Machine Learning for Your Sales Pipeline Forecasting

On average, more than half of companies miss their monthly revenue forecasts by more than 10 percent. Less than one in seven are within the ideal range of 5 percent. The same Forrester report, Revenue Operations And Intelligence Delivers Predictable Growth (August 2021), also revealed that about one-third of companies indicated ineffective technology is a cause of forecast variability. What’s the impact of these mishaps? Missing a forecast can lead to budget restrictions, damage a company’s reputation, and cause turnover in the sales organization.

Sales pipeline forecasting is a leading indicator of future bookings and revenues. It empowers Sales, Marketing, and Finance teams to understand how much pipeline currently exists, the timing of open sales deals, and how well-positioned the organization is to hit its future financial targets. It also helps teams understand where gaps and risks exist to better manage investments and marketing planning.

However, sales pipeline forecasting falls short of its promise. Many companies struggle with the accuracy and objectivity of their forecasting. These challenges point to limitations around what data is used, as well as the lack of flexibility and business context included in technology solutions. 

Company growth is tied directly to an organization’s ability to forecast sales pipeline with speed and accuracy. So, it’s time to move toward a model that uses all relevant data and can be easily customized to match an organization’s needs. These requirements demonstrate the need for machine learning (ML) in pipeline forecasting.

While the benefits of pipeline forecasting are many, so are the challenges that organizations face. The four most common issues include:

1. Long and complex sales cycles, especially in the B2B enterprise space, which make it hard to predict booking probabilities and timelines;

2. Human-driven processes and nuanced customer journeys, both of which can be challenging to capture and account for in forecasting; 

3. Highly varied data quality due to the multitude of human touch points across systems; and 

4. Disconnected systems and data silos, which impede the ability to conduct analysis on all available data.

While some organizations lack a pipeline forecasting process due to limited data, resources, or awareness, most companies use a CRM platform or commercial solution to deliver their forecasting. 

With CRM-based applications, pipeline forecasting is often configured on the vendor’s platform and delivers predictions based on data captured in the CRM system. As a result, the pipeline forecast is constructed from information entered by sales reps and represents a sum of open deals. The downfall to this approach is that organizations don’t have the ability to leverage all of their valuable data in this analysis and are limited to what the CRM holds.

Commercial solutions tend to provide pipeline forecasting by applying analytics or data science models to an organization’s data. The issue with third-party tools is straightforward: It’s extremely difficult for a vendor to build a one-size-fits-all model or to customize a solution based on each organization’s specific business context. Flexibility is crucial if you want to take a more granular approach to pipeline forecasting. 

What organizations require is machine learning. Specifically, an end-to-end machine learning application that is fully customizable, uses all relevant organizational data, produces near real-time pipeline predictions (as required by business cadence), and is accessible to all teams to ensure organizational alignment.  

With machine learning powering your pipeline forecasting, it’s possible to deliver an accurate picture of your current pipeline position, predictions around the timing of open sales deals, and a forecast for the quantity of new deals you expect to open in each sales geography.