Kellblog covers topics related to starting, managing, leading, and scaling enterprise software startups. My favorite topics include strategy, marketing, sales, SaaS metrics, and management. I also provide commentary on Silicon Valley, venture capital, and the business of software.
In the previous post, I introduced the idea of an inverted demand generation (demandgen) funnel which we can use to calculate a marketing demandgen budget based given a sales target, an average sales price (ASP), and a set of conversion rates along the funnel. This is a handy tool, isn’t hard to make, and will force you into the very good habit of measuring (and presumably improving) a set of conversion rates along your demand funnel.
In the previous post, as a simplifying assumption, we assumed a steady-state situation where a company had a $2M new ARR target every quarter. The steady-state assumption allowed us to ignore two very real factors that we are going to address today:
Time. There are two phase-lags along the funnel. MQLs might take a quarter to turn into SALs and SALs might take two quarters to turn into closed deals. So any MQL we generate now won’t likely become a closed deal until 3 quarters from now.
Growth. No SaaS company wants to operate at steady state; sales targets go up every year. Thus if we generate only enough MQLs to hit this-quarter’s target we will invariably come up short because those MQLs are working to support a (presumably larger) target 3 quarters in the future.
In order to solve these problems we will start with the inverted funnel model from the previous post and do three things:
Quarter-ize it. Instead of just showing one steady-state quarter (or a single year), we are going to stretch the model out across quarters.
Phase shift it. If SALs take two quarters to close and MQLs take 1 quarter to become SALS we will reflect this in the model, by saying 4Q20 deals need come from SALs generated in 2Q20 which in turn come from MQLs generated in 1Q20.
Extend it. Because of the three-quarter phase shift, the vast majority of the MQLs we’ll be generating 2020 are actually to support 2021 business, so we need to extend the model in 2021 (with a growth assumption) in order to determine how big of a business we need to support.
Here’s what the model looks like when you do this:
You can see that this model generates a varying demandgen budget based on the future sales targets and if you play with the drivers, you can see the impact of growth. At 50% new ARR growth, we need a $1.47M demandgen budget in 2020, at 0% we’d need $1.09M, and at 100% we’d need $1.85M.
Rather than walk through the phase-shifting with words, let me activate Excel’s trace-precedents feature so you can see how things flow:
With these corrections, we have transformed the inverted funnel into a pretty realistic tool for modeling MQL requirements of the company’s future growth plan.
In reality, your business may consist of multiple funnels with different assumption sets.
Partner-sourced deals are likely to have smaller deal sizes (due to margin given to the channel) but faster conversion timeframes and higher conversion rates. (Because we will learn about deals later in the cycle, hear only about the good ones, and the partner may expedite the evaluation process.)
Upsell business will almost certainly have smaller deal sizes, faster conversion timeframes, and much higher conversion rates than business to entirely new customers.
Corporate (or inside) sales is likely to have a materially different funnel from enterprise sales. Using a single funnel that averages the two might work, provided your mix isn’t changing, but it is likely to leave corporate sales starving for opportunities (since they do much smaller deals, they need many more opportunities).
How many of these funnels you need is up to you. Because the model is particularly sensitive to deal size (given a constant set of conversion rates) I would say that if a certain type of business has a very different ASP from the main business, then it likely needs its own funnel. So instead of building one funnel that averages everything across your company, you might be three — e.g.,
A new business funnel
An upsell funnel
A channel funnel
In part III of this series, we’ll discuss how to combine the idea of the inverted funnel with time-based close rates to create an even more accurate model of your demand funnel.
The spreadsheet I made for this series of posts is available here.
In my last post, I made the case that the simplest, most intuitive metric for understanding whether you have too much, too little, or just the right amount of pipeline is opportunities/salesrep, calculated for both the current-quarter and the all-quarters pipeline.
This post builds upon the prior one by examining potential (and usually inevitable) problems with pipeline distribution. If the problem uncovered by the first post was that “ARR hides weak opportunity count,” the problem uncovered by this post is that “averages hide uneven distributions.”
In reality, the pipeline is almost never evenly distributed:
Despite the salesops team’s best effort to create equal territories at the start of the year, opportunities invariably end up unevenly distributed across them.
