Quiz: Your marketing team generates an MQL. It’s passed to an SDR, who does basic BANT-style qualification and decides it’s real. They create a sales opportunity in your pipeline and pass it to a seller. What number is in the opportunity’s value field at this time?
Four answers I hear frequently:
- I don’t know. C’mon Dave, that’s a detail, why would I care about that? Keep reading.
- Some semi-random proxy value, say $25K. Because, well, we’ve always done it that way, and I’m not sure why.
- Our average sales price (ASP), say $100K. For extra credit, our segment-specific ASP: SMB opportunities get valued at $25K and enterprise ones get valued at $100K.
- Zero dollars. And that’s the only way I’d ever do it.
What’s my answer? Zero dollars (and that’s the only way I’d ever do it). Before I tell you why, let’s remind ourselves why we should care about the answer to this question.
Do you ever look at:
- Pipeline coverage, as a way to determine your confidence about the future or to give investors confidence in the future?
- Pipeline conversion rates (on a regular or to-go basis) as a way of measuring pipeline quality or triangulating the forecast?
- Pipeline generation efficiency (e.g., pipe-to-spend ratio) in order to determine which programs or channels are better than others?
If the answer to any of those question is yes, you need to care about your definition of pipeline. And while many people think about stage (e.g., should that SDR-created, stage-one opportunity even be considered pipeline?), few people seem to think as much about value.
In a typical funnel , by the time you get to stage 3 or 4 of your sales process you may have weeded out half your pipeline. Now imagine it’s early in a quarter and your pipeline is loaded with stage 2 and stage 3 opportunities, all valued at $100K. You may have a big air bubble in your pipe.
You think, alas, no worries, Dave, I can handle that in other ways:
- When we say pipeline around here, we actually mean stage 4+ pipeline, so we just exclude all those opportunities.
- When we look at stage-weighted pipeline, we weight at 0% all the stage 2 and 3 opportunities, so they’re effectively ignored.
Doing this will bleed a lot of air out of the pipeline, but let’s step back for a minute. You’re telling me that you’re putting in a $100K placeholder value at opportunity creation time and then systematically ignoring it? Yes. Well, tell me again, why are you putting it in the first place?!
The answer to that question is usually:
- We want to show a big pipeline to get everyone excited.
- That’s how everybody does it.
- We want to be able to compare against companies that use placeholder values.
Before challenging those answers, let me object to the air bleeding processes mentioned above:
- Pipeline should mean pipeline. If there’s no adjective before the word pipeline, it means the sum of the value of all opportunities with a close date in the period. It’s sloppy to say, “pipeline” and then revise to, “oh, I mean current-quarter s3+ pipeline.” They’re not the same. Which one are you using when?
- Pipeline that’s ignored in analytics is usually ignored in operations. If your company defines “demo” as stage 4 (which you shouldn’t) and measures conversion rates from stage 4, I can guarantee you one thing: the stage 1-3 pipeline is a garbage dump. I have literally never met a company that does analytics from stage 3 or stage 4 where this is not true. As Drucker said, what gets measured, gets managed. And conversely. This is bad practice. All pipeline is valuable. It should all be inspected, scrubbed, and managed. That doesn’t happen when you systematically ignore part of it.
- How do I know if a given $100K opportunity has a real or placeholder value? You can’t. Maybe you have a rule that says by stage 3 all values need to be validated, but do you know if that happened? If you create opportunities with $0 value and say, “don’t enter a value unless it’s socialized with the customer,” then you’ll know. Otherwise you’ll never be able to tell the difference between a real $100K and a fake one .
- Stage weights should come from regressions, not thin air. For those regressions to work, stage definitions should come from clear rules. Then, and only then, can you say things like, “given our (consistent) definition of stage 2 opportunity, we typically see 8% of stage 2 ARR value converted in the current quarter and 9% more converted in the quarter after that.”  Arbitrarily zeroing-out certain stages due to poor pipeline discipline and despite their actual conversion rates is bad practice.
Let’s close with challenging the three answers above:
- Everybody does it. Ask your parents about Johnny and bridges. That’s not a good reason to do the wrong thing when derived from first principles.
- We want to get people excited. Good. How about we get them excited by creating a real pipeline that converts at a healthy rate , instead of giving everyone a false sense of security with an inflated big number?
- We want to be able to compare to (i.e., benchmark against) others who use placeholder values? Super. Then create a new metric called “implied pipeline” where you take all the zero-dollar opportunities and substitute an appropriate placeholder value. You can compare to Johnny without following him off the bridge.
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 While stage definitions and conversions vary widely, to make this concrete, here’s one sample funnel that I think is realistic: stage 1 = BANT, stage 2 = sales accepted with 80% conversion from prior stage, stage 3 = deep dive completed with 80% conversion, stage 4 = solution fit confirmed with 50% conversion, stage 5 = vendor of choice with 60% conversion, stage 6 = win with 80% conversion. Overall, that implies a s2-to-close rate of 16%, which is in the 10 to 25% range that I typically see.
 The hack solution to this is to use $99.999K as the placeholder — i.e., a value that people are unlikely to enter and then ignore that. Which leads again to the question of why to put fake data into the system only to carefully ignore it in reporting and analytics? (And hope that you always remember to ignore it.)
 This in turn relies on both a consistent definition of close date and a reference to which week of the quarter you’re talking about — such conversion rates vary across the week of the quarter.
 One of my CMO friends pointed out that sometimes this “excitement” takes dysfunctional forms — e.g., when sales wants to “cry poor” either to defend a weak forecast or argue for more investment, they can artificially hold oppties at zero value for an extended period (“uninflated balloons”). This, however, is easily caught when the e-staff is looking at both pipeline (dollar) coverage as well as count (i.e., opportunities/rep).