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.
Does my company spend too much on marketing? Too little? How I do know? What is the right level of marketing spend at an enterprise software startup? I get asked these questions all the time by startup CEOs, CMOs, marketing VPs, and marketing directors.
You can turn to financial benchmarks, like the KeyBanc Annual SaaS Survey for some great high-level answers. You can subscribe to SiriusDecisions for best practices and survey data. Or you can buy detailed benchmark data  from OPEXEngine. These are all great sources and I recommend them heartily to anyone who can afford them.
But, in addition to sometimes being too high-level , there is one key problem with all these forms of benchmark data: they’re not about you. They’re not based on your operating history. While I certainly recommend that executives know their relevant financial benchmarks, there’s a difference between knowing what’s typical for the industry and what’s typical for you.
So, if you want to know if your company is spending enough on marketing , the first thing you should do is to make an inverted demand generation (aka, demandgen) funnel to figure out if you’re spending enough on demandgen. It’s quite simple and I’m frankly surprised how few folks take the time to do it.
Here’s an inverted demandgen funnel in its simplest form:
Let’s walk through the model. Note that all orange cells are drivers (inputs) and the white cells are calculations (outputs). This model assumes a steady-state situation  where the company’s new ARR target is $2,000,000 each quarter. From there, we simply walk up the funnel using historical deal sizes and conversion rates .
With an average sales price (ASP) of $75,000, the company needs to close 27 opportunities each quarter.
With a 20% sales qualified lead (SQL) to close rate we will need 133 SQLs per quarter.
If marketing is responsible for generating 80% of the sales pipeline, then marketing will need to generate 107 of those SQLs.
If our sales development representatives (SDRs) can output 2.5 opportunities per week then we will need 5 SDRs (rounding up).
With an 80% SAL to SQL conversion rate we will need 133 SALs per quarter.
With a 10% MQL to SAL conversion rate we will need 1,333 MQLs per quarter.
With a cost of $250 per MQL, we will need a demandgen budget  of $333,333 per quarter.
The world’s simplest way to calculate the overall marketing budget at this point would be to annualize demandgen to $1.3M and then double it, assuming the traditional 50/50 people/programs ratio .
Not accounting for phase lag or growth (which will be the subjects of part II and part III of this post), let’s improve our inverted funnel by adding benchmark and historical data.
Let’s look at what’s changed. I’ve added two columns, one with 2019 actuals and one with benchmark data from our favorite source. I’ve left the $2M target in both columns because I want to compare funnels to see what it would take to generate $2M using either last year’s or our benchmark’s conversion rates. Because I didn’t want to change the orange indicators (of driver cells) in the left column, when we have deviations from the benchmark I color-coded the benchmark column instead. While our projected 20% SQL-to-close rate is an improvement from the 18% rate in 2019, we are still well below the benchmark figure of 25% — hence I coded the benchmark red to indicate a problem in this row. Our 10% MQL-to-SQL conversion rate in the 2020 budget is a little below the benchmark figure of 12%, so I coded it yellow. Our $250 cost/MQL is well below the benchmark figure of $325 so I coded it green.
Finally, I added a row to show the relative efficiency improvement of the proposed 2020 budget compared to last year’s actuals and the benchmark. This is critical — this is the proof that marketing is raising the bar on itself and committed to efficiency improvement in the coming year. While our proposed funnel is overall 13% more efficient than the 2019 funnel, we still have work to do over the next few years because we are 23% less efficient than we would be if we were at the benchmark on all rates.
However, because we can’t count on fixing everything at once, we are taking a conservative approach where we show material improvement over last year’s actuals, but not overnight convergence to the benchmark — which could take us from kaizen-land to fantasy-land and result in a critical pipeline shortage downstream.
Moreover because this approach shows not only a 13% overall efficiency improvement but precisely where you expect it to come from, the CEO can challenge sales and marketing leadership:
Why are we expecting to increase our ASP by $5K to $75K?
Why do you think we can improve the SQL-to-close rate from 18% to 20% — and what you are doing to drive that improvement? 
What are we doing to improve the MQL-to-SAL conversion rate?
How are we going to improve our already excellent cost per MQL by $25?
In part II and part III of this post, we’ll discuss two ways of modeling phase-lag, modeling growth, and the separation of the new business and upsell funnels.
You can download my spreadsheet for this post, here.
 For marketing or virtually anything else.
 i.e., looking at either S&M aggregated or even marketing overall.
