Forecasting and pipeline management don’t get nearly the attention they deserve and that doesn’t make sense. Of all the parts of CRM, the forecast is one of the few things many companies still leave to manual systems, i.e. spreadsheets. Even sales compensation has a higher place in heaven as companies like Xactly have blazed a trail away from spreadsheets to a system with a database and analytics, with excellent results. You’d think that sales people would be willing to invest as much in the forecast as they do in counting their commissions.
Part of the challenge with forecasting and pipeline management is that some professionals might resent conventional forecasting systems for the same reasons they like compensation systems. Confused? You shouldn’t be. Both types of system reduce uncertainty to certainty as much as possible. But while that’s a good thing when you are counting existing money (your commissions), it’s a problem when figuring out the future because the future is anything but certain.
That’s why last week’s Cloud 9 Analytics user meeting was so important. At their third annual user conference, CEO Jim Burleigh, talked about the importance of understanding the probabilities when forecasting. It’s no coincidence that Cloud 9 now boasts a forecasting user interface that uses probabilities but also acts like a sales manager.
If you’ve spent any part of your career in sales then you know there are deals and there are DEALS. Some deals are like racehorses, they practically sprint from first call to closure while others plod along and maybe even stop. That’s an extreme situation and it’s easy to spot the real winner. But consider two deals at a 90 percent completion stage. They might look the same numerically but each took a different path to that 90 percent mark. One might have taken twice as long, one might not have enough money budgeted, one may be run by a C-level officer on the customer side the other might be managed by a director.
These differences in the history of the deal add up and a seasoned sales pro knows they are important. But conventional pipeline and forecasting tools (e.g. spreadsheets) make no use of history, which might help explain why only nine percent of organizations we’ve surveyed have a 0.9 correlation between the forecast and reality. The rest? Foregtaboutit. When it comes to forecasting these deals, the sales pro might favor one over the other for reasons that add up to gut instinct. So, it’s no surprise that the pros create three flavors of forecast — the best case, worst case and the most probable.
The genius of Cloud 9 today is that they’ve found a way to take the best of what analytics can do to track history and spot trends and combined it with a forecasting user interface that enables a professional to apply common sense to arrive at best, worst and most probable scenarios. Some people call it gut instinct and I suppose that’s as good a term as any, but really, it’s not gut — it’s applied intelligence and experience that just happen to be hard to put into words. At any rate, the new forecasting UI is straightforward and looks easy to use and it will remind professionals of their beloved spreadsheets, but with a lot more intelligence behind it.
Getting sales people to put aside the pure spreadsheet approach and go with something with more rigor behind it may still be a challenge. But Cloud 9 has demonstrated that it both understands the challenge in all its dimensions and that it can turn its knowledge into very serviceable product. Like the compensation managers before them, Cloud 9 has replaced the spreadsheet with something that makes more sense, is easier to use and should result in better results all around.
I have been studying sales forecasting and forecasting tools a lot recently and I have come to the conclusion that we need better tools as well as better ways of using them.
There is a lot that can be said about forecasting, its current state and how to improve it and I don’t want to leave anything out but I will try to be brief. First off, how we forecast says a lot about our views on economics. Given that most of us are not economists, our views of economy are most likely derived from what we see and hear on a daily basis, much like our view of the weather.
For over thirty years our view of economics has been increasingly colored by the ascendant views of the New- or Neo-Classical school of economics. To over simplify, it is a view that goes back to Adam Smith, of supply and demand and a belief that economics is a hard science governed by equations as rigorous as Newtonian physics — wishful thinking I’m afraid.
The most germane idea for our purposes is Say’s Law. Say was a French economist, very much in the Classical school who said that “production creates its own demand” and from that we derive the famous supply side economics of the last thirty years. Supply side economics corresponded nicely with another phenomenon in our world, the introduction of the CPU chip in 1968 and the cascade of new products that ensued over the coming forty years, roughly the high-tech era.
Increasing CPU power followed Gordon Moore’s famous dictum, now Moore’s Law, of increasing CPU power and decreasing cost, and it created a special circumstance that governed supply and demand for technology goods. Moore’s Law made Say’s Law work like a charm. A corollary to Say is that all markets clear, i.e. all supply is eventually absorbed at some price — but maybe not a premium price.
Moore’s Law ensured that a fresh supply of technology goods that superseded the earlier generation would arrive and drive demand thus ensuring Say’s Law would operate as advertised. But if Say’s Law requires something like Moore’s Law to operate smoothly, then it must be said that Say’s Law is a special case, not an iron clad law of economics.
What’s that got to do with sales forecasting? Quite a bit. In the special case of selling into a market with undiminished demand, sales forecasting need not be a lot more complicated than determining where we are in the sales cycle. If we’re ninety percent through the cycle we ask for the order and there is a reasonable chance that we will get the business — no guarantee, but a reasonable chance.
It hardly matters that our ninety percent is not really a probability derived statistically but really just a milestone in a process. In an expanding market there are enough deals percolating that reasonably diligent effort will result in on-quota performance. But on-quota performance is not what it once was and forecasting is in disrepute in many places.
According to Jim Dickey and Barry Trailer at CSO Insights, only about fifty-eight percent of sales people manage to make or exceed quota. Also, according to my research less than ten percent of sales forecasts have an accuracy of ninety percent; the rest aren’t worth the time and effort it takes to compile them.
What’s happening to sales forecasting is not surprising. With Moore’s Law slowing down and with so many formerly new market niches filled with products, we are transitioning from an era of expanding markets to one of zero-sum conditions. In a zero-sum situation, if you are going to win business you need to do it by displacing another product. If you are a customer in a displacement game it is always easy to do nothing and wait for a better offer and continue using an existing product that might not have all the bells and whistles you want but fills the need nevertheless.
A zero-sum economic environment has a lot of uncertainty in it. You might use the words uncertainty and risk interchangeably but they are not the same. Risk is something that is unknown but knowable. If a deal forecast is at risk a sales representative — frequently at the urging of the sales manager — can ask more questions, get more data, and piece together an answer. There are many issues in sales that are simply unknowable or mostly unknowable, for example, the details of the bid your competition makes.
When uncertainty — not just risk — enters the picture, our forecasting paradigm that relies on milestones in the sales process becomes useless. We need better tools if we are to forecast in the face of uncertainty and those tools exist but few of us have taken them up yet. For example, prudent managers might start with the territory planning process. How much white space is in the territory? What percentage of that white space is likely to churn this year? What is the overall economic forecast? Given our market share what is the probable share of that white space that we can capture? Is that enough to sustain quota for one or more people? How should we incentivize them?
Sales forecasting will always be an inexact science but we can do better than we are currently. We could persist in basing our forecasting ideas on Say’s Law but inevitably it is a race to the bottom, to pure competition on price. The airlines do that but none of them makes any money.