An alternative to forecasting in major-account sales environments
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.
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Tags: measurement, sales forecast
November 13th, 2008 at 12:14 am
Hi, Justin. Just thought I would leave a slightly contrary opinion!
It is true that sales forecasting is more difficult in the kinds of environments you describe. However, it is not impossible.
First, you are absolutely right that applying statistical techniques to opinions destroys information rather than creating it! The way most sales organizations and CRM software approach the issue is foolhardy.
Yet, if you know how, you can definitely help salespeople produce a more statistically valid forecast. The key is to rely not on their independent opinions or feelings, but to help them develop a list of relevant observable facts about their deals. Such general things as “extent of the prospect’s pain,” and “our relationship with the decision maker” can be resolved into specific, observable, concrete characteristics or customer behaviors.
If you:
1) help salespeople develop a list of assessment questions scoring those characteristics on a likert scale,
2) ask them to score and track the outcomes of a statistically significant number of deals over time, and
3) statistically analyze the data,
you learn some amazing things. One of the things you learn is which of the questions are statistically relevant (often not what salespeople think it would be). Another is that there is a “tipping-point” … a narrow range of scores below which there is almost no chance of closing, and above which there is almost a 100% chance. This is what everyone is looking for around sales forecast accuracy. After re-designing the questions based on the results of the statistical analysis, you end up with a forecast indicator that is usually better than 90% accurate. You need a savvy statistician at your side to pull this off, but it has worked every time we’ve done it.
Of course, in the kind of environment you describe (large complex deals with a slow cycle) it might take years to get enough data to follow the steps above. However, it is still possible to make an improvement. After having done these across a range of industries, there are patterns in the data which point to the kinds of customer characteristics and behaviors that are likely to be root causes for wins and losses. Designing a qualification/forecast assessment around root causes that have been valid in the past is likely to provide a better forecast assessment than relying on the company’s salespeople’s gut feelings, and will most likely provide something closer to the statistical reliability your clients want.
If you or your clients would like to try out this approach, I’ll be happy to provide assistance. I’ll be releasing a book about this kind of project as part of the Sales Process Improvement Series, probably in February.
Michael Webb
http://www.salesperformance.com
November 18th, 2008 at 12:50 am
Michael, I’m not suggesting that improvement isn’t impossible, rather that statistics has little to offer management in this environment.
As a colleague pointed-out to me offline, any statistic must be quoted in two parts, the central tendency and the range. In major-account environments you are likely to find that the range is so large as to render the central-tendancy measurement meaningless — particulary if the time-horizon is ‘years’ as you suggest.
Justin
December 26th, 2008 at 8:22 am
Good evening,
What a great debate between the two people who I consider leaders in their respective frameworks. Michael coming from the Six-Sigma camp and Justin from the TOC camp.
Can I throw a spanner in the works (and geographically take an entirely different position!)?
If we map the decision making process correctly, (based on interviews with clients, coaches, technical people and the sales and marketing team) then we can move away from the terrible CRM software fix which is the bain of all our lives and into a sales stage and gate process which can be controlled by utilising either or both frameworks.
I have recently been using parts of the TRIZ framework and have been getting good results using function analysis and Trimming both with external clients and internally with sales of research projects. The great thing about using function analysis is that sales teams take to it like a proverbial duck to water and are quite happy to share the concept with their clients as they feel in control and are offering extra value.
best regards
Andrew
December 26th, 2008 at 10:55 am
Andrew
I think this is more a new thread than it is a ‘spanner in the works’!
Can you provide a concrete example of the method you reference it and how you’ve applied it?
Justin