If you view marketing as dropping leads from airplanes, the odds that those leads fall evenly over your territories is zero. In some cases, marketing can control where leads land (e.g., a local CFO event in Chicago), but in most cases they cannot.
Tenured salesreps (who have had more time to develop their territories) usually have more opportunities than junior ones.
Warm territories tend to have more opportunities than cold ones .
High-activity salesreps  tend to have more opportunities than their more average-activity counterparts.
The result is that even my favorite pipeline metric, opportunities/salesrep, can be misleading because it’s a mathematical average and a single average can be produced by very different distributions. So, much as I generally prefer tables of numbers to charts, here’s a case where we’re going to need a chart to get a look at the distribution.
Here’s an example:
Let’s say this company thinks its salesreps need 7 this-quarter and 16 all-quarters opportunities in order to be successful. The averages here, shown by the blue and orange dotted lines respectively, say they’re in great shape — the average this-quarter opportunities/salesrep is 7.1 and the average all-quarters is 16.6.
But behind that lies a terrible distribution: only 4 salesreps (reps 2, 7, 10, and 13) have more than 7 opportunities in the current quarter. The other 11 are all starving to various degrees with 5 reps having 4 or fewer opportunities.
The all-quarters pipeline is somewhat healthier. There are 8 reps above the target of 16, but nevertheless, certain reps are starving on both a this-quarter and all-quarters basis (reps 4, 11, 12, and 14) and have little chance at either short- or mid-term success.
Now that we can use this chart to highlight this problem, let’s examine the three ways to solve it.
Generate more opportunities, ideally in a super-targeted way to help the starving reps without further burying the loaded reps. Sales loves to ask for this solution. In practice, it’s hard to execute and inherently phase-lagged.
Reduce the number of reps. If reps 4, 11, and 12 have been at the company for a long time and continuously struggled to hit their numbers, we can “Lord of the Flies” them, and reassign their opportunities to some of the surviving reps. The problem here is that you’re reducing sales quota capacity — it’s a potentially good short-term fix that hurts long-term growth .
Reallocate opportunities from loaded reps to starving reps. Sales management usually loathes this “Robin Hood” approach because there are few things more difficult than taking an opportunity from a sales rep. (Think: you can pry it from my cold dead fingers.) This is a real problem because it is the best solution to the problem  — there is no way that reps 7 and 13 can actively service all their opportunities and the company is likely to be losing deals it could have won because of it .
You can download the spreadsheet for this post, here.
# # #
 The distinction here is whether the territory has been continuously and actively covered (warm) vs. either totally uncovered or partially covered by another rep who did not actively manage it (cold).
 Yes, David C., if you’re reading this while doing a demo from the back seat of your car that someone else is driving on the NJ Turnpike, you are the archtype!
 It’s also a bad solution if they are proven salesreps simply caught in a pipeline crunch, perhaps after having had a blow-out result in the prior quarter.
 Other solutions include negotiating with the reps — e.g., “if you hand off these four opportunities I’ll uplift the commissions twenty percent and you’ll split it with salesrep I assign them to — 60% of something is a lot more than 100% of zero, which is what you’ll get if you can’t put enough time into the deal.”
 Better yet, in anticipation of the inevitable opportunity distribution problem, sales management can and should leave fallow (i.e., unmapped) territories, so they can do dynamic rebalancing as opportunities are created without enduring the painful “taking” of an opportunity from a salesrep who thinks they own it.
Pipeline is a frequently scrutinized SaaS company metric because it’s one of relatively few leading indicators in a SaaS business — i.e., indicators that don’t just tell us about the past but that help inform us about the future, providing important clues to our anticipated performance this quarter, next quarter, and the one after that.
Thus, pipeline gets examined a lot. Boards and investors love to look at:
Aggregate pipeline for the year, and how it’s changing 
Expected values of the pipeline that create triangulation forecasts, such as stage-weighted expected value or forecast-category-weighted expected value.
But how much pipeline is enough?
“I’ve got too much pipeline, I wish the company would stop sending so many opportunities my way” — Things I Have Never Heard a Salesperson Say.
