Sales Forecasting Best Practices

When it comes to Sales forecasting, traditional CRM solutions have not been able to address the industry’s needs. Forecasting is complex, and most companies struggle with resource constraints, accuracy, and actionability. Because of this, organizations are now beginning to combine process, analytics and Artificial Intelligence to hone accuracy, to make decisions that will improve Sales, and to make employees more productive.

AI-enabled forecasting is still very new, and magical thinking is abounds, which is exacerbated by exuberant claims across the enterprise software space. While figuring out how to leverage AI in forecasting can be a challenge, the right approach will unfairly tilt the playing field to your advantage.

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How AI Is Transforming Forecasting

For many years, forecasting has relied on manual calculations in spreadsheets that aggregate historical direct and channel sales inputs, thus making or breaking a quarter. Unfortunately, these approaches are subjective, not actionable, and not real-time. Now a data-driven, predictive approach to forecasting is possible, using transparent statistical methods applied at scale, and in the real-time flow of your business.

1. Sales Pipeline Prediction

These are predictions that score pipelines provide a “bottoms up” view of the business. This method allows companies to drill down into opportunity-level data to better understand uncertainty in demand. Companies can analyze each opportunity individually and use logistic regression models to score a specific opportunity’s probability of success. By combining those predictions with the expected size of each deal, the system can project an expected Sales forecast across product groups, managers, regions, and the overall Sales organization.

Often, multiple propensity models are used for different types of product and customer segments, as those segments may behave differently. The explanatory process of building these models will inform a better business understanding of the factors that influence both win rate and pipeline velocity. And the resulting insights are surfaced to users in the context of their opportunities.

2. Run Rate Prediction

Aggregate forecast predictions look at your data through a different lens. Rather than scoring individual opportunities, these predictions look at aggregate Sales volumes across segments of the business, including channel, geography, product and account segments. This approach allows for predictions on pipeline not yet generated or visible to the business and allows a more comprehensive look-ahead to the end of the quarter or future quarters.  These types of predictions are highly useful for all business models, including direct sales, channel sales, and run-rate businesses. This method can also be used to predict customer and partner consumption against contractual volume commits, with action frameworks to address gaps.

Forecasting should be treated for what it really is: a science. Without scientific logic applied in near real-time to your data, you run the risk of being overly optimistic or pessimistic, with inefficient calorie burn along the way. AI-enabled forecasting will drive discipline in the care and feeding of underlying data and will enable organizations to make real-time fact-based decisions to improve their businesses. We need to spend less time defining and negotiating what our forecast should be, and more time focused on what actions we can take to drive lift in across our business.

Adopting a new approach to forecasting may seem daunting or even unnecessary to Sales organizations that are rooted in their manual ways. However, when done right, an AI-enabled forecast has the ability to guide organizations with insights and recommendations to improve both conversion and customer retention.

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