Improved Sales Forecasting Through AI and ML

Last Updated: December 16, 2021

Sales forecasting has traditionally been known for inaccuracies and taking too long. In the new world of AI and ML, this is changing. By leveraging these emerging technologies, companies are better able to forecast their sales, says, Geoff Birnes, SVP Customer Engagement, Atrium.ai.

CRM solutions have a spotty track record in the realm of sales forecasting, and companies have battled with inaccuracies while generally blaming bad data. However, the way in which we utilize artificial intelligence (AI), machine learning (ML) and predictive analytics will change how we view forecasting. According to Salesforce, only a quarter of companies use predictive analyticsOpens a new window . Of those using it, 86% have already seen a positive return. Predictive analytics can be brought to the CRM to create more accurate sales forecastsOpens a new window  and provide actionable insights for reps, partners and managers. This is known as the “intelligent experience.” Read on to better understand how intelligent experience is a perfect fit for sales forecasting. 

Benefits of Intelligent Forecasting

Data now drives business. In order to utilize data in a meaningful way, companies need to have the right skills and tools to convert data into actionable insights. This is the core of intelligent data-driven experience. In such an experience, users aren’t spending all of their time searching for the data they need. Rather, meaningful insights are presented to system users in the context of their workflow. For example, a company using a standard CRM is able to look at their pipeline and opportunity data and see forecasted sales figures. By comparison, a company with intelligent forecasting sees all of that same data and beyond, presented in an actionable way. The intelligent forecast predictions will analyze past opportunities, successes, misses, win rates and other criteria to create a recommended forecast, which can be compared against field inputs. The biggest differentiator when it pertains to intelligent forecasting is not just the ability to improve accuracy and confidence, but also the ability to provide insights and recommended actions to improve win rates.  

The Approach 

To deliver intelligent forecasting, an organization must define the experience it wants to create across each revenue channel. For example, a multi-tier channel sales organization will require a different experience from that of a direct sales organization. Further, different data sets and models will be required to predict each revenue channel. The two most common prediction methods are propensity-based predictions for direct sales, and run rate predictions for both channel sales and aggregate forecast rollups. Propensity-based models examine individual opportunities and score them, while run rate models look at aggregate sales volumes across segments of the business (i.e. channel, geography, product, etc.). Once implemented, the insights are combined across both methods, and integrated into user workflows within the CRM. 

How To Get Started 

An overwhelming majority of machine learning projects failOpens a new window  to deliver results either because companies believe they need to gather the perfect dataset before embarking on the journey, or they believe they need to create elaborate custom models that ultimately lack transparency and actionability. The reality is, companies don’t need perfect data to gather meaningful insights, nor do they require a large data pool to start their journey. Model simplicity and transparency is also critical.  

Follow these best practices to get started.

  • Start simple with models that are highly transparent, actionable, and easy to operationalize. The experience should drive the model, not the other way around.
  • Treat the development of your intelligent forecast as a journey, where an agile approach to deliver rapid incremental improvements over time. 
  • Utilize early insights to prioritize the expansion of your data pipeline.  
  • Treat your intelligent forecast initiative as a team sport, with business, data science and IT teams working together.  
     

The intelligent forecast experience is more accessible now than ever. Companies are combining the power of data, analytics and AI to improve both their sales predictability and performance. Use it to your competitive advantage.  

Geoff Birnes
Geoff Birnes

Co-founder and SVP Customer Engagement, Atrium

As a co-founder and SVP of Customer Engagement, Geoff is responsible for Atrium’s customer outcomes. Geoff brings extensive experience in large scale business transformation programs across sales, marketing, service and middle office. Prior to Atrium, Geoff led strategic accounts for Appirio-Wipro, and has spent 20 years in the consulting space, focused on CRM and business intelligence sales and delivery. Geoff attended Penn State University where he earned a B.S. in Engineering.  
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