Quantitative forecasting examples
Forecasting in sales is tricky business and can be a daunting prospect for any executive. However, it is an important part of planning for growth. Accurate sales forecasting helps with budgeting and allocating resources, hiring the appropriate number of staff, making sure that the supply chain is functioning properly, and so much more. Understanding what is to come is vital in running a business.
For businesses looking to predict the future with any measure of accuracy, the best place to start is to examine the idea of quantitative forecasting.
What is quantitative forecasting?
Quantitative forecasting enables sellers to figure out the future by looking at the past. Most companies have collected data about their past performance, and this data can be analyzed using different methods to create predictions of future sales. Quantitative forecasting methods differ from more traditional, opinion-based qualitative methods because they are based on mathematical and statistical reasoning rather than human opinion or informed estimates.
Most quantitative methods analyze historical data to project future revenue. That can take many forms in practice: Each method looks at data through its unique analytical lense to inform sales forecasts. Time series forecasting, for example, compares data within defined periods of time, maybe a month or a quarter, to identify trends that can be used in estimating what similar periods of time will look like in the future. Then there are simpler forms of forecasting, including linear regression models, which effectively draw a line through data points plotted on a chart to provide estimates of where future data points will land.
Because these quantitative forecasting methods focus solely on data, it is paramount that the data they analyze is accurate. Garbage in, garbage out. Producing accurate data has long been a major challenge for sales teams using quantitative forecasting; it can take a lot of time to enter data into a CRM to keep it in good shape. Maybe it’s not surprising, then, that a recent study by Oracle found that 66% of sellers would rather clean a bathroom than update CRM databases.
It’s not all bad news for modern sales teams looking to make the most of their data, however. Quantitative forecasting techniques can be bolstered by modern tools enabled by artificial intelligence (AI) that reduce manual work in collecting and analyzing data. More on that later.
Why is quantitative forecasting important?
Quantitative forecasting methods are an important part of any forecasting model because they offer an objective foundation on which to build predictions about future sales. They are more detailed than qualitative forecasting methods, the most basic sales forecasting techniques, which are based on human instinct and expert opinion. Often sales teams leverage techniques from both of these types of forecasting to build stronger forecasts; predictive approaches such as quantitative, historical forecasting help structure opinion-based forecasts by balancing them with objective data.
For example, a salesperson may have several promising meetings with customers and optimistically project selling 1,000 units next week. That would be a qualitative forecast. However, past data shows that the most units this salesperson has moved in a week over the past decade is 750. Based on that, this seller’s manager knows the likelihood of that salesperson moving 1,000 units is not very high and may adjust that forecast accordingly. Quantitative forecasting is an important tool that can provide more accurate and less biased estimates to inform business decisions.
Though it has traditionally been a time-consuming process to keep clean, updated CRM data, doing so is vital to making quantitative forecasts reflect reality. In the example above, the sales manager was only able to hedge the seller’s qualitative forecast because that seller had been accurately recording sales data for many years.
What are quantitative forecasting examples?
There are many techniques in quantitative forecasting, and each has its advantages and drawbacks. While quantitative methods are based on data, there may be variables that the formulas are unable to account for. One example of quantitative forecasting that doesn’t account for many variables is the simplest method: naive forecasting. The formula is not really a formula at all — it just predicts that the sales for the next period will be the same as the last one. For example, a seller may assume that January’s sales will be the same as December’s sales. Yet at least one variable is unaccounted for: Businesses usually spend more in December because they need to use up a budget before the end of the year. January can be a slow sales month because budgets for the new year are still being finalized.
Some quantitative business forecasting examples require a lot more math and statistical knowledge, such as the historical growth rate method or the linear regression method:
- The historical growth rate method is used to calculate the rate of change based on historical data and apply that rate to the next period of sales to come up with a prediction.
- Linear regression is a way to compare variables and chart their relationship to each other to create a forecast. Some methods are more easily understood with visual graphs, such as the moving and weighted average methods. Sellers calculate the averages of datasets within multiple time periods and create a graph to see the trends and generate a sales forecast.
How does Collective[i] simplify quantitative forecasting methods?
While the advantages of quantitative forecasting are clear, a major downside is the amount of time and labor that goes into these methods — all for the sake of producing predictions that can’t ever be truly accurate because they’re limited by historical data. Complex formulas and complicated techniques can make it difficult to stay on top of creating and utilizing dynamic forecasts.
Collective[i] can do the legwork for you, using modern technology to deliver one-click forecasts (along with risk alerts, updates, and recommendations) based on bigger sets of more varied data. The artificial intelligence of our Intelligent ForecastTM uses data mining, deep learning, and a neural network to provide accurate information without human error or bias. That means our tech thinks the way the human brain does, finding connections between disparate data sets and drawing conclusions that go beyond the backward-looking quantitative forecasts of the past.
Explore Collective[i] today and see how you can improve your win rate.Explore Collective[i]