I love Brad Feld’s blog and have learned a ton from it over the years. He’s also done me a huge favor in the past, speaking at a talk I organized at my old company. But this past weekend, I found myself significantly disagreeing with Brad for probably the first time. Here’s a hurricane delayed response.
Since I started doing significant research with sales leaders for how they manage their Salesforce.com reporting, I’ve dug in deep on the topic of forecasting and pipeline management. Brad Feld hit on the topic this week, with his post “Play Offense When Predicting Revenue“. I don’t disagree with the premise of being aggressive as opposed to passive when managing your pipeline, but I do viscerally disagree with him when he writes (in the comments):
I then prefer to see three categories: (1) Forecast, (2) Upside, and (3) Pipeline. Forecast are the ones you BELIEVE are going to close. Upside are the ones that could close. Pipeline are the ones that are not going to close this quarter but that you are working on.
Then, you should revisit this list every week. It’s totally fine for something to fall out of Forecast into Upside based on new info, like the example you gave me above. Last week you thought there was a 100% it was going to close (forecast), now you no longer have certainty but there’s still a chance. And, if there’s no way it’ll close this quarter, it should go in Pipeline.
This helps focus on where you should be putting most of your short term energy (Forecast), some of your short and some of your long term energy (Upside), and the balance of your long term energy (Pipeline).
Brad gets at two elements of pipeline management: forecasting your performance and prioritizing your efforts. He also identifies a distinction that I make between how you do so, either objectively or subjectively. Let’s tackle each of those elements, one at a time.
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Forecasting and Pipeline Management
Brad’s comment advocates for one of the two basic forecasting methods I’ve observed amongst sales teams for generating their forecast number. One, outlined by Brad, creates a set of overlay “forecast stages”, typified by names like “Commit” or “Forecast” or “Upside” or “Strong Upside”.
Where this approach starts to rub me wrong is when words like “BELIEVE” are being used. You are asking an emotional human an emotional question. You are creating a quantitative number based on subjective beliefs of sales people. You are begging for clouded judgment and “happy ears” to play a significant role in your forecast number.
The counter to the approach advocated by Brad is calculating a forecast number based on rigorous objective analysis. What do I mean by objective? Using observable, calculated behaviors. How do the opportunities match against our typical sales cycle length? How many opportunities in this particular opportunity stage historically have closed? And base those stages on observed behaviors that the customer has displayed: holding a prospecting meeting, a demo for the decision-maker (as opposed to an influencer), the customer logging into a trial (as opposed to setting one up), negotiating a quote (as opposed to receiving one). And of course, derive probabilities to close based on historic performance of your organization. Does holding a prospecting meeting really mean that the prospect is 10% likely to close? Run the numbers and find out.
Don’t ask your reps what they believe will happen. They’re optimistic beings by nature (especially when under pressure to deliver). Ask your reps what the customer has done. Then execute an objective analysis. Calculate the likely value of the opportunity based on the correlation of those behaviors to closing the business.
Prioritization and Pipeline Management
Let’s move onto opportunity prioritization.
Simply calculating a forecast number isn’t enough. Far more important is determining your execution plan through the end of the month or quarter. Every organization is constrained in terms of the effort they can bring to bear to close deals. You’ve got to pick your spots.
Just as you can use objective analysis to calculate a forecast number, you can also objectively prioritize your sales reps’ efforts. Of the deals due to close soon, which opportunities most closely map to your typical sales cycle lengths? Which opportunities exhibited behaviors that correlate to wins for you in the past? Which ones have had a closed date moved out repeatedly?
Prioritize your pipeline based on how well the opportunities fit to objective measures, not solely on your beliefs. So if you are Brad and you have your “Forecasted” deals, you certainly prioritize those. But what falls next in your prioritization bin? Your “Upside” bin is probably full of flawed deals, in some fashion or another. They are probably your “coin flip” deals. Profile these opportunities and find those that fit the profile of your successful opps. And put your effort behind those as well.
Finding Middle Ground
Is there a middle ground between what Brad is portraying and what I’m advocating for? I think so. From Brad’s post, he states that much of his experience is based on companies with quarterly sales cycles. I read that as “companies that tend to sell to larger companies with long sales cycles”. Let’s call that enterprise sales for short. The longer and more complex the sale cycle, the better chance there is for variation and deviation from norms.
In those enterprise sales cases, I would recommend still creating a foundation of objective pipeline management. Still understand your customer behaviors that correlate to winning deals. Still understand your typical sales cycles. Still calculate your probabilities. But those companies that sell to enterprises, when you ask yourself what you “BELIEVE” will close, you can’t stop there. You have to ask yourself “WHY?” What substantial reason do you have for thinking that this deal is special and will break the norms? What is the cause? What are the unique circumstances that invalidate your standard model? And how can they be incorporated moving forward into our objective analysis?
For me, it’s not about what your reps believe or whether your trust your reps. It’s about creating a pipeline management process that is built on a foundation of logical, objective analysis.
Like what you’ve read here? Try our previous post, Choosing the Right Sales Performance Metrics.
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