Sales forecasting process

By understanding the complexities of the sales forecasting process, we can begin to appreciate how difficult it is to manage this data and also how powerful an impact it can have if done with the help of AI technology.

Read more below.

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Sales forecasting process

Sales forecasting is changing, in large part because customers are expecting more, deals are becoming more complex, and sales teams are trying everything they can to keep up. Markets move more quickly as technology improves, and forecasters need new strategies to keep up, to process more information at an ever-increasing rate. And, frankly, these changes can feel overwhelming.

But like dissembling a machine to understand its working parts, sales teams can study sales forecasting strategies and techniques to know what still works — and what can be improved, streamlined, or discarded altogether.

Data management is a key component of sales forecasting — it is now and will be in the future as well. What data is collected, how it’s collected, and how it’s utilized are the main elements that inform the predictive work of sales forecasting. Let’s take a look at those processes. By researching how sellers have historically conducted sales forecasting, modern sales teams can identify processes that are still valid means for collecting and assessing sales forecasting data — and where they fit into modern selling at an enterprise level.

What is a sales forecast?

Forecasters of any kind observe a series of data and use pattern logic to make predictions about how a market, product, or service will react over a designated timeline. In the simplest of terms, there’s a repeated action, a collection of information each time that action happens, and an observer identifying what variables contribute to certain actions.

These are the same steps in sales forecasting: identifying key actions, when and why they occur, and predicting future behavior.

Sales forecasting is a tool that enables companies to study how market and consumer behavior affects their overall business strategy — from production and distribution to marketing and staffing. They use sales forecasting to plan their month, quarter, year, or overall service/product life expectancy. This process goes far beyond counting chickens before they hatch because the success of the business depends on how a company plans around sales growth.

The business that fails to seriously identify and work through the steps in sales forecasting risks a blind approach to the market, and this could negatively affect cash flow, budgets, operations, expenses, or production — all critical components of a company’s health.

What are the steps involved in sales forecasting?

Let’s take a bird’s-eye view of the process and define the broad, simplified steps of forecasting. In doing so, it’s possible to really define and understand the purpose of sales forecasting.

Defining goals and expectations

Before any type of sales forecasting or model is introduced — before teams devote hours and resources toward collecting data and adapting it into a forecasting tool — business leaders need to define their goals and expectations for any forecast they prepare. This target goal will be the true north for all the forecasting process steps that follow.

Businesses may have very targeted goals for forecasts. Sometimes they may want to run a market test to understand how sales might look for a new product. A forecast may be included in a new business plan for investors to review. But when it comes to sales teams and how they utilize forecasting, one clear goal drives the whole science of forecasting: to increase revenue. Done right, according to a modern, technology-driven process, sales forecasts are able to not only project future revenue but also provide sellers with insights so they know where to focus their time and energy to drive revenue.


Once target goals have been decided, businesses can look to different models of sales forecasting that provide increasingly useful projections.

One way to understand the varying methodologies is to separate them into categories. The oldest type of opinion-based forecasting methods, also called qualitative, uses expert intuition and insight to predict market trends. Historical forecasting, also known as quantitative, relies on hard, numerical data to make predictions. Many sales teams employ both types to gain unique perspectives on the business. Below are just a few of the different tactics representing each:

Opinion Forecasting

  • Jury of executive opinion
  • Market research
  • Grassroots forecasting

Historical Forecasting

  • Opportunity stage forecasting
  • Length of sale cycle
  • Historical growth rate
  • Regression analysis

Another way of thinking about different forecasting methods is to compare predictive forecasting and prescriptive forecasting models. Predictive forecasting uses a wide variety of data inputs to predict future sales. Prescriptive forecasting takes that a step further by interpreting data to prescribe actions sellers can take to close deals and predict what the impact of those actions will be.

Both opinion-based and historical forecasting tactics are examples of predictive forecasting, whose aim is to produce increasingly accurate forecasts that, ultimately, are just predictions. Modern sales teams are turning to the kind of prescriptive forecasting made possible by Collective[i]’s networked intelligence to gain insight into the best actions to take as a result of a forecast.


What data needs to be collected? Businesses need to know the variables involved to decide what research needs to be done and what data to collect. Start with the simplest equation: customer + product = sales. And then build that equation with additional known factors: anything that affects this simple process.

What data is collected will generally depend on what forecasting method is used.

Sales forecasts have generally relied heavily on historical data to show patterns in past sales: increased quarterly sales among a certain demographic correlating with a targeted marketing campaign, for example. But as sales teams define the steps in sales forecasting in a new, faster environment, research will have to be conducted in real time and discover contributing variables quickly to have any real effect. That’s where networked intelligence has the potential to completely transform the sales forecasting process.


During the review process, businesses analyze their findings to generate an estimated forecast that will inform executive decisions, such as whether to pull the trigger on an investment opportunity, or reduce staffing because the predictions showed a demand reduction. The problem is, inconclusive results are not uncommon, but the decisions made based on these results continue to have a far-reaching impact on businesses and their employees.

What is the role of forecasting?

The review portion of forecasting — when businesses evaluate the results from different forecasting methodologies — doesn’t always provide accurate, conclusive results. And, if leaders make the wrong call, the implications are damaging on a macro and micro level. Why forecast at all?

Any one traditional tactic used to forecast sales can’t offer broad enough insights to actually move the needle. The prediction is not exactly a full picture — it’s just a prediction based on the type of data collected, the type of forecast used, and the people involved in the process. In other words, it’s too limited, even given the vast resources businesses dedicate toward the process. This has been the problem with traditional sales forecasting: The market is too immense and moves too quickly for most models to come to a definitive, accurate conclusion.

