Forecasting, already a challenge for traditional services businesses, has grown even more complex with the rise of new services.
How can we as services leaders adapt our forecasting to account for these new revenue streams? In order to best answer that question we need to break forecasting into three categories:
Service knowns call for the forecasting exercise known as a bottom-up forecast. It’s pretty much just as it sounds: forecasting what we know, meaning anything in our system with a date and a value. Specifically, these knowns would consist of the following:
To be even more specific, service knowns equal the sum of our scheduled backlog plus actuals in a given period. Though this is nothing new, it’s worth noting that any sizable services organization would struggle to calculate its bottom-up forecast without a sophisticated professional services automation (PSA) system.
Let’s use the following as an example: Product A was sold for $100,000 and a 12-month contract. With the delivery curve, our forecast could look like this:
Now that the deal is forecasted, using a project start date we can run a forecast at any point in time and capture the project’s anticipated revenue versus just leaving it in our unscheduled backlog. While delivery curves will vary based on type of product or project, the most important thing is that you can turn static, unscheduled backlog into a dynamic forecast of upcoming revenue, taking you one step toward better overall forecasting.
In the services world, things can and do change quickly. To account for potential changes in a forecast, it’s common practice to create three different scenarios—typically “Expected,” “Best case,” and “Worst case.”
The following table shows forecast drivers and examples on how to weight them. The different scenarios help us better understand the business and what our results can be, while also giving us a view into what the forecast relies upon. For example, maybe there is a large milestone in the period that is at risk of being delayed. Knowing the impact on the business if it does push helps us better manage expectations and pinpoint what’s needed to make sure it doesn’t push.
Everything discussed in this ebook relies on data residing in CRM and PSA systems. Assuming a healthy backlog and sales pipeline, this will generate a very reliable forecast for 6-12 months.
Forecasting beyond that will require modeling pipeline and delivery data unavailable through CRM or PSA systems. This is where AI will shine. AI systems like Salesforce Einstein will be able to predict pipeline and backlog for 9-18 months out by evaluating a number of variables ranging from market trends to anticipated performance and growth.
Not only will Certinia incorporate AI for forecasting further out, but we’ll also use it to build better delivery curves since AI can evaluate a volume of elements that would normally require a team of data scientists to consider. Processing data quickly, modeling delivery, and continuing to refine those models will transform how businesses deliver and manage services in the near future.