Some try to focus on building an annual pipeline. I think that’s misguided. Don’t focus on the long-term and hope the short-term takes care of itself; focus consistently on the short-term and long-term will automatically take care of itself. I made this somewhat “surprised that it’s seen as contrarian” argument in I’ve Got a Crazy Idea: How About We Focus on Next-Quarter’s Pipeline?
But somehow, amidst all the frenzy a very simple concept gets lost. How many opportunities can a salesperson realistically handle at one time?
Clearly, we want to avoid under-utilizing salespeople — the case when they are carrying too few opportunities. But we also want to avoid them carrying too many — opportunities will fall through the cracks, prospect voice mails will go unreturned, and presentations and demos will either be hastily assembled or the team will request extensions to deadlines .
So what’s the magic metric to inform you if you have too little, too much, or just the right amount of pipeline? Opportunities/salesrep — measured both this-quarter and for all-quarters.
What numbers define an acceptable range?
My first answer is to ask salesreps and sales managers before they know what you’re up to. “Hey Sarah, out of curiosity, how many current-quarter opportunities do you think a salesrep can actually handle?” Poll a bunch of your team and see what you get.
Next, here are some rough ranges that I’ve seen :
Enterprise reps: 6 to 8 this-quarter and 12 to 15 all-quarters opportunities
Corporate reps: 10 to 12 this-quarter and 15 to 20 all-quarters opportunities
I’ve been in meetings where the CRO says “we have enough pipeline” only to discover that they are carrying only 2.5 current-quarter opportunities per salesrep . I then ask two questions: (1) what’s your close rate and (2) what’s your average sales price (ASP)? If the CRO says 40% and $125K, I then conclude the average salesrep will win one (0.4 * 2.5 = 1), $125K deal in the quarter, about half a typical quota. I then ask: what do the salesreps carrying 2.5 current-quarter opportunities actually do all day? You told me they could carry 8 opportunities and they’re carrying about a quarter of that? Silence usually follows.
Conversely, I’ve been in meetings where the average enterprise salesrep is carrying close to 30 large, complex opportunities. I think: there’s no way the salesreps are adequately servicing all those deals. In such situations, I have had SDRs crying in my office saying a prospect they handed off to sales weeks ago called them back, furious about the poor service they were getting . I’ve had customers call me saying their salesrep canceled a live demo on five minutes’ notice via a chickenshit voicemail to their desk line after they’d assembled a room full of VIPs to see it . Bad things happen when your salesreps are carrying too many opportunities.
If you’re in this situation, hire more reps. Give deals to partners. Move deals from enterprise to corporate sales. But don’t let opportunities that cost the company between $2,000 and $8,000 to create just rot on the table. As I reminded salesreps when I was a CEO: they’re not your opportunities, they’re my opportunities — I paid for them.
Hopefully, I’ve made the case that going forward, while you should keep tracking pipeline on an ARR basis and looking at ARR conversion rates, you should add opportunity count and opportunity count / salesrep to your reports on the current-quarter and the all-quarters pipeline. It’s the easiest and most intuitive way to understand the amount of your pipeline relative to your ability to process it.
# # #
 With an eye to two rules of thumb: [a] that annual starting pipeline often approximate’s this year’s annual sales and [b] that the YoY growth rate in the size of the pipeline predicts YoY growth rate in sales.
 Pipeline coverage = pipeline / plan. So if you have 300 units of pipeline and a new ARR plan of 100 units, then you have 3.0x pipeline coverage.
 Though there’s a better way to solve this problem — rather than excluding early-stage opportunities that have been created with a placeholder value, simply create new opportunities with value of $0. That way, there’s nothing to exclude and it creates a best-practice (at most companies) that sales can’t change that $0 to a value without socializing the value with the customer first.
 The High Crime of a company slowing down its own sales cycles! Never forget the sales adage: “time kills all deals.”
 You can do a rough check on these numbers using close rates and ASPs. If your enterprise quota is $300K/quarter, your ASP $100K, and your close rate 33%, a salesrep will need 9 current-quarter opportunities to make their number.
 The anemic pipeline hidden, on an ARR basis, by (unrealistically) large deal sizes.
