Qualitative and quantitative forecasting methods
Businesses are defined by the critical decisions their leadership makes. That leadership has to be informed with accurate predictions of future revenue to make smart decisions. The strongest businesses seem to be able to gauge the market’s behavior before it happens.
Qualitative and quantitative forecasting methods have traditionally been used to achieve this. Yet, while they are valuable, they haven’t always been reliable. Let’s take a closer look at these types of forecasting — and the modern forecasting methods that are transforming the way businesses make decisions about the future.
What are the three types of forecasting?
Most forecasting studies can be categorized in two main buckets that are used in tandem to achieve the best results: quantitative and qualitative forecasting methods. The oldest form is qualitative forecasting, which is opinion-based. Quantitative methods, which employ historical data to create predictive forecasts, represent the next evolutionary step. There’s also a newer, third bucket representing the future of sales forecasting: prescriptive forecasting.
The third type of forecasting combines elements of both opinion-based and historical forecasting and builds upon them to analyze larger sets of better data and provide prescriptive forecasts that recommend the next best steps for sellers. It requires one of two things: either an expensive expert team of forecasters conducting large-scale studies to amalgamate the data or technology enabled by artificial intelligence (AI) that can pull vast amounts of data from different sources to make more accurate predictions than traditional forecasting models can.
What are the qualitative and quantitative methods of forecasting?
Each bucket — qualitative and quantitative — has several methods of forecasting reliant on their particular data sets. There are multiple methods of analyzing these data sets, and a few are listed below.
For starters, what is qualitative demand forecasting? Qualitative demand forecasting is an approach to predicting future sales using the opinions and instincts of sellers and other experts.
Also known as a grassroots forecast, a salesforce forecast is a qualitative method that relies on the people interacting with the customers directly — typically salespeople. They spend time with customers, and their insights into customers’ needs and wants inform predictions with some accuracy.
The Delphi method looks to industry experts for insights, garnering their opinions and predictions from their responses to several rounds of questionnaires designed to help the participating experts reach a consensus about the future.
Especially helpful when a company introduces a new product or service, market research collects direct customer feedback from reviews, surveys, and questionnaires to get insights into what customers believe they prefer.
Though based on subjective data, qualitative forecasting is a scientific approach: The methodology of collecting the data is intelligent, offers valuable predictions, and can be repeated in different settings.
Speaking of data, what are quantitative forecasting methods? Quantitative forecasting methods rely on data — traditionally historical sales data — and predictive algorithms to project future revenue based on past sales.
Historical forecasting relies on the collection of historical sales data. Typically, the longer a company has kept track of sales data, the more accurate its predictions are. Take, for example, a company that has been collecting sales data for decades; with a broad data set, forecasts can adjust for influences such as seasonality or economic recessions and take into account their impact on sales.
Other quantitative forecasts may collect data based on customer behavior: Opportunity stage forecasting can examine a sales pipeline and calculate how likely customers will be to buy based on where they are in the pipeline.
Which type of forecasting approach, qualitative or quantitative, is better?
Each forecasting method, whether quantitative or qualitative, provides some insight into future sales but is limited by the purview of its data. Qualitative forecasting methods provide some insight into some buyers’ intent to purchase in the future but can’t account for every factor at play when it comes time for those buyers to purchase again — or not. Likewise, quantitative forecasting methods have traditionally focused on the past to predict the future. The best historical data sets can give a very clear picture of the past, but what happened in the past is not always indicative of what will happen in the future.
That’s why a third methodology is needed — one that can blend and assign value to qualitative and quantitative data sets to identify real-time, accurate predictions that inform productive actions.
Collective[i] has developed a solution that uses AI-enabled technology and data sets from varied sources to provide real-time market insights, changing the game for forecasters and the business leaders who rely on them. Our networked intelligence represents the first true prescriptive sales forecasting tool focused on providing sellers and business leaders with everything they need to make smart decisions and drive revenue.
Explore Collective[i] today and see it for yourself.Explore Collective[i]