Average deal cycles or deal lengths are an important part of modelling revenue, and are often one of the more difficult parts to actively model within a spreadsheet.
Deal cycles are important however as they represent the time it takes for opporutnity to turn into revenue. If this period of time increases, it means that revenue can arrive later than originally planned, and if making big changes to targeting that results in big changes to deal cycles, it can work to cause revenue feasts or famines.
Deal cycles are essentially the time it takes for a deal to close as a won deal, from a defined sales stage (such as opportunity). When working with deal cycles and opportunity conversion & volumes, it's key that definitions remain consistent.
Modelling how many stage 2 opportunities will turn into revenue and when.
You would need to know:
Volume of stage 2 opportunities
Average Stage 2 - Close Conversion rate
Average time from Stage 2 - Close
These definitions can shift (especially in the case of MQL or SQL) and so need to be kept tight.
Calculating the average deal cycle is pretty simple - Take the close won date and the date at which an opportunity hit the correct stage and calculate the difference.
Then, add all and divide by the number of opportunities to find out the average.
Your average can then be used to give a fairer representation of your deal cycles across your models - You should however still pay attention to the upper and lower bounds as they can both throw out your average through being outliers, but can also be used for scenario testing to understand what would happen if deals on average trended more towards a different deal length.
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