 And they actually first went to HR seeking advice about what to do, because they didn’t want “rat out” the offending salesrep.
 Invoking my foundational training in customer support, I listened actively, empathized, and offered to assign a new salesrep — the top rep in the company — to the account, if they’d give us one more chance. That salesrep turned a deal that the soon-to-be-former salesrep was too busy to work on, into the deal of the quarter.
“Wait, hang on. How is that pipeline distributed by quarter? By stage? By forecast category? By salesrep? You can’t just look at it as a giant lump and declare that you’re in great shape because you have 3x the F4Q coverage. That’s lazy thinking. And, by the way, you probably don’t even need 3x the F4Q target, but you sure as hell need 3x this quarter’s coverage  and better be building to start next quarter with 3x as well. You do understand that sales can starve to death and we can go out of business – the whole time with 3x pipeline coverage — if it’s all pipeline that’s 3 and 4 quarters, out?”
I’ve got a crazy idea. How about as a first step, we stop looking at annual pipeline  and start looking at this-quarter pipeline and, most importantly, next-quarter pipeline?
What people tell me when I say this: “No, no, Dave. We can’t do that. That’s myopic. You need to look further out. You can’t drive looking at the hood ornament. Plus, with a 90-day average sales cycle (ASC) there’s nothing we can do anyway about the short term. You need to think big picture.”
I then imagine the CMO talking to the head of demandgen: “Yep, it’s week 1 and we only have 2.1x pipeline coverage. But with a 90-day sales cycle, there’s nothing we can do. Looks like we’re going to hit the iceberg. At least we made our 3x coverage OKR on a rolling basis. Hey, let’s go grab a flat white.”
I loathe this attitude for several reasons:
It’s parochial. The purpose of marketing OKRs is to enable sales to hit sales OKRs. Who cares if marketing hit its pipeline OKR but sales is nevertheless flying off a cliff? Marketing just had a poorly chosen OKR.
It’s defeatist. If “when the going gets tough, the tough get a flat white” is your motto, you shouldn’t work in startup marketing.
It’s wrong. The A in ASC stands for average. Your average sales cycle. It’s not your minimum sales cycle. If your average sales cycle is 90 days  then you have lots of deals that close faster than 90 days, so instead of getting a flat white marketing should be focused on finding a bunch of those, pronto .
Here’s my crazy idea. Never look at rolling F4Q pipeline again. It doesn’t matter. What you really need to do is start every quarter with 3.0x  pipeline. After all, if you started every quarter with 3.0x pipeline coverage wouldn’t that mean you are teed up for success every quarter? Instead of focusing on the long-term and hoping the short-term works out, let’s continually focus on the short-term and know the long-term will work out.
This brings to mind Kellogg’s fourth law of startups: you have to survive short-term in order to exist long-term.
This process starts by looking at the this-quarter (aka, current-quarter) pipeline. While it’s true that in many companies marketing will have a limited ability to impact the current-quarter pipeline — especially once you’re 5-6 weeks in — you should nevertheless always be looking at current-quarter pipeline and current-quarter pipeline coverage calculated on a to-go basis. You don’t need 3x the plan number every single week; you need 3x coverage of the to-go number to get to plan. To-go pipeline coverage provides an indicator of confidence in your forecast (think “just how lucky to do we have to get”) and over time the ratio can be used as an alternative forecasting mechanism .
In the above example, we can see a few interesting patterns.
We start the quarter with high coverage, but it quickly becomes clear that’s because the pipeline has not yet been cleaned up. Because salespeople are usually “animals that think in 90-day increments” , next quarter is effectively eternity from the point of view of most salesreps, so they tend to dump troubled deals in next-quarter  regardless of whether they actually have a next-quarter natural close date.
Between weeks 1 and 3, we see $2,250K of current-quarter pipeline vaporize as part of sales’ cleanup. Note that $250K was closed – the best way for dollars to exit the pipeline! I always do my snapshot pipeline analytics in week 3 to provide enough time for sales to clean up before trying to analyze the data. (And if it’s not clean by week 3, then you have a different conversation with sales .)
