What Salesforce Had to Learn About Forecasting After Missing Internal Sales Goals

Robin Glinton, Vice President of Data Science Applications, Salesforce

It all started when Salesforce missed internal sales goals one quarter.

This took everyone by surprise. It was caused by a lack of data transparency combined with human bias. Our sales pipeline lacked vital information that would have highlighted the risk of missing the quarter, while optimism created tunnel vision that led to the incorrect belief that the excellent results of the prior year would continue. In the end, the sales forecast was completely off. This event propelled us to build an AI-driven forecasting tool to take the emotion out of the process and ultimately establish a transparent sales culture to keep this from happening again. Here I’ll share how we took on this challenge, what we learned, and what your organization can take away from the process.

Understand the sales domain

When we decided to build our own forecasting technology, it started with domain understanding. By that, I mean understanding the sales realm, sales pipeline, teams, structures, and processes. We sat down with sales leaders, reps, and all of the constituents to dig into the sales pipeline as a framework. We asked very specific questions such as:

• Which fields on an opportunity are used by reps and what do they mean?
• How does an opportunity relate to an actual sales deal — that is, the real interactions?
• How is an opportunity created?
• What are the stages in an opportunity? What do they represent? How are they updated?

With these answers, we were armed with enough domain knowledge to understand the problem on a deeper level and, by extension, to develop a product that would meet the needs of our users.

Ask the right questions

To refine our understanding of the problem, we asked sales leaders and the sales strategy team, “When you’re faced with this problem and there’s a shortfall in your business, what questions come to mind?” Their answers guided how we shaped the forecasting tool as a product.

We learned from the feedback that if there’s a shortfall, someone in the management chain is associated with it. Who is that? Is someone further down the sales team responsible for it? Once the problem is localized to an individual within the business, the next step is discovering what is driving the shortfall. A data-driven forecasting product needs to help that individual understand how to fix the problem.

With a strong understanding of these product-shaping questions, we moved on to start designing the actual product. This meant designing a data pipeline to source and process the input data, designing models to transform that data into a forecast, and finally designing a user interface (UI) to present the model outputs as actionable intelligence to the end user.

To read the complete article, “What Salesforce Had to Learn About Forecasting After Missing Internal Sales Goals,” visit Quotable.com.