Forecasting is a whole lot more than just turning in a number.
Submitting a forecast number is the most obvious, visible part of the traditional forecasting process, but it’s really the most trivial. In fact, turning in a number is nothing more than the culmination of the administrative portion of the process.
To provide context for our announcement today about the release of the Cloud9 Intelligent Forecasting Suite, I wanted to step back and take a broader look at forecasting. Thinking about the complete forecasting cycle, one realizes that there are two fundamental components – administration and analysis. Both components are critical to sales success, but current approaches leave both inadequately addressed.
Intelligent forecasting is about addressing both of those components-bringing together operational administration of forecasting with “intelligence”, i.e. analytical insights into the forecast and pipeline. That’s what our announcement today about the release of the Cloud9 Intelligent Forecasting Suite is about.
Let’s look more closely at these two components.
Forecast administration encompasses the collection, manipulation and presentation of forecast and pipeline data. Many organizations feel pain surrounding forecasting because their CRM systems do not support the manipulation of pipeline and forecast data in a way that accurately represents how their sales organization is structured or operates. A common example is overlay forecasts, where a CRM system may operate according to a territorial roll-up or ‘hierarchy,’ but overlay teams such as account teams, product teams, or other specialized matrixed teams are left with cumbersome workarounds to produce their forecasts or pipeline reports. Other typical operational pains include managing deals that are split or shared by multiple sales reps, forecasting revenue vs bookings, and anything to do with historical forecast or pipeline data or comparisons. CRM systems are either very poorly equipped or flat out cannot maintain historical forecast or pipeline data, so sales organizations are forced to export, snapshot, or even compare paper copies of reports to have any hope of comparing forecast or pipeline data from multiple points in time.
The other component of forecasting, which is much larger and substantially more valuable, is the intelligence that goes into making the forecast meaningful. Once pipeline data has been gathered, refined and sorted, how do you decide what the forecast number is, and how do you proactively manage your sales teams and their pipeline to make that number? This is where analysis comes in. Human judgment is heavily leaned upon in all forecasting, regardless of what approach you use.
Where human judgment is central, which is to say all cases, CRM technology does little or nothing to assist the human’s judgment. Occasionally, sales teams turn to business intelligence solutions to assist, but BI solutions are far too often expensive, cumbersome, and not easily adapted by the sales team (especially when it means relying on your overstretched IT department). So spreadsheets are the default-in fact, they’re the world’s most widely used forecasting product. But Excel was never built to be a forecasting solution, leaving it woefully inadequate for the task of giving you the insight you need to forecast successfully.
But insight is what the “intelligent” part of forecasting is all about. This is where a sales manager or sales executive combines analysis of historical and current information with their experience and intuition to make an informed decision about what to forecast and how to deliver on that forecast. If you just needed one more report or one more dashboard or one more scorecard, you probably would have found a way to get that from your CRM system and spreadsheets even though it might not have come easy. But what you really need is more than that-you need a set of analyses that have meaning to your business and that leverage your data. It could be as simple as tracking historical forecast accuracy to understand which sales reps and teams forecast aggressively vs. conservatively. Or it could be more sophisticated, looking at success patterns in historical opportunities and then comparing the current opportunities in the forecast to those patterns to get an early warning about which are on track for success and which are at risk of being lost.
That’s what Cloud9 technology provides. Not only an application to handle the administration of your forecasting process, but also a tool that provides you the analytical intelligence that helps you to make the right calls about the forecast and know where to focus to make that forecast a reality.
I’ll talk more about both of these components in future posts, but in the meantime I’d encourage you to take a look at the information we’ve provided about today’s announcement. We’ve pulled together information about our product as well as what analysts and customers have to say about intelligent forecasting on our website. And let us know more about what you’re doing to improve your forecasting.
we are rolling this functionality out at Splunk - we’ve been Cloud9 customers for quite some time now and it’s the best example of delivering analytics via cloud service that I’ve seen, all implemented and driven by our sales operations team while we in IT focus on other projects! will see you at the upcoming cloud analytics show…