“My forecasts are always 100% accurate. Unfortunately, my customers don’t always buy when they are supposed to.”BEST cash advance
— Anonymous Salesperson
While customers don’t always do what we expect of them, sales pipelines do behave in a logical manner — as long as you know what to look for.
If your company’s sales process involves multiple stages where salespeople interact with numerous decision makers, knowing which data to extract from your CRM system to build a forecast can be difficult — particularly when many CRM systems don’t automatically track what’s most valuable. Using these five pipeline metrics, you can significantly improve your forecast accuracy:
- Opportunity Quantity — the simplest of the four, this is a measure of how many opportunities are in the pipeline
- Opportunity Size — another easy one, this is the projected dollar (Euro, Yen, etc.) value of each opportunity and is often expressed as an “average deal size”
- Conversion Rate — assessed on a stage-by-stage basis, this measures what percentage of opportunities at one stage typically make it through to the next
- Velocity — also measured on a stage-by-stage basis, this is a measure of how much time opportunities typically spend in each stage (Note: this one is often left out by CRM systems – if so, search for a plug-in that will help you track this metric)
- Opportunity Quality — the hardest one to quantify, this is an assessment of how precisely an opportunity matches a predefined definition of a well-qualified sales lead (another one often left out by CRM systems, this can be easily added using a custom field)
Turning our attention to how each of these metrics will enter our forecast model, Opportunity Number and Opportunity Size are fairly straightforward. Multiply the former by the latter (assuming the latter is expressed as the average deal size), and you have the overall value of your pipeline if everything closes (don’t we wish!). This will act as the baseline upon which the rest of our calculations will be based.
The next metric, Conversion Rate, will be used to peel off the value of those opportunities in each stage that, based on historical analysis, will not close. If you’ve done your homework and understand the conversion rates for each stage, you can determine how much revenue you can anticipate coming out of each stage by multiplying the value of opportunities in each stage by the conversion rate for that stage and by the conversion rates for all subsequent stages. An example will help illustrate the math required here…
- Suppose we have four sales stages with the following conversion rates:
- Between stage one and two: 25%
- Between stage two and three: 35%
- Between stage three and four: 50%
- Between stage four and close: 65%
- To figure out the value of your pipeline qualified by conversion rate, you would add together the following values:
- (The value of opportunities in stage one) * .25 * .35 * .50 * .65
- (The value of opportunities in stage two) * .35 * .50 * .65
- (The value of opportunities in stage three) * .50 * .65
- (The value of opportunities in stage four) * .65
Velocity enters into our model to allow for sales cycles that are longer than the desired forecast period. If, using historical data, we know the average time-in-stage for each stage, we can calculate a forecast for a given time period by taking into account the velocity of our pipeline. Here’s how: if our above example has a one year sales cycle that’s perfectly uniform (i.e., opportunities are in each stage for three months), a quarterly forecast would simply be the value of opportunities in stage 4 multiplied by the corresponding conversion rate of .65.
The last variable to consider is Deal Quality. This is tricky because how well an opportunity adheres to our definition of a top-tier prospect is subjective. As such, deal quality is usually represented by a weighting factor applied to each opportunity. For example, “hot” opportunities (i.e., those that match our top-tier prospect definition perfectly) may have a weighting factor of 1.0 – meaning that 100% of their value is passed along to the formula we’ve developed above. Prospects that are only “warm” (i.e., those that loosely match our top-tier prospect definition) may have a weighting factor of .65 – meaning that only 65% of their value will be used in our calculations. While they introduce a little “slop” into our math, these weighting factors give your salespeople a badly needed means to qualify opportunities in their individual pipelines. This can go a long way toward neutralizing an accurate forecast’s worst enemy: sandbagging. What’s important is to have a clear top-tier prospect definition that everybody agrees upon and a clear understanding of the weighting factors associated with the deal quality labels you allow your salespeople to use (e.g., hot/warm/cold).
While this process can be refined in a variety of ways, it is an excellent starting point that you can use to develop your own forecasting model. What’s critical is to view the development of your forecasting model as a process and not an event. Conversion rates and velocities will vary with the ups and downs of the economy, the season, and the competitive landscape. As such, you will need to constantly refine your forecasting formula based on how these and other variables impact your key pipeline metrics over time. In this regard, accurate forecasting is an historical science – the more you look to the past, the better you will become at predicting the future.