CleanTech WebLog

Now we really get into the “art vs science” —but don’t believe this for a minute.  Forecasting is one area where math applied does give you some real data to match against the revenue plan. 

First, put a scoring system in place so that every possible deal has an objective frame.  Match it to your sales cycle.  For example, when the first conversation is had with a potential customer, and it goes well, it’s natural to give this deal a lot of attention and a high “score”.  If you break down the things that need to happen before this opportunity actually becomes a sale, a more realistic picture emerges.   OK, the prospect is interested.  Good, let’s assign that interest level one point.  When they have seen a demo, or some other proof source, and they remain interested, let’s give them another point.  When they have indicated that they have budget that can be used for our product, another. And so on.   As positive “sell” indicators take place, points are assigned.  By the same token, points are lost if certain things happen.  As an example, let’s say your sponsor gets fired.  Or a competitor enters the scene where none existed, or an RFP is issued.   These things usually lower the score, and may change the momentum that this particular deal has.  

Ultimately, each opportunity has a numerical score that tells sales and management how strong the opportunity is, and there is a minimum score required to advance the deal onto the forecast and through the stages of the sales cycle.   This takes a lot of the guesswork (“art”) out of the process.   It’s not a lot of work; many SFA systems support it, but it’s also very easy to set up manually. 

And it means you can control how sales time is spent—on the opportunities that have the best chance of closing!

“How can we plan for revenue?—we don’t have a track record!”  What are best practices for revenue planning for a company that is just starting out, or bringing a new product to a market?    

It is challenging to predict how much you’ll sell, but it’s data that a company really needs in order to plan all the other expenditures.  Some companies that I have worked with use the “if we get one order this month, we’ll be set” model.   Others spend too much time and rigor on a complex revenue plan before really understanding their market.  Both of these philosophies are flawed, but understandable, because it is challenging to find a consistent method for new companies to plan what their revenue will be over time.  It’s also important to distinguish revenue planning from forecasting and pipeline management.  

The revenue plan should be the goal that a company is shooting for, and realistically believes it can achieve, if all the other pieces fall into place.    

The revenue plan should start with an honest assessment of what you have to sell, the selling price and how many are likely to be sold in a given period.   Then take into account these four factors: sales expenses, the time it will take to hire salespeople and make them productive, revenue recognition, and most importantly, the length of the sales cycle.  Sales cycle time becomes critically important for companies selling to municipalities or large entities like utilities, where evaluation and approval steps are lengthy indeed.  If all these factors are considered, any company will have the basis of a revenue plan, and the ability to tweak any of these dials to accommodate changes in the model.   

This is more art than science, as any sales professional will tell you, but the act of pulling these numbers into a excel spreadsheet creates a model and structure against which you can measure performance in real time.  It’s funny how actually looking at a projection on paper creates accountability and a realistic view of what must be done.   Next time: what about forecasting?