The third destination on the list of my VP of Sales survey is creating a better model at predicting revenue from the current opportunity pipeline. Based on my discussions nearly all companies I spoke with are using some type of weighted forecasts to predict the value of future revenue that will be generated from their current opportunity pipelines. Similarly, most tools provide a method of auto-generating a weighted forecast. For the sake of this discussion, we will assume that a weighted forecast is in use and the key factor in creating a better model lies in the generation and usage of the weighting factors.
Among my findings are that most companies have instituted an opportunity pipeline that is at least loosely based on a standard or modified sales process. (See my blog on accelerating opportunities for more detail on sales processes) My findings also indicate that most customers put little to no thought into the stage percentages and either utilize the defaults given during the sales process training or a set of numbers due to simplicity such as 0%, 25%, 50%, 75% and 100%. Well one of the reasons the weighted models are poor at predicting future revenues is that they are based on randomly generated numbers.
The first step in improving the model is improving the weighting factors and — if you have been using your CRM for a reasonable amount of time, the data you need to adjust your weighting factors closer to reality already exists. Most CRMs typically store and allow you to report on the history of opportunities and with a little ingenuity with custom reporting you can generate the actual rates by stage of opportunities that reach the production phase.(Note: it must be done at the individual opportunity level) Using these rates is the first step in improving your model. The table below, using a real world rate example shows the significance of this impact.
|Stage||Annual Value||Original Stage %||Predicted Revenue||Calculated Stage %||Predicted Revenue||Actual Revenue|
The change in the weighting factors in this example improved the future revenue forecast from 35% error down to a 12% error. Many other factors have significant impacts, such as how accurate are the annual value number of the opportunities, but with a little diligence and number crunching a simple change can pay immediate dividends.
Forecast accuracy takes work and attention to the details. NEHANET CRM lets you figure out what the real weighting factors are and adjust your forecast to reflect what your sales process achieves.