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A Guide to Revenue Forecasting Models


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A Guide to Revenue Forecasting Models

Success in dynamic markets means being able to plan ahead. In order to respond to the many challenges and opportunities which arise each day in the business world, companies have to be as agile and adaptive as possible. How companies prepare for the future, however, can differ widely and predictive methods have been revolutionized in recent years. 

In the past, choosing directions and strategies for your business depended on the intelligence, experience, and foresight of the c-suite. This meant success was based largely on the observance and luck of a few individuals–in other words, on chance.

A lot has changed since then, and the most important shifts have come as a result of the development and honing of data software technologies. An example is revenue forecasting software: a new type of analytic tool which examines and leverages vast amounts of data in order to optimize operations and maximize profits. Every action and tactic engaged in by a business entails some effect on profits, so knowing and understanding all the variables and data points affecting revenue is extremely valuable. This can be very difficult, and is one of the problems data technology solves. 

Increasingly, companies of all sizes and markets are investing in this software to improve their sales performance and boost profits. This trend is changing what it means to be successful and competitive in the market, and, as such, it is imperative that all businesses understand these tools and their potential benefits. In this article we’ll go over how revenue forecasting models are constructed from demand and sales data; we’ll examine sales individually and the ways software can utilize data to produce a sales forecast calculator, we’ll look at the various different types and techniques of data analysis used in sales forecasting, and, finally, how to forecast sales even without any historical data input. 

Demand Forecasting Methods

One of the most important things for a business to understand and track with as much accuracy as possible is the demand for its products. This is the purpose of demand forecasting software. These programs look at all the factors affecting prospects, competition, and the market itself to determine exactly how easy it will be to sell your products or services in the future.

Demand forecasting methods are varied, but many programs will look first at prospects. Your company has a target audience of potential buyers who have a need for or interest in your product/service offerings. Demand forecasting software can take data from your CRM, advertising campaigns, surveys, and many other sources and generate predictions about the behavior of this prospect base. It can determine whether your base will expand or contract, and by how much; it can define new market segments which may become interested in your business in the future; it can even determine whether prospects who convert are likely to become regular clients or not.

The activities of competitors can also be monitored and forecasted and aspects of rival product offerings and marketing tactics analyzed to anticipate their future trajectories. Relative market shares, product features, launch dates, ad coverage, and other metrics can all be tracked to ensure your marketing strategy is constructed and adjusted to be as competitive and effective as possible.

Qualities of the market itself can also be analyzed and modelled. This enables businesses to know beforehand information about effective pricing, buyer needs, market saturation, design trends, and a host of other factors. Buyers only act within the medium of the larger market, so keeping an eye on the bigger picture is vital. 

These are just a few demand forecasting examples, however, and the predictive abilities of forecasting software can easily be directed at any aspect of the demand side of a business venture. 

Sales Forecasting Methods

Sales, however, is where forecasting is most effectively applied. Aspects of a company’s sales process can be much more easily adjusted in response to the information generated by predictive software than external factors can.

Sales forecasting methods are varied but tend to focus on the sales pipeline. Predictive software can analyze both the performance of a sales team and the behavior of prospects while engaging in a deal. 

Forecasting can determine the efficacy of both your sales team as a whole and the individual representatives within it. Software can take previous sales figures and determine the efficiency of reps. Knowing how quickly and efficiently each link in the sales apparatus works enables accurate modeling of future sales trends. This way, the number and size of the deals a team will close in the future can be predicted with pinpoint accuracy. 

Like demand forecasting software, sales forecasting programs can utilize the data in your company’s CRM to analyze the behavior of prospects and clients. Each potential or returning buyer can be categorized and modelled to predict how long they will take to move through the various stages of your pipeline and how much revenue they are likely to generate for the company. This way all returning and future clients can be treated as statistics and probable sales figures can be generated this way. 

The major advantage of statistical forecasting methods for sales is, of course, determining how much income your business will realize in the future. This is important above all for budgetary purposes. Having an accurate idea of how much free capital your company will have in future quarters allows you to green-light or scrap new product ideas, ad campaigns, and other initiatives. 

Finally, sales forecasting can also reveal if certain practices are detrimental to profits, allowing them to be reworked or eliminated as need be. 

Sales Forecast Formula

Predictive software can generate models of future trends through various analytical methods. How the data is processed by a given program depends on the type of data input into it and the types of information it is coded to produce. 

Taking sales as an example, software will generally use either quantitative or qualitative forecasting methods to generate numeric or descriptive models and conclusions. The sales forecast formula used determines the kind of data produced.  

Quantitative forecasting methods will produce information in the form of numbers. Quantitative sales forecasting examples might include conversion rates for different types of prospects, win rates for individual sales reps, cycle lengths, profit margins, opex, and more. 

Qualitative forecasting will produce descriptive data. Examples of this might include whether overall sales are increasing or decreasing, if a certain sales rep is improving, whether clients are likely to remain loyal, whether or not an ad campaign will draw more prospects, and many others. 

Both these kinds of forecasting are imperative to revenue intelligence and future planning. Canopy offers a powerful suite of programs which both crunches the numbers and offers key insights to management. Its arsenal of forecasting software utilizes machine learning to generate accurate models of any statistic or metric, and its augmented revenue analysis engine is an industry leader in fiscal optimization and expense monitoring. For any kind of data and any type of business, Canopy is an indispensable tool. 

Types of Sales Forecasting

How the actual programs used to forecast revenue data work is extremely complex and varies from one piece of software to another. Nonetheless, in order to choose the right tools, it is important to be familiar with two of the most basic types of sales forecasting and revenue prediction. 

Statistical forecasting methods are employed by the majority of software and have been around for considerably longer. Statistical forecasting simply generates future trajectories based on the trends in extant historical data. If sales figures for the past five years indicate a linear trend, statistical methods will project that line into the future. Statistical forecasting programs treat all data points the same and merely extend their lines of flight forward. 

Time series methods of prediction are newer and considerably more effective at forecasting. Unlike statistical tools, time series-based software is programmed specifically not to treat data equally. These algorithms are often built on machine learning tech and can prioritize certain data over others depending on temporal factors. These factors usually include things like seasonality and recentness. Time series analysis, thus, can generate more accurate models which account for cyclical fluctuations in data trajectories and new trends which don’t align with older data.

Statistical forecasting methods can be useful for certain more stable data series, but software like Canopy which utilizes time series analysis and machine learning techniques will tend to exhibit greater precision and accuracy. 

How to Forecast Sales Without Historical Data

If your company hasn’t yet incorporated data software solutions into its business model, you may not have logs of historical data to feed into forecasting programs. While this may seem like a serious setback, there are several solutions for how to forecast sales without historical data. 

First you should consider that you know more about creating revenue than you realize. You know that for a business to be successful, sales should be increased, opex should be minimized, and prospect bases should be expanded. Qualitative forecasting can help you determine whether these and other criteria and goals are being met and help you monitor the health of your business generally.

Second, in the saturated world of modern marketing, there are almost certainly competitors in your line of business. Their data, tactics, and success can easily be analyzed to gain insight into your own trajectories and leveraged to improve demand, sales, and revenue. 

Finally, you can focus on demand. Historical data about your software niche, buyer behaviors, and the market generally can be obtained from a variety of databases, surveys, and studies online. If you’re still working on building a data archive for your sales division, you can still obtain key insights by looking at the demand side of things.


To learn more about the varieties and benefits of revenue forecasting software, visit our website and see what Canopy can do to supercharge your business, whatever business it is. 

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