May 14, 2021

Written by

Collective[i] Team

  • Posted in
  • Sales Forecasting
  • Sales Forecasting Methods

What are the types of sales forecasting?

Successful companies in today’s market are students of buyer behavior — and they drive their business initiatives based on what service or product they think customers will want or need next. They have to continuously innovate to stay ahead of the curve, making the data collected through sales forecasting methods all the more relevant.

But are businesses still relying on the same types of sales forecasting techniques they always have to develop new business strategies? There’s an adage here involving insanity, doing the same thing, and expecting different results.

What are the types of forecasting techniques businesses rely on today to learn about their customer base? Are these methods still providing the types of information that allow businesses to keep their competitive edge?

We’ll take a look at some of the types of sales forecasting that have been used historically and how they might now be adapted to glean new insights for businesses before their competitors do — and what the future of AI-enabled forecasting looks like.

What are the different types of forecasting techniques?

There are three high-level types of forecasting techniques: opinion, historical, and prescriptive. Sales teams rely on forecasting to make predictions about future sales cycles and to gauge market response; it’s the mirror ball of sales — some parts science, some parts experience.

Traditionally, sales forecasting has meant keeping close tabs on past data regarding buying patterns, internal and external factors that affect sales, and likelihoods of consumer behavior based on previous sales cycle trends. Let’s break some of those data-based methods down:

Market analysis: Measuring levels of product usage, collecting data regarding economic outlook and growth projections, technological innovations, demographic and regional trends — market analysis takes a look at the top contributing factors affecting the market’s overall health. Sales teams use the data collected through market analysis to make predictions about their specific placing in the surrounding market.

Opportunity stage forecasting: Sales teams rely on this type of forecasting to break down a complicated sales cycle into data-centric predictors: at any given point during a stage of the sales process, how likely or unlikely — near or far — is a customer from completing the sales process and buying the product? Use the car dealership example: what percentage of customers will be likely to enter the office of a sales rep if they commit to a test drive? This indicator lets sales members make informed predictions about their process.

Regression analysis: Using dependent and independent variables (like sales affected by cold-calls), this statistical method of sales forecasting measures how internal and external factors affect your measured sales over a given period of time, or sales cycle. By studying the relationship between two or more factors over time, sales teams can make predictions how similar factors might contribute to influence change.

Are these types of forecasting techniques still relevant? The short answer is, yes. But businesses restrict themselves when they rely too much on historical data and forget to factor in the more subjective, qualitative factors in sales.

Which is better, opinion or data-based sales forecasting?

Both of these types of forecasting play an important role in predicting future sales. Opinion forecasts, also known as qualitative, are experience based. Data-based, or quantitative forecasting, relies on specific data (historic patterns, trends, measurables). Here are some examples of qualitative techniques:

Grassroots forecasting: When sellers have uniquely strong ties to their customer base, the information they garner can give sales teams a leg up against contrary market data. Understand grassroots forecasting in sales by looking at the world of politics; when under-represented, inspired campaigns end up making large headway in larger political theaters, it can come as a surprise to the powers that be. The same is true for sellers who chase deals based on instinct.

Delphi method: Expert opinion from reliable, credible sources provides truly valuable insight for sales teams. When that expert opinion is unaffected by the need to fit in with the group? It’s a best-case-scenario. The Delphi Method relies on collected expert data through anonymous or private means and allows experts to change their responses as they see other responses each round.

Market research: Consumer surveys, questionnaires, and buying trends reflect the unpredictable nature that runs counter to some quantitative market data. Sales teams need to take into account the microcosmic data that might reflect macrocosmic movements in the market — and individual market research could reflect unexpected changes.

Prescriptive forecasting

To even debate whether qualitative or quantitative forecasting is more effective misses the point of how businesses actually approach sales forecasting. Almost all businesses combine qualitative and quantitative information to create historical forecasts. While these traditional forecasting methods do give sales teams invaluable data about consumer behavior, they fall short in providing a comprehensive understanding of how the market is changing in real time.

Instead, sellers need tools that reflect better data from the different types of forecasting techniques. and Collective[i] provides the AI-enabled technology that does just that.

Collective[i] uses a neural network that absorbs data from quantitative and qualitative forecasting methodologies from a growing group of sales teams. Paired with real-time information about the consumers you sell to, we can show a comprehensive reflection of the market’s current positioning.

It’s the combination of machine learning paired with the immeasurable amounts of data available to sales teams that, until now, has remained unharnessed. Collective[i] synthesizes this data so sales teams can take pragmatic steps to grow and outperform competition.

Learn more about Collective[i] here, and catch a glimpse of the new wave of intelligent sales forecasting.

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