One of the abiding urban myths that misinforms sales pipeline management is the idea that sales people need at least 3x pipeline coverage in order to achieve their quota. Where this “golden number” came from, nobody seems to know, but it’s a fair bet that it dates back beyond the Neolithic.
Another widespread urban myth is the idea that whenever you have a bigger sales pipeline, you end up selling more. It’s the sort of misconception that leads marketing teams to drive to create an ever-larger number of MQLs without any regard for how many of them ever actually result in any revenue.
The simple fact is that there is no one-size-fits-all answer to the question of what the optimum coverage ratio for any specific sales pipeline is…
In my experience, one of the things that distinguishes top sales performers from the rest is that they have too much respect for their own time to waste it pursuing opportunities that they have no realistic chance of winning - while their less-discriminating colleagues cling onto every available opportunity until long past its sell-by date, often because they misguidedly fear that qualifying out will make their pipeline look smaller.
This is not always just the sale person’s fault - sales managers are often to blame as well, by focusing more on the size of the sales person’s pipeline than the quality of the opportunities it contains.
If we’re to address this issue, we need to turn to rational methodology, and not urban myth. We need to instrument our sales pipelines so that we can identify the root causes of success and failure, and the behaviours that result in winning outcomes.
There’s a growing body of evidence to suggest that pipelines that are too large to manage are just as problematic as pipelines that don’t contain enough opportunity in the first place. For every sales environment, there is an optimal range of pipeline coverage - it just isn’t the same in every situation.
Here are a few of the factors that most commonly impact pipeline conversion efficiency:
- Type of business (new customer vs. cross-sell vs. up-sell vs. renewal)
- Type of purchase (required vs. considered)
- Complexity of selling/buying process
- Number of stakeholders involved in decision
- Deal value (and approval level required)
- Experience of sales person
- Adoption of sales process
Some of these factors impact the efficiency and predictability of the sales pipeline as a whole - others reflect predictable differences between the performances of different product offerings or even individual sales people.
Applying a single, untested (and often unmeasured) universal pipeline coverage target makes no sense at all. And with the emergence of sales analytics into the mainstream, there’s no excuse for not knowing what the real optimum target ought to be across a range of different sales scenarios.
The team at InsightSquared (the leading sales analytics platform for salesforce.com users) have done a great job of debunking the myth in this excellent article.
Their research shows that companies that emphasise quantity over quality in their sales pipelines tend to have much lower quality leads within their sales pipelines - and far lower conversion ratios. They also found that big deals (2x or more above average) tend to have lower likelihoods of closing and longer sales cycles.
Analytics can also show which sales people are more efficient than others in converting initial interest into sales action. Studying and comparing the shape of individual sales people’s funnels can highlight where some are more effective than others - and suggest ways in which targeted coaching can improve performance.
My own experience of applying sales analytics has convincingly proven that better qualified opportunities (using rational, consistent qualification criteria) have a measurably higher conversion rate and shorter sales cycles.
In fact, it’s pretty clear that the old 3x sales pipeline coverage mindset really ought to be long past its retirement date. There’s simply no excuse for not adopting a smarter, situational and evidence based approach.