Examples of forecasting in business
Successful sales teams know that making accurate short- and long-term projections about their revenue can make or break their business. Sales forecasts inform hiring, inventory, marketing, and much more — so they are of the utmost importance to companies. Yet fewer than 50% of sales leaders have high confidence in the accuracy of their sales forecasts. Understanding the different types of business forecasting methods, including forecasting example in B2B scenarios, can help sales teams decide which method will give them the results they’re looking for.
What are the three types of forecasting?
Before providing examples of forecasting in business, let’s review the three types of business forecasting methods: opinion, historical, and prescriptive.
- Opinion forecasting is the oldest approach to sales forecasting, and it relies on sellers and market experts to provide qualitative insights about market characteristics and upcoming or current prospects in order to project sales.
- Historical forecasting is one of the quickest types of business forecasting. It relies on quantitative, historical sales information to make forecasts about the future.
- Prescriptive forecasting uses technology enabled by artificial intelligence (AI) to combine quantitative and qualitative forecasting with real-time, third-party networked data to take the guesswork out of forecasting.
Business forecasting examples
Understanding what real-life examples of business forecasting look like within a sales team and having a firm grasp on the differences between the types of forecasting can help teams make more informed forecasting decisions. Below are a few forecasting examples to illustrate what each type of forecasting looks like for B2B companies.
A five-year-old SaaS company needs to make its next quarterly sales forecast. Using opinion forecasting, it distributes an internal survey to each member of its sales team asking several questions about current sellers in the pipeline, future prospects, and what they believe their sales revenue will be over the next three months. This information, combined with market insights shared by the sales leader, is used to create the upcoming quarterly sales forecast.
In this example of forecasting in business, a lot of weight is placed upon the knowledge and experiences of the sales team. Sellers are certainly experts in their field, and opinion forecasting can add a measure of accountability to the team. However, the weaknesses of opinion forecasting include the potential for bias — for example, sellers may give a conservative estimate of sales revenue so they won’t be in danger of not meeting their quarterly goals — as well as a lack of knowledge about upcoming trends in the market.
An engineering company has been in the game for more than a decade. Like many sales teams with several years of sales data, they rely heavily on historical forecasting. Each quarter, the sales team analyzes one-, two-, three-, and five-year-old data from the same time period to make predictions about the upcoming quarter.
Historical forecasting is a relatively quick and easy method (depending on the forecasting tool a team chooses — but more on that later). However, as with most uses of forecasting, there are drawbacks with this method. Historical forecasting relies on the assumption that the past and the future will be fairly similar. But external factors such as new products or economic downturns weigh heavily on the market, so the past is often an inaccurate prediction of the future.
A relative newcomer to the field, a two-year-old B2B computer components company just purchased a new AI-driven forecasting tool, Collective[i], to help make its sales forecasting simpler and more accurate. Collective[i]’s tools automate basic tasks and capture high-impact data from a growing network of sellers to provide teams with a precise and dynamic forecast every day. Created by a team of data scientists, Intelligent ForecastTM is the first automated, daily, and adaptive forecast available. By using AI, it eliminates bias, time, and human error from the process.
The AI technology uses a machine learning process that combines historical and opinion forecasting to analyze the company’s existing historical data along with real-time data and market insights from a growing external network of sellers. The result is accurate forecasts free of guesswork.
Other uses of forecasting with prescriptive AI tools include providing informed recommendations that help sales teams move the needle with a better, fuller picture of the data. This type of forecasting drives high-impact projections that enable sales teams to stop guessing and start proactively growing their revenue.
[Learn more about how Collective[i]] takes the guesswork out of business forecasting](https://collectivei.com/#get-started “Learn more about how Collective[i]] takes the guesswork out of business forecasting”).Explore Collective[i]