What is forecasting and its examples?
The ability to predict future sales performance can make or break the success of a business. Part instinct, part science, predicting how sales of a product or service will fare in the coming week, quarter, or year allows you and your team to make decisions that impact overall business growth. Sales forecasting is the process of honing significant data to make your predictions as accurate as possible. Historically, sellers have gauged the future by looking at the data from the past.
Collective[i] uses prescriptive, AI-enabled forecasting, and we believe it’s soon how everyone will be forecasting. In this article, we’ll take a look at a few of the different sales forecasting examples that effectively collect and employ historical sales data to inform future choices. These are just some of the many ways to think about the data you have from previous consumer activity, starting points for further inquiry. More importantly, however, we’ll look at modern prescriptive forecasting examples that can give your sales team even better forecasting insights.
What are the types of forecasting?
The data that’s traditionally been relevant to forecasting in business is readily available — especially when a company already has a few years of collected sales and performance data. Sales teams learn from successes and failures, measuring what’s worked in the past. Forecasting looks at the factors that affect this to predict future revenue.
There will be shifts outside of your organization in the economy, politics, environment, weather, and moves made by other companies that will affect your sales strategies in ways you can’t control. The year 2020 illustrated this for every sales team. And while you can find patterns that might suggest future movements in global markets, the COVID-19 pandemic proved that forecasting has to enable sellers to watch the markets and adapt quickly to change.
Pipeline/opportunity stage forecasting
Break down each stage of your sales pipeline over a given period of time. By knowing what stage a customer is at on their journey from introduction to closing, you can collect data and make predictions on their future behavior — a key to forecasting in business.
This moves away from looking at specific consumer behavior and instead measures overall performance over a given period of time. Seasonal forecasting could be included in this, but also measurement year over year to identify trends in sales. This is a core element of traditional forecasting, and represents the quantitative approach.
So far we’ve been describing quantitative assessments and forecast types — very necessary parts to learning from previous sales methodologies. Intuitive forecasting is a qualitative approach that relies on the knowledge sales teams develop through relationships — things that don’t show up in a spreadsheet. It’s their job to know their customers, and they can often provide insights that don’t appear in traditional reports.
If the above forecasting methods represent how it’s been done in the past, AI-enabled forecasting represents a way forward. Leveraging emerging technologies like machine learning and neural networks, modern forecasting tools augment quantitative and qualitative approaches with networked data and automated data management to provide daily sales forecasts.
Business forecasting examples
So what are some examples of forecasting in business that demonstrate how to use collected data to make business decisions? Below are some of the ways businesses have looked at the above types and factors in sales forecasting to make informed decisions.
Macroeconomic forecasting example
Each year across the United States, forest fires, hurricanes, tornados, floods, and other major weather events have a huge effect on countless industries, and demand rapid responses. Look, for instance, at just one piece of this response: how the typical disruption in distribution models might lead to consumers changing purchasing decisions at the grocery store.
Pipeline/opportunity stage forecasting example
Qualifying conditions are a good example of how some businesses make sales forecasts in their pipeline. Historical data about what percentage of leads at a given stage in the buying process can provide insights about current leads. For example: if a client or customer makes it to the demo stage of your sales pipeline, you can determine how likely they are to close based on other customers who have already been there.
Historical forecasting example
One way a business might utilize historical data is to look for patterns in sales cycles and account for them in future projections. If, for example, five recent years of sales data suggests that clients are more likely to close at the end of a quarter, then a team might expect more revenue in March, June, September, and December. Keep in mind that this is a narrow way of setting goals, since any number of factors could completely disrupt that cycle — like a pandemic, for example.
What is forecasting in sales? It’s empowerment.
If that last example sounds like the future of sales, it’s not. Collective[i] puts powerful AI tools at your disposal today so you can increase revenue tomorrow. By combining historical data from a growing network of sales teams with macroeconomic markers and quantitative networking tools, we’ve transformed sales forecasting from a guessing game into sales’ secret weapon.
Click here to learn more about how Collective[i] can empower your sales team in the present, not the future.Explore Collective[i]