as historical forecasting, can be a good way to understand sales history and conduct trend analysis for a given period of time.
Companies can use this forecasting tool daily, quarterly, or yearly to analyze market trends and establish a baseline for current and future sales. They can determine sales seasonality and the length of the average sales cycle for their company.
For example, if last year during this time a company made $1,000,000 in sales, then it can be estimated that based on the forecasting model, that this year at the same time a company will roughly bring in the same amount.
This sales forecasting technique gives a somewhat accurate estimate of what current and future sales will look like based on past sales data. The reason why it can only be considered somewhat of an accurate estimate is that seasonal abnormalities that occur in previous years can hinder the forecasting estimates.
Is Historical Sales Forecasting Right For You?
Historical sales forecasting as previously mentioned is used when companies have at least a year’s worth of historical data to import for reference. If companies are less than a year old, they must resort to other quantitative forecasting methods such as intuitive sales forecasting.
There are instances however where a company that has been around for more than a year might not use historical forecasting as a sales forecasting method. For example, this method would not be very reliable if the company is continually launching new products.
The sales team will have a hard time developing a sales forecasting process for a company that has new products that don’t have historical data that can be referenced, making it difficult to predict sales for the product.
There are many factors that can come into play when determining whether historical forecasting is right for your company and since each company is different, there isn’t a universal formula that can be applied to everyone.
Having a good sales team that is lead by a sales manager who understands the company’s sales process and sales cycles is an integral part of conducting a proper historical forecast.
A company’s sales team that has the ability to determine product seasonality and how it related to the average sales cycle can be useful tools for a sales manager to analyze historical data.
The best way to use historical data is to use it in conjunction with other forecasting methods like pipeline forecasting, length of sales cycle forecasting, opportunity stage forecasting, and or roll-up forecasting.
Benefits of Historical Sales Forecasting
While historical data can have its own set of obstacles, it can be an efficient forecasting method for companies that already have the data.
Historical forecasting is a quick way of predicting sales which is why companies prefer to use this method. It is accurate data that has been analyzed and sets a precedence for current sales. This forecasting method can also help companies determine conversion rates for customers and the potential value of each deal.
Historical sales forecasting is an ideal method for stagnant markets. Markets that don’t change and remain relatively constant can use this sales forecasting method because past sales can be an accurate benchmark for future sales and improve forecasting accuracy for a company.
Challenges of Historical Forecasting
While some companies use historical sales forecasting and find it an accurate way to predict sales, for other companies, this forecasting method can be a hindrance resulting in forecasting errors.
For one, this method doesn’t take into account market anomalies and season changes which is why this forecasting method wouldn’t be ideal for scenario planning. The COVID-19 pandemic is a perfect example of how historical forecasting can be inaccurate.
If companies had used past data to predict current sales, they would have overestimated their sales revenue because the COVID pandemic has reduced revenues for many companies.
In addition, using data from the pandemic to predict future sales is also inaccurate because when the pandemic is over, company revenue will likely be significantly higher than it is currently.
Historical forecasting also doesn’t take into account the economic theory of supply and demand. Buyer demand changes over time so just because buyers are purchasing less one year doesn’t mean that this will be the case for the following years. This can be an issue for sales forecasting because there is no accurate way of predicting consumer demand.
If companies use historical sales forecasting, they should also use other forecasting methods to confirm the accuracy of the data provided by this sales forecasting method. Companies that have a large sales budget can implement other expensive forecasting methods such as AI forecasting, Regression analysis forecasting or multivariable forecasting to verify the historical sales data.
Components of Historical Sales Forecasting Method
To use historical forecasting, companies need past data to set a benchmark for the number of sales that can be predicted for a given quarter. Companies that have a year’s worth of data can use it to establish historical growth.
Historical growth is the percentage of growth revenue experiences from one year to the next. The average can be taken and used as an estimate to determine revenue growth for future sales.
[(Sales from Year 2 – Sales from Year 1)/ Sales from Year 1 x 100] = Historical Growth Percentage
For example, if a company made $3,000,000 in year one and $5,000,000 in year 2, then the historical growth for this company would be approximately 67% from year 1.
Example of Historical Sales Forecasting
If your company made a total of $1,000,000 in sales last December and there were no economic or environmental anomalies factoring into the sales, then it can be predicted that this year in December, the company will also generate at least $1,000,000 in sales.
Companies can also use this benchmark and historical growth together to predict any expected increases in sales from last year. For example, based on annual historical revenue, it can be estimated that the annual growth is 5%, so the sales forecast for this year will be $1,050,000.
The phrase “history tends to repeat itself” is a philosophical idea that can be applied to sales. Patterns that emerge from past company data can help predict future outcomes.
Companies that use historical forecasting need to make sure that the data is accurate or else any forecasting analysis made with the tainted data will be meaningless.
Whether your company decides to use historical forecasting or not, it is a good idea to keep a record of past company sales data in order to make more informed future sales decisions.
Financial planning for product sales is a key part of a company’s supply chain. Using historical data as an analytic tool for data tracking can help companies assess their current sales pipeline based on historical data points gathered from past product sales. Historical sales forecasting is a great forecasting tool because it can help companies develop accurate forecasting techniques to solidify their business model and thus increase cash flow.