 The other two pillars of marketing are product marketing and communications. The high-level benchmarks can help you analyze spend on these two areas by subtracting your calculated demandgen budget from the total marketing budget suggested by a benchmark to see “what’s left” for the other two pillars. Caution: sometimes that result is negative!
 The astute reader will instantly see two problems: (a) phase-lag introduced by both the lead maturation (name to MQL) and sales (SQL to close) cycles and (b) growth. That is, in a normal high-growth startup, you need enough leads not to generate this quarter’s new ARR target but the target 3-4 quarters out, which is likely to be significantly larger. Assuming a steady-state situation gets rid of both these problems and simplifies the model. See part II and part III of this post for how I like to manage that added real-world complexity.
 Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.
 When I say demandgen budget, I mean money spent on generating leads through marketing campaigns. Sometimes that very directly (e.g., adwords). Other times it’s a bit indirectly (e.g., an SEO program). I do not include demandgen staff because I am trying to calculate the marginal cost of generating an extra MQL. That is, I’m not trying to calculate what the company spends, in total, on demandgen activities (which would include salary, benefits, stock-based comp, etc. for demandgen staff) but instead the marketing programs cost to generate a lead (e.g., in case we need to figure out how much to budget to generate 200 more of them).
 In an increasingly tech-heavy world where marketing needs to invest a lot in infrastructure as well, I have adapted the traditional 50/50 people/programs rule to a more modern 45/45/10 people/programs/infrastructure rule, or even an infrastructure-heavy split of 40/40/20.
 Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.
Slowly and steadily, over the past decade, the industry has evolved from a mentality of “all salesreps must do everything” – including some percent of their time prospecting — to one of specialization. We, with the help of books like Predictable Revenue, have collectively decided that in-bound lead processing is different from outbound lead prospecting is different from low-end, velocity sales is different from high-end, enterprise sales.
Despite the old-school, almost-character-building emphasis on prospecting, we have collectively realized that having our top hunters dialing for dollars and digging through inbound leads isn’t, well, the best use of their time.
Industrialization typically involves specialization and the industrialization
of once purely artisanal software sales has been no exception. As part of this specialization the sales
development representative (SDR) role has risen to prominence. In this post, we’ll do a quick review of what
SDRs typically do and discuss the relative merits of having them report into
sales vs. marketing.
“Everyone under 25 in San Francisco is an SDR.” – Anonymous startup CEO
SDRs Bridge the Two
SDRs typically form the bridge between sales and marketing. A typical SDR job is take inbound leads from
marketing, perform some basic BANT-style  qualification on them, and then
pass them to sales if indicated. While SDRs typically have activity quotas
(e.g., 50 calls/day) they should be primarily measured on the number of
opportunities they create per week. In enterprise software, typically that quota
is 2-3 oppties/week.
As companies get bigger they tend to separate SDRs into two
Inbound SDRs, those who only process in-bound
Outbound SDRs, those who primarily do targeted outreach
over the phone or email
Being an SDR is a hard job.
Typical SDR challenges include:
Adhering to service-level agreements for all leads (i.e., touches with timeframes)
Contacting prospects in an increasingly spam-hostile, call-hostile environment
Figuring out which leads to work on the hardest (e.g., which merit homework to customize the message and which don’t)
Remembering that their job is to sell meetings and not product 
Supporting multiple salespeople with often conflicting priorities 
Managing the conflict between supporting salespeople and executing the process
Getting salespeople to show-up at the hand-off meeting 
Avoiding burnout in a high-pressure environment
To Which Department
Should SDRs Report: Sales or Marketing?
Historically, SDRs reported to sales. That’s probably because sales first decided to fund SDR teams as a way getting inbound lead management out of the hands of salespeople . Doing so would:
Enable the company to consistently respond in a
timely manner to all inquiries
Free up sales to spend more time on selling
Avoid the problem of individual reps not
processing new leads once they are “full up” on opportunities 
The problem is that most enterprise software sales VPs are not particularly process-oriented , because they grew up in a pre-industrialized era of sales . In fact, nothing drives me crazier than an old-school, artisanal, deal-person CRO insisting on owning the SDR organization despite the total inability to manage it. They rationalize: “Oh, I can hire someone process-oriented to manage it.” And I think: “but what can that person learn from you  about how to manage it?” And the answer is nothing. Your desire to own it is either pure ego or simply a ploy to enrich your resume.