Going forward, we burn off more pipeline to fall into the 2.6 to 2.8 coverage range but from weeks 5 to 9 we are generally closing and burning off pipeline  at the same rate – hence the coverage ratio is running in a stable, if somewhat tight, range.
Let’s now look at next-quarter pipeline. While I think sales needs to be focused on this-quarter pipeline and closing it, marketing needs to be primarily focused on next-quarter pipeline and generating it. Let’s look at an example:
Now we can see that next-quarter plan is $3,250K and we start this quarter with $3,500K in next-quarter pipeline or 1.1x coverage. The 1.1x is nominally scary but do recall we have 12 weeks to generate more next-quarter pipeline before we want to start next quarter with 3x coverage, or a total pipeline of $9,750K. Once you start tracking this way and build some history, you’ll know what your company’s requirements are. In my experience, 1.5x next-quarter coverage in week 3 is tight but works .
The primary point here is that given:
Your knowledge of history and your pipeline coverage requirements
Your marketing plans for the current quarter
The trends you’re seeing in the data
Normal spillover patterns
That marketing should be able to forecast next quarter’s starting pipeline coverage. So, pipeline coverage isn’t just an iceberg that marketing thinks we’ll hit or miss. It’s something can marketing can forecast. And if you can forecast it, then you adjust your plans accordingly to do something about it.
Let’s stick with our example and make a forecast for next-quarter starting pipeline 
Note that we are generating about $250K of net next-quarter pipeline per week from weeks 4 to 9.
Assume that we are continuing at steady-state the programs generating that pipeline and ergo we can assume that over the next four weeks we’ll generate another $1M.
Assume we are doing a big webinar that we think will generate another $750K in next-quarter pipeline.
Assume that 35% of the surplus this-quarter pipeline slips to next-quarter 
If you do this in a spreadsheet, you get the following. Note that in this example we are forecasting a shortfall of $93K in starting next-quarter pipeline coverage. Were we forecasting a significant gap, we might divert marketing money into demand generation in order to close the gap.
Finally, let’s close with how I think about all-quarters pipeline.
While I don’t think it’s the primary pipeline metric, I do think it’s worth tracking for several reasons:
So you can see if pipeline is evaporating or sloshing. When a $1M forecast deal is lost, it comes out of both current-quarter and all-quarters pipeline. When it slips, however, current-quarter goes down by $1M but all-quarters stays the same. By looking at current-quarter, next-quarter, and all-quarters at the same time in a compact space you can get sense for what is happening overall to your pipeline. There’s nowhere to hide when you’re looking at all-quarters pipeline.
So you can get a sense for the size of opportunities in your pipeline. Note that if you create opportunities with a placeholder value then there’s not much purpose in doing this (which is just one reason why I don’t recommend creating opportunities with a placeholder value) .
So you can get a sense of your salesreps’ capacity. The very first number I look at when a company is missing its numbers is opportunities/rep. In my experience, a typical rep can handle 8-12 current-quarter and 15-20 all-quarters opportunities . If your reps are carrying only 5 opportunities each, I don’t know how they can make their numbers. If they’re carrying 50, I think either your definition of opportunity is wrong or you need to transfer some budget from marketing to sales and hire more reps.
The spreadsheet I used in this post is available for download here.
# # #
 Assuming you’re in the first few weeks of the quarter, for now.
 Which is usually done using forward four quarters.
 And ASC follows a normal distribution.
 Typically, they are smaller deals, or deals at smaller companies, or upsells to existing customers. But they’re out there.
 Or, whatever your favorite coverage ratio is. Debating that is not the point of this post.
 Once you build up some history you can use coverage ratios to predict sales as a way of triangulating on the forecast.
 As a former board member always told me — a quote that rivals “think of salespeople as single-celled organisms driven by their comp plan” in terms of pith.
 Or sometimes, fourth-quarter which is another popular pipeline dumping ground. (As is first-quarter next year for the truly crafty.)
 That is, one about how they are going to get their shit together and manage the pipeline better, the first piece of which is getting it clean by week 3, often best accomplished by one or more pipeline scrub meetings in weeks 1 and 2.
