A useful feature of Salesforce is the ability to assign a probability for closing each opportunity by stage. Then if you pull a report on the pipeline, you can get an idea of the likely revenue to come from it. However, this loses its value entirely if you don’t assign a realistic probability to each stage.
The stages and probabilities in Salesforce are completely customizable. So, for example, you might have stages and probabilities such as:
| Prospecting |
3% |
| Qualifying |
10% |
| Needs analysis |
15% |
| Proposal delivered |
33% |
| Etc. |
The above example would mean that you expect to close about 1 in 30-35 companies that you’re approaching, but that once you have an interest you expect to close 1 in 10, and if you get to the point where they’re qualified and want a proposal — they’re a real opportunity — you expect to close 1 in 3.
I’ve seen companies assign a probability of over 50% for the “proposal delivered” stage, meaning they expect to win the majority of the opportunities that they bid on. Most companies don’t have the kind of track record to justify that.
Every company will have different percentages for each stage and, critically, these should be based on the company’s historical data, not wishful thinking. So, when creating these stages and assigning probabilities, go through the last few years’ data and see how you’re actually doing. And don’t just look at, for example, the last three years as a whole. Look at it year by year to see if you’re trending toward closing more or fewer deals, and try to determine at what stage your probabilities are changing. That could be very important.
And you probably will have very different percentages for sales to existing clients compared to new accounts, so you might want to create separate Opportunity stages for each.
Of course, if your company has a large volume of deals in its pipeline you can do this for shorter time periods than a year, and you can see if your close rate is changing by quarter. (That may be significant, or it may be a phenomenon that repeats itself every year, just as retail sales peak annually between Thanksgiving and Christmas.)
When companies look at the actual data, rather than what they kind of think is going on, they sometimes find out that their closing percentages are not nearly as good as they thought. Working with accurate probabilities can greatly improve your forecasting.
This isn’t rocket science. GIGO.
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