Audio also available on Apple Podcasts and Spotify.
What is it about revenue forecasting that can be so challenging? For people-based industries such as professional services, their project revenue is based on talent supply. And people—including all 109 billion of them who have ever lived on Earth—can be difficult to predict.
“People are highly variable,” said Justin Joseph, senior director of product strategy at Workday. “They aren’t always available. They go on vacation. And, as we’ve experienced the past few years, people leave, and there can be skills shortages. So there’s a lot of variability in how professional services companies generate project revenue.”
Beyond the variability of their employees and the impact of unprecedented trends and events, professional services firms also deal with different systems in different parts of the organization. These silos can cause data to be inconsistent and inaccurate—challenges that only get worse as an organization grows.
“The larger an organization, the more complex things get,” said Mark David, vice president of solution management at Workday. “Organizations then become more reliant on processes to manage projects and people, but that requires accurate data from disparate places.”
In this episode of the Workday Podcast, we’re 100% focused on revenue forecasting for professional services, with our guests Joseph and David. They share trends impacting firms, the pros and cons of different types of forecasting, and how firms can start to solve their challenges to better plan and forecast.
Here are a few highlights of our conversation, edited for clarity. Be sure to follow us wherever you listen to your favorite podcasts, and remember you can find our entire podcast catalog here.
“A year ago, a customer who runs a 5,000-employee professional services firm told me the one thing he needed was a good revenue forecast more than anything else right now. As you can imagine, this was especially needed with what’s happened over the last few years, which have made forecasting where your business is going even more difficult.” —Mark David
“With unexpected scenarios, you’re following the exact same processes as expected scenarios, but you have to forecast at a much faster pace and much more frequently because your assumptions are changing so rapidly, maybe hour by hour or day by day. How quickly can you pull this data together and then model it and share it out? It may sound contradictory, but they’re similar. Speed is ultimately what’s different.” —Justin Joseph