 Burning off takes one of three forms: closed/won, lost or no-decision, or slipping to a subsequent quarter. It’s only really “burned off” from the perspective of the current-quarter in the last case.
 This depends massively on your specific business (and sales cycle length) so you really need to build up your own history.
 Technically speaking, I’m making a forecast for day-1 pipeline, not week-3 pipeline. Once you get this down you can use any patterns you want to correct it for week 3, if desired. In reality, I’d rather uplift from week 3 to get day-1 so I can keep marketing focused on generating pipeline for day-1, even though I know a lot will be burned off before I snapshot my analytics in week 3.
 Surplus in the sense that it’s leftover after we use what we need to get to plan. Such surplus pipeline goes three places: lost/no-decision, next-quarter, or some future quarter. I often assume 1/3rd goes to each as a rule of thumb.
 As a matter of principle I don’t think an opportunity should have a value associated with it until a salesrep has socialized a price point with the customer. (Think: “you do know it cost about $150K per year to subscribe to this software, right?”) Perversely, some folks create opportunities in stage 1 with a placeholder value only to later exclude stage 1 opportunities in all pipeline analytics. Doing so gets the same result analytically but is an inferior sales process in my opinion.
 Once you’re looking at opportunities/rep, you need to not stop with the average but make a histogram. An 80-opportunity world where 10 reps have 8 opportunities each is a very different world from one where 2 reps have 30 opportunities each and the other 8 have an average of 2.5.
If your sales organization is like most, you classify sales opportunities in about four categories, such as:
Commit, which are 90% likely to close
Forecast, which are 70% likely to close
Upside, which are 33% likely to close
Unlikely, which are 5% likely to close
And then, provided you have sufficient pipeline, your sales management team basically puts all of its effort into and attention on the commit and forecast deals. They’re the ones that get deal reviews. They’re the ones where the team does multiple dry runs before big demos and presentations. They’re the deals that get discussed every week on the forecast call.
The others ones? No such much. Sure, the salesreps who own them will continue to toil away. But they won’t get much, if any, management attention. You’ll probably lose 75% of them and it won’t actually matter much, provided you have enough high-probability deals to make your forecast and plan.
But, what a waste. Those opportunities probably each cost the company $2500 to $5000 to generate and many multiples of that to pursue. But they’re basically ignored by most sales management teams.
The classical solution to this problem is to tell the sales managers to focus on everything. But it doesn’t work. A smart sales manager knows the only thing that really matters is making his/her number and doing that typically involves closing almost all the committed and most of the forecast deals. So that is where their energy goes.
The better way to handle these deals is to recognize they’re more likely to be lost than won (e.g., calling them jump-balls, 50/50 balls, or face-offs, depending on your favorite sport), find the most creative non-quota-carrying manager in the sales organization (e.g., VP of salesops) and have him/her manage these low-probability, high-risk deals in the last month of the quarter using non-traditional (i.e., Crazy Ivan) tactics.
This only works if you have happen to have a VP of salesops, enablement, alliances, etc., who has the experience, passion, and creativity to pull it off, but if you do it’s a simply fantastic way to allow core sales management to focus on the core deals that will make or break the quarter while still applying attention and creativity to the lower probability deals that can drive you well over your targets.
This is not as crazy as it might sound, because those in sales ops or productivity positions typically do have prior sales management experience. Thus, this becomes a great way to keep their saw sharp and keep them close/relevant to the reality of the field in performing their regular job. What could be better than a VP of sales productivity who works on closing deals 4 months/year?
If your VP of sales ops or sales enablement doesn’t have the background or interest to do this, maybe they should. If not, and/or you are operating at bigger scale, why not promote a salesperson with management potential into jump-ball, overlay deal management as their first move into sales management?
CEO: “Wow the quarterly pipeline dropped 20% this week. What’s going on sales VP?”
Sales VP: “Well, that’s because we cleaned it up this week.”
CEO: “That sounds great, but you said that last week.”
VP of Sales: “Well, that’s because we scrubbed it then, too.”
CEO: “So shouldn’t it have been clean after last week’s cleaning? Why did it require so much more cleaning that it dropped another 20% this week.”
