For each stage in your buying process, based either on best-in-class metrics or your own historical results, you can look at two key metrics; the ratio between how many leads enter a stage and how many progress beyond it, and the average time in the stage for those that do progress beyond it. Note that it is critical to have leads degrade from a stage after an appropriate period of time. Without this, your calculations will be misguided as you will be basing your numbers on leads that are no longer sufficiently engaged to be accurately counted as part of that stage.
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In order to create those SALs, sales need to be provided with MQLs that they accept. With an 80% acceptance rate, this means marketing needs to create 3125 MQLs. If the Interested Inquiry to MQL ratio is 10%, that means you need 31250 Interested Inquiries in order to make that $10M number.
However, time is an important element of analysis of the marketing funnel. If it takes, on average 2 months for and inquiry to become an MQL, a SQO is only created after a few initial calls, taking on average 1 month, and the sales process takes 3 months, those 31250 Interested Inquiries will not convert into $10M in revenue for 6 months. This is obviously a very simplified example, but the importance of taking time into account cannot be underestimated. The more your model of the buyers’ buying process evolves to include multiple stages, the more insightful this analysis can be. Seeing gaps in the funnel early can give you insight into revenue shortfalls many months in advance.
It’s important to note though, that is is only a model for deals that are generated by marketing. If you have a partner channel generating deals on their own, or your field sales team generates their own opportunities, this revenue will be over and above the revenue modeled with this calculation.
1 comments:
Steve,
Do you segment leads at all based on how they come in? For example, do you have different numbers if they first came in for a whitepaper versus their first contact was an eval request? I'm curious to know if building that granularity is worth the effort? Does it more let you more accurately predict revenue to segment, and if so by enough to make it worthwhile.
Great article, thx.
-matt
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