The sales forecast is the clearest indicator of miscommunication in the C-Suite.
CFOs build their economic models on it and COOs hire, buy and equip based on it. Yet the sales forecast is one of the least understood numbers in the corporate world.
Every year most companies begin with a revenue projection. Most of the major decisions facing the organization will be made based on this number. The number of employees, raw materials purchased and capital equipment expenditures are formulated based on the anticipated revenue.
Every year, that number is wrong.
There is a valid reason why this happens - there must be since it's a ongoing occurrence.
Until now, the problem has been the complexity of trying to estimate what revenue will come in, when it will come in, who the customers will actually be and which products or services they will buy. Thanks to sales analytics, that is changing.
Why This Happens
To appreciate the cure, understand the disease. Let's say Little Company has a $3 Million revenue forecast. And, to keep things as simple as possible, let's say their revenue will come from two sales, a $1M and a $2M sale. The $1M customer will be buying red units and the $2M customer will be buying blue ones.
Simple! The CFO builds a budget on $3M in anticipated revenues. The COO readies production (people, materials, machinery, etc.) for one-third red units and two-thirds blue.
What if the $1M customer decides to test the product first and places an order for only $200,000? And, what if the $2M customer finds they actually need $2.2M in products? Despite the up-tick with the larger order, the total is still only $2.4M. And, operations has ordered materials for too many red and too few blue units.
So we ask the salespeople and the CSO for "worst case - most likely - best case" scenarios. Then we will know the range of possibilities. "We might sell as few as or as many as -" becomes the marching order for the COO and the CFO. That's not what they want or need.
OOPS! There's another variable: no sale. What if either or both customers decide to buy from the competition or decide to buy next year?
It just got a little more complicated.
This is one of the factors that has prompted the need for sales analytics. The tools available today are phenomenal because they not only project probability, they also allow us to run what-if scenarios.
The Solution
Have everyone operate on the analytical forecast, not the sales objective. Either use the sophisticated process (recommended) or the quick-and-dirty.
Quick and Dirty
Develop three outcomes: 1) worst case, 2) most likely and 3) best case. Assume that worst case is 90% - 100% likely and build your plans on it. Have a Plan B contingency in case the most likely scenario happens. This usually means ordering in smaller quantities, down-staffing and other tactics. Recalculate at least monthly. Make quarterly comparisons of planned vs. actual.
Sophisticated
Sales analytic tools allow you to make more accurate and detailed forecasts. You can update it regularly, even daily, allowing the CFO and COO can make better, more informed decisions as the sales situation changes. All it takes is a few clicks of the mouse and a few numerical adjustments.
- You know those end-of-the-month rushes to get some sales in to meet quota? You can avoid most of them.
- And that, "We missed it this month but we'll make it up next month, boss, trust me!" conversation? Now you can say, "show me" - and they can.
- Also the, "Hey I sold it but the factory couldn't deliver," drops to near zero as well.
The cure stems from using the simple - to - complex sales analytics resources. Some of the best, like Oracle's Crystal Ball, are off-the-shelf and only require you build a model in Excel. Other proprietary tools have to be built from the ground up but the payback is usually faster than our clients thought it would be.
Check them out. There are too many cool tools for Chief Sales Officers for any sales organization to be holding up a wet finger to see where business will come from.
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