Technology enabled with artificial intelligence (AI) such as Collective[i]’s platform offers a modern approach that leverages machine learning and neural networks to automatically combine data from multiple sources — both real-time and historical — and analyze it to produce accurate, helpful insights on the best actions for sellers to take to close deals. This approach gives forecasters and business leaders a concise, comprehensive understanding of market and buyer trends.

The role of forecasting is changing for the better — if businesses leverage the right technology. Too often businesses dismiss forecasting altogether because of inconclusive or incorrect predictions.

Who should be involved in the forecasting process?

Qualitative forecasting techniques rely on people to make intelligent predictions. Qualitative forecasting is based on opinion — and the accuracy of these forecasts depends on who those people are — and what they’re basing their predictions on.

Who should be involved in qualitative forecasting? Keep in mind, this is subjective, opinion-based research being conducted, and many of the predictions garnered from qualitative forecasting will be representative of the people who produced them. Variety is key: Sales teams will want to call on experts from all sides of the business:

  • Internal and external representation
  • Industry experts
  • Market observers
  • Veteran sales team members
  • Members of a company’s leadership team

In quantitative forecasting, the only people involved are salespeople and those familiar with the type of data used and how this data is measured. That includes people within an organization who are involved with high-level analytical sales data.

During the review process, involve people who were part of the initial team that defined targeted goals for sales forecasting, those involved with the research components, data collectors, and whoever conducted specific sales forecasting methodologies. Each representative can see how the cumulative process worked and what it entailed. This is useful for future sales forecasting processes.

What are the steps to preparing a sales forecast?

How do you prepare a forecast? Sales forecasters rely on defined parameters such as timelines, data sets, types of measurements, and target audiences. These help track what influences patterns in customer behavior. To prepare a sales forecast, defining what those parameters are will help in measuring the data. Let’s take a look at what those parameters may look like and which sales forecasting techniques showcase different parameters.

Determining timeline and scope will help a company identify the type of data it can look for and the type of methodology to use — because many methodologies of forecasting, such as time series analysis forecasting, depend on specific periods of time to measure data.

Time series analysis uses historical data to identify cyclical patterns, trends, and growth rates. This type of methodology is especially useful because many of the patterns it identifies can be plugged into other data models and forecasting methodologies to gauge how a new variable might affect a market or customers’ behavior at a certain point of introduction.

For example, if time series analysis projects that sales for a particular service diminish during a certain season — for example, the time of year when most businesses are still outlining budgets and don’t have approval to buy — then sales teams can make causal assumptions about almost any marketing push during that time. They know low numbers from the marketing push don’t necessarily mean the marketing campaign failed.

There are many problems with relying on limited sets of data, and that’s especially true for sales forecasting. Too often businesses are attempting sophisticated prediction models they are uncomfortable with in the first place that only make sweeping business generalizations. To their credit, they know how valuable their data could be, but they don’t have means to measure everything involved.

What are the processes and methods of forecasting sales?

Qualitative and quantitative forecasting help prove that different types of data can be worthwhile in different ways. The 20-plus-year sales team veteran’s opinion matters, especially if that opinion matches opinions gathered from surveys of a specific demographic or industry experts. Other forecasting processes and methods will provide additional relevant data.

Opportunity stage sales forecasting, or pipeline forecasting, approaches sales forecasting with a focus on the sales cycle and how likely customers are to act at certain stages of this cycle. For example, a percentage of probability is assigned to a customer at each stage of the pipeline: prospecting, investigation, proposal, negotiation. When a salesperson is selling to an existing customer with whom they have a strong relationship, the probability of closing the deal increases. That’s helpful information if a company is trying to project sales, and it can inform other business decisions.

Causal models of forecasting help us understand how different variables affect others in a linear progression. Known as regression analysis, this is the most mathematically complex forecasting model, and it requires team members who understand statistical modeling. The purpose is to compare how certain variables affect one another and to what degree. The upside of this method is it determines precise variables that impact sales at any given moment, making it highly valuable for sales forecasting.

What is the process of forecasting in a market that’s changing rapidly? So many of the methodologies we’ve discussed take a significant amount of research-intensive months or even years to leverage.

What are the steps in the forecasting process?

When modern sales teams embrace networked intelligence, the steps in the forecasting process combine into one: Let the AI do the heavy lifting. Steps in traditional forecasting methods have historically been too slow to respond to quickly shifting buyer and economic trends, even while the data is relevant. But the biggest issue is that most forecasting techniques do not leverage enough data to accurately guide sales teams. AI-enabled prescriptive models combine the historical data businesses have with real-time data collected from other companies, market sellers, and individual buyers the team is targeting.

Sales teams need new tools to navigate the sales forecasting process today. With so much market and customer data, it’s impossible to harness that potential without advanced technology and data processing systems.

AI-enabled, prescriptive sales forecasting is the key tool for this, and Collective[i] helps business leaders and forecasters harness the value of previous sales forecasting strategies into something that supports today’s buyers and sellers. Replacing the monthly, quarterly, and even annual sales forecasts of the past, prescriptive forecasting offers daily insights and recommendations for sellers based on real-time data. In turn, that empowers sellers with a sales forecasting process free from complicated statistical models and manual data entry, allowing them to focus on what they do best: selling.

Explore Collective[i]’s product today to discover how it uses all this data to create actual positive differences for your team.

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In the meantime, explore Collective[i] and find answers to frequently asked questions.