Creating Meaningful Sales and Revenue Forecasts

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Exclusive Q&A

Why should you be paying more attention to sales and revenue forecasting?


For both public and privately-held companies, it’s important to give accurate guidance regarding future revenues since missing targets without warning diminishes trust among investors, management and employees and can negatively impact the stock price. One factor that has contributed to the difficulty of generating accurate forecasts is the rise of subscription and usage business models, and the corresponding shift as many organizations now recognize that revenue is more than just new business. It should also include renewals, expansion, and revenue generated through new self-service digital e-commerce channels as well as partner channels.

Organizations need to understand where they stand in relation to period-end targets and goals to be able to course-correct as necessary. Having an accurate forecast of what products and services will be sold (and when and where) helps plan, allocate and re-allocate resources to better bridge gaps. It enables better risk management and improves the organization’s ability to take advantage of opportunities by more efficiently turning forecasts into actual revenue.

Why won’t the tried-and-tested spreadsheet approach continue to work?


As business becomes multi-channel and multi-business-model, it is insufficient to rely on traditional judgement-based and spreadsheet-based revenue forecasts. Spreadsheets struggle to deal with large volumes of data, and significant model errors in spreadsheets can rise exponentially as complexity grows. And as more teams become directly involved in revenue activities (for instance, teams related to renewals and e-commerce), the growing list of participants in a spreadsheet-based forecasting process will cause issues with process manageability.

What are some of the newer, better ways of creating forecasts?


The traditional approach to sales forecasting is often referred to as “bottoms up” and depends on understanding and measuring sales milestone progress and levels of activity and interactions. But these judgement-based forecasts are prone to bias and are only useful for late-stage deals. For many organizations, committed late-stage deals can represent as little as 10% of the pipeline and don’t include renewals, deals that are not in the pipeline and self-service deals.

Newer techniques combine the narrow, “bottoms up” judgement forecasts with AI-assisted forecasts that both validate or challenge judgement while giving more accurate views of other types of opportunities and other revenue channels as important sources of revenue. By using historical data, the latest forecasting approaches are better at estimating not just the total amount, but also what products and services are projected to be being sold and when and where this will happen. This gives a more useful view to the rest of the organization in resource allocation and product distribution.

Much is written about Artificial Intelligence and Machine Learning (AI/ML). But aren’t they only as good as the quality of the CRM data on which they rely?


That has traditionally been the standard view, but there are several new initiatives that are improving the quality of the underlying data by automatically recording meetings and email, voice, and text communications in the CRM. Additional context about buying organizations include market events and news that supplement user-generated CRM data. Transparent explanations of why a number is called aids in establishing user confidence in the predictions and leads to sustained adoption and to better insights as to what process and actions are more effective in helping hit goals and objectives. In addition, more flexible applications allow AI to be applied to industry- and customer-specific questions, thus moving away from a one-size-fits-all approach.

Who else within an organization will benefit from improved forecasting processes and applications?


Sales leaders will benefit from earlier alerts of the potential for projected sales to miss target, as this will allow for corrective action. In addition, opportunities can be prioritized and “zombie” deals removed to ensure productive focus by sales teams. The office of the CRO will be able to view and understand projections for all sources of revenue—not just new business using trusted scores of new, renewal and cross- and up-sell opportunities. These more trustworthy forecasts can then be used by the rest of the organization, including the CFO’s team. Sales and operations teams will benefit from the insights provided as they fine tune sales and revenue processes and seek to understand individual revenue team members’ performance and identify skills and coaching needs.


Stephen Hurrell

VP and Research Director
Ventana Research

Stephen Hurrell is responsible for the overall research direction for the Office of Revenue at Ventana Research, including the areas of digital commerce, price and revenue management, product information management, sales enablement, revenue performance management and subscription management. His focus areas include product and CS leadership, data-driven applications in sales enablement, financial reporting and planning, and billing and monetization platforms.