I’ll say again because it drives me crazy: do not be the VP of Sales who insists on owning the SDR organization in the annual planning meeting but then shows zero interest in it for the rest of the year. You’re not helping anyone!
As mentioned in a footnote in a prior post, I greatly prefer SDRs reporting to marketing versus sales. Why?
Marketing leadgen and nurture people are metrics- and process-oriented animals, naturally suited to manage a process-oriented department.
It provides a simple, clear conceptual model: marketing is the opportunity creation factory and sales is the opportunity closing machine.
In short, marketing’s job is to make opportunities. Sales’ job is to close them.
# # #
 BANT = budget, authority, need, time-frame.
 Most early- and mid-stage startups put SDRs in their regular sales training sessions which I think does them a disservice. Normal sales training is about selling products/solutions. SDRs “sell” meetings. They should not attempt to build business value or differentiation. Training them to do so tempts them to do – even when it is not their job.
 A typical QCR:SDR ratio is 3-4:1, though I’ve seen as
low as 1:1 and as high as 6:1
 Believe it or not, this sometimes happens (typically when your reps are already carrying a lot of oppties). Few things reflect worse on the company than a last-minute rescheduling of the meet-your-salesperson call. You don’t get a second chance to make a firm impression.
 Although most early models had wide bypass rules – e.g., “leads with VP title at this list of key accounts will get passed directly to reps for qualification” – reflecting a lack of trust in marketing beyond dropping leaflets from airplanes.
 That problem could still exist at hand-off (i.e., opportunity creation) time but at least we have combed through the leads to find the good ones, and reports can easily identify overloaded reps.
 While they may be process-oriented when it comes to the sales process for a deal moving across stages during a quarter, that is not quite the same thing as a velocity mentality driven by daily or weekly goals with tracking metrics. If you will, there’s process-oriented and Process-Oriented.
 One simple test:
if your sales org doesn’t have monthly cadence (e.g., goals, forecasts)
then your sales VP is probably not capital P process-oriented.
 On the theory you should always build organizations where people can learn from their managers.
Most startups today use some variation on the now fairly standard terms SAL (sales accepted lead) and SQL (sales qualified lead). Below see the classic  lead funnel model from marketing bellwether Sirius Decisions that defines this.
One great thing about working as an independent board member and consultant is that you get to work with lots of companies. In doing this, I’ve noticed that while virtually everyone uses the terminology SQL and SAL, that some people define SQL before SAL and others define SAL before SQL.
Why’s that? I think the terminology was poorly chosen and is confusing. After all, what sounds like it comes first: sales accepting a lead or sales qualifying a lead? A lot of folks would say, “well you need to accept it before you can qualify it.” But others would say “you need to qualify it before you can accept it.” And therein lies the problem.
The correct answer, as seen above, is that SAL comes before SQL. I have a simple way of remembering this: A comes before Q in the alphabet, and SAL comes before SQL in the funnel. Until I came up with that I was perpetually confused.
More importantly, I think I also have a way of explaining it. Start by remembering two things:
This model was defined at a time when sales development reps (SDRs) generally reported to sales, not marketing .
This model was defined from the point of view of marketing.
Thus, sales accepting the lead didn’t mean a quota-carrying rep (QCR) accepted the lead – it meant an SDR, who works in the sales department, accepted the lead. So it’s sales accepting the lead in the sense that the sales department accepted it. Think: we, marketing, passed it to sales.
After the SDR worked on the lead, if they decided to pass it to a QCR, the QCR would do an initial qualification call, and then the QCR would decide whether to accept it. So it’s a sales qualified lead, in the sense that a salesperson has qualified it and decided to accept it as an opportunity.
Think: accepted by an SDR, qualified by a salesrep.
Personally, I prefer avoid the semantic swamp and just say “stage 1 opportunity” and “stage 2 opportunity” in order to keep things simple and clear.
# # #
 This model has since been replaced with a newer demand unit waterfall model that nevertheless still uses the term SQL but seems to abandon SAL.
 I greatly prefer SDRs reporting to marketing for two
reasons: [a] unless you are running a
pure velocity sales model, your sales leadership is more likely to deal-people
than process-people – and running the SDRs is a process-oriented job and [b] it
eliminates a potential crack in the funnel by passing leads to sales “too early”. When SDRs report to marketing, you have a
clean conceptual model: marketing is the
opportunity creation factory and sales is the opportunity closing factory.
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.
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 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.
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).