VP of Sales: “Well, you know it’s a big job and you can’t clean up the whole pipeline in a week.”
CEO: “Should I expect it to drop another 20% next week?”
VP of Sales: “Uh.”
CEO: “Soon you’re going to say that we don’t have enough to make our numbers.”
VP of Sales: “Well, I did mean to mention that I’ve been thinking of cutting the forecast because we just don’t have enough opportunities to work on.”
CEO: “But we started the quarter with 3.2x pipeline coverage, shouldn’t that be enough?”
VP of Sales: “Normally, yes. But the pipeline wasn’t really clean. Some of those opportunities weren’t real opportunities.” 
CEO: “What does ‘clean’ mean? When does it get clean? Once clean, how long does it stay clean.”
VP of Sales: “Well, look our view here is that we should always be scrubbing, so we’re constantly scrubbing the pipeline, always finding new things.”
What’s wrong with this conversation? A lot. This Sales VP:
Has no clear definition of a scrubbed pipeline.
Has no process for scrubbing the pipeline.
Takes no accountability for the pipeline and its quality.
In my experience, the statement “we always scrub the pipeline” means precisely one thing: “we never scrub the pipeline.”
Should that matter? Well, using some quick assumptions , the average first-line enterprise sales manager is managing pipeline that cost $50,000 to generate per rep, so if they’re managing 6-8 reps they are managing pipeline that cost the company $300,000 – $400,000. Sales managers need to manage that pipeline. The way to manage it is through periodic, disciplined scrubs .
Now some managers don’t play the “always scrubbing” card. Instead, they say “we scrub the pipeline every week on my sales forecast call.” But once understand what a pipeline scrub looks like and remember the purpose of a forecast call , you realize that it’s impossible to do both at once.
How to Properly Scrub the Pipeline
While everyone will want to take their own unique angle on how to approach this, the core of a pipeline scrub is to review all the opportunities (this quarter and out quarters) in every sales rep’s pipeline to ensure that they are classified correctly with respect to:
Close date (which determines what quarter pipeline it’s in)
Stage (along a series of well defined and verifiable stages)
Value (following specific rules about how and when to value opportunities)
These rules should be documented in a living document called something like Pipeline Management Rules (PMR) to which managers should refer during the pipeline scrub (e.g., “Jimmy, tell me what’s the rule for picking a close date in the PMR document”).
The other important thing about pipeline scrubs is timing, because pipeline scrubs will affect your sales analytics (e.g., pipeline coverage ratios, pipeline conversion rates, stage- and forecast-category weighted expected values). Ergo, I picked a few fixed weeks per quarter (weeks 3, 6, and 9) to present scrubbed pipeline and then we typically use the week 3 snapshot for most of our early-quarter pipeline analytics .
The goal of the pipeline scrub is to ensure that the entire pipeline is fairly represented with respect to those rules. By following this disciplined procedure you can ensure that your sales forecasting and analytics are not a castle built on a sand foundation, but an edifice built on bedrock.
 If you haven’t gone insane yet, this one should push you over. Wait, whose job it is to accept opportunities into the pipeline? Sales! Once an opportunity gets into what’s known as either “stage 2” or “sales accepted lead” status, sales doesn’t get to play that card. This represents a total failure to accept accountability.
 10 this-quarter and 10 out-quarter opportunities per rep * $2,500 mean cost per opportunity = $50,000.
 I am not arguing that you can’t also clean up opportunities along the way, but that needs to be a supplement to, not a substitute for, a proper pipeline scrubbing process.
 A forecast call is usually focused on the current quarter and on the opportunities that are expected to close in order to make the forecast. Thus, low-probability and out-quarter opportunities are easily overlooked.
 Implying of course that sales perform the scrubs during weeks 2, 5, and 8 so the resulted can be presented on Monday morning of weeks 3, 6, and 9.
In this post we’ll examine how we to use pipeline conversion rates as early indicators of your business performance.
I call such indicators triangulation forecasts because they help the CEO and CFO get data points, in addition to the official VP of Sales forecast, that help triangulate where the company is going to land. Here are some additional triangulation forecasts you can use.
