July 10, 2021

Written by

Collective[i] Team

  • Posted in
  • Sales Forecasting
  • Sales Forecasting Process

What are the steps involved in sales forecasting

Making predictions about the future sounds like magic. However, while we still have yet to see real magic take over the sales forecasting industry, many businesses stick closely to preset methods and techniques for predicting the future outcome of sales.

As forecasters look toward their next forecast, it’s important that they consider the right steps of forecasting for creating accurate estimations about upcoming sales cycles. So, what are the steps involved in forecasting?

That’s not an easy question to answer simply, but there are a number of steps that successful forecasters employ to achieve remarkably accurate forecasts. In this blog, we’ll seek to answer this question: What are the steps in a forecasting system? Let’s take a look at three critical steps to take when preparing your next sales forecast process.

1. Know the goals

Before we dive into some steps for accurately measuring future sales, professionals tasked with sales forecasting for their company should know that every business is unique. What one business uses to estimate its own forecasts may not fit the intricacies of another.

First, identify forecasting goals by assessing the business’s needs. An older business with a long history of sales data may employ different measurement methodologies than a startup with only a few years under its belt. Companies that sell a variety of products and services may use still another set of measurement methodologies — the more there is to track, the more siloed the approach to each product or product line may become.

A salesperson who supports a specific product or service may already have goals identified. Great! Let’s move on to the next step.

2. Clearly define sales cycles

Once forecast goals are set, it’s time to define the unique sales cycles associated with products and/or services. A typical sales cycle for a service may be different from that of a product, and one product may experience a different sales cycle from another. As a brand carefully interacts with prospective clients, it’s important to capture data about the average sales cycle from start to finish. Use the data to answer:

  • How long does a standard cycle take to close?
  • How long are lost opportunities still in the hopper before they are set aside or considered dead?
  • What are the stages of a sales cycle, and how long do clients and customers spend in each one?

Answers to those questions are critical to understanding how to create sales forecasts that consider a brand’s unique circumstance.

3. Determine forecasting methods

Depending on a business’s structure, history, and available resources, the techniques used to capture and manage data, run it through an algorithm or equation, and produce valuable insights into future sales can vary drastically. What are the methods of forecasting that are most used? There are generally three main types of sales forecasting, which sales teams use in concert to construct their forecasts:

  • Qualitative sales forecasting measures the human aspect behind sales and industry analysis. Whether the methods employed seek expert opinion, such as with the Delphi method, or consider the gut feelings of sales reps across an organization, the results of qualitative forecasting methods often fall short without objective data analysis.

  • Quantitative sales forecasting sticks to objective data and science to gather estimations for future sales cycles. Relying on consistent historical data, quantitative forecast methods can produce objective results but fail to see the larger picture, leaving unpredictable factors out of the equation.

  • AI-enabled sales forecasting stretches across both qualitative and quantitative forecasting methods, producing predictions with objective data that are then compared against the human elements of selling, such as market trends, industry demand, and expert opinions. Artificial intelligence uses machine learning to acquire, manage, and analyze data automatically. Then it connects that data to a neural network of industry competitors to produce real-time forecasts and daily sales-tactic recommendations for hitting benchmark sales figures, connecting better with prospects, and learning how the ever-changing world affects a business’s trajectory.

When salespeople ask, “What are the steps in a forecasting system?”, they’re really looking for help to determine the best options available for accurately forecasting future sales outcomes. Today, smart machine learning technology enables any business to build the best sales forecasting system for its unique needs.

At Collective[i], we believe artificial intelligence is the future of sales forecasting, and we’ve seen it work magic for modern sales teams. Click here to see it for yourself.

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