In a previous post, I poked fun at the practice of sales forecasting in major-account sales environments — referring to it as hocus-pocus with a dollar sign.
The essence of my argument was that, in environments where transactions are small in frequency, but large in magnitude ($’s), the traditional approach to forecasting destroys information — rather than creating it.
The traditional approach: statistics
Here’s an example.
Let’s assume that you are the manager of a project environment in a technology company. Your job is to deploy teams of technical experts to deliver large projects that have been sold by your sales department.
Your challenge is that you need to make resource-allocation decisions based upon both current and future orders.
You ask your sales department for an estimate of future sales and — after much gnashing of teeth and grinding of gears — you are provided with a forecast. This forecast advises that sales next month will be $653k; the following month: $432k; and so on.
These numbers seem strangely precise, considering that they are estimates. Furthermore, the forecast doesn’t give you any indication of how confident you should be of these estimates falling within a particular range of values.
Suspicious; you ask the head of sales how these numbers were compiled. He advises that the following method was used:
- Salespeople used a set of rules to assign a probability to each opportunity upon which they are working (these rules prompt salespeople to consider factors like number of competitors, seniority of decision-makers, and so on).
- Salespeople’s data was aggregated so as to provide a risk-adjusted estimate of future orders by calendar month.
Ask yourself, how useful is this forecast?
The forecast tells you that orders in two months’ time will total $297k. When you enquire into this number you discover that it primarily consists of a $635k order that Sales believe they have a 34% probability of winning.
What decisions can you make on the basis of this estimate? Can you set-aside 34% of the necessary resources? Should you recruit new personnel? And, in either case, can you pay for these resources using the $216k revenue that constitutes the risk-adjusted value of the $635k sale ($635k x 34%)?
Of course not.
If Sales fails to win that order in two months’ time as projected, the project will place no demand whatsoever on your resource pool, and will generate precisely $0 in revenues!
To use statistics in this environment is obviously foolish because orders are won so infrequently. The problem is compounded by assigning probabilities to opportunities based primarily upon salespeople’s (subjective) opinions.
So, if this approach is so obviously foolish, why do so many organisations adopt it?
I can think of a handful of reasons:
- Because it does make sense in environments where transactions are high in frequency and low in magnitude
- Because it appears (at first glance) to be scientific
- Because senior management (or other departments) demand it
- Because the CRM came with this capability built-in
- Because no one is aware of an alternative approach
An alternate approach: heuristics
Let me propose an alternative approach — the approach we use internally.
But first, let’s remind ourselves that, because the dataset upon which we are reporting is not statistically significant, our approach must not make use of statistics.
The alternative approach is remarkably simple.
Sales maintains two sets of assumptions for each opportunity:
- Optimistic (but not exuberant)
- Pessimistic (but not paranoid)
So, for each of the critical opportunity variables (opportunity value and commencement month) both an optimistic and a pessimistic value is recorded — and revised weekly. These values are not calculated, they are arrived at by discussion in sales meetings. In other words, a value for each variable is negotiated by the salesperson, the sales coordinator and the sales manager.
Then, each week, two scenarios are created for distribution to senior management (and other departments) — the optimistic and the pessimistic scenario. Each scenario details specific projects (and the full value of each) — as opposed to risk-adjusted aggregates. Ideally, each scenario will also detail resourcing and cashflow implications.
Each department then uses these two scenarios to plan as best they can.
What happened to precision?
At first glance the second (heuristic) approach lacks the precision of the traditional approach to forecasting (statistical).
However, upon closer examination it becomes obvious that the precision of the traditional approach is an illusion. Contrary to popular belief, the mathematical manipulation of salespeople’s opinions does not convert subjective data into objective information.
The heuristic approach acknowledges the existence of uncertainty, rather than disguising it. Accordingly, it is significantly more useful to those who intend to use the reports provided by sales to make actual business decisions.