Salesrep-level forecast (aggregate of every salesperson’s forecast)
Manager-level forecast (aggregate of the every sales manager’s forecast)
Stage-weighted expected value of the pipeline, which takes each opportunity and multiplies it by a stage- and ideally time-specific weight (e.g., week 6 stage 4 conversion rate)
Forecast-category-weighted expected value of the pipeline, which does the same thing relying on forecast category rather than stage (e.g., week 7 upside category conversion rate)
With these triangulation forecasts you can, as the old Russian proverb goes, trust but verify what the VP of sales is telling you. (A good VP of sales uses them as part of making his/her forecast as well.)
Before looking at pipeline conversion rates, let me remind you that pipeline analysis is a castle built on a quicksand foundation if your pipeline is not built up from:
A consistent, documented, enforced set of rules for how opportunities are entered into the pipeline including, e.g., stage definitions and valuation rules.
A consistent, documented, enforced process for how that pipeline is periodically scrubbed to ensure its cleanliness. 
Once you have such a pipeline, the first thing you should do is to analyze how much of it you convert each quarter.
This helps you not only determine your ideal pipeline coverage ratio (the inverse of the conversion rate, or about 4.0x in this case), but also helps you get a triangulation forecast on the current quarter. If we’re in 4Q17 and we had $25,000K in new ARR pipeline at week 3, then using our trailing seven quarter (T7Q) average conversion rate of 25%, we can forecast landing at $6,305K in new ARR.
Some folks use different conversion rates for forecasting — e.g., those in seasonal businesses with a lot of history might use the average of the last three year’s fourth-quarter conversion rate. A company that brought in a new sales VP five quarters ago might use an average conversion rate, but only from the five quarters in her era.
This technique isn’t restricted to this quarter’s pipeline. One great way to get sales focus on cleaning next quarter’s pipeline is to do the same analysis on next-quarter pipeline conversion as well.
This analysis suggests we’re teed up to do $6,818K in 1Q18, useful to know as an early indicator at week 3 of 4Q17 (i.e., mid/late October).
At most companies the $6,305K prediction for 4Q17 new ARR will be pretty accurate. However, a strange thing happens at some companies: while you end up closing around $6,300K in new ARR, a fairly large chunk of the closed deals can’t be found in the week 3 pipeline. While some sales managers view this as normal, better ones view this as a sign of potentially large problem. To understand the extent to which this is happening, you need perform this analysis:
In this example, you can see a pretty disturbing fact — while the company “converted” the week 3 ARR pipeline at the average rate, more than half of the opportunities that closed during the quarter (30 out of 56) were not present in the week 3 pipeline . Of those, 5 were created after week 3 and closed during the quarter, which is presumably good. However, 25 were pulled in from next quarter, or the quarter after that, which suggests that close dates are being sandbagged in the system.
 I am not a big believer in the some sales managers “always be scrubbing” philosophy for two reasons: “always scrubbing” all too often translates to “never scrubbing” and “always scrubbing” can also translate to “randomly scrubbing” which makes it very hard to do analytics. I believe sales should formally scrub the pipeline prior to weeks 3, 6, and 9. This gives them enough time to clean up after the end of a quarter and provides three solid anchor points on which we can do analytics.
 Technically the first category, “closed already by week 3” won’t appear in the week 3 pipeline so there is an argument, particularly in companies where week 1-2 sales are highly volatile, to do the analysis on a to-go basis.
I’m Dave Kellogg, technology executive, investor, independent director, adviser, and blogger. I’m also a hiker, oenophile, and fly fisher.
From 2012 to 2018, I was CEO of cloud enterprise performance management vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a highly competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of Service Cloud at Salesforce and CEO at NoSQL database provider MarkLogic. Before that, I was CMO at Business Objects for nearly a decade as we grew from $30M to over $1B. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including ClearedIn, FloQast, GainSight, Lecida, MongoDB, Recorded Future, Tableau and TopOPPs. I currently sit on the boards of Alation (data catalogs) and Nuxeo (content management) and previously sat on the boards of agtech leader Granular (acquired by DuPont for $300M) and big data leader Aster Data (acquired by Teradata for $325M).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).