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.
Enterprise SaaS and retailers have more in common than you might think.
Let’s think about retailers for a minute. Retailers drive growth in two ways:
They open new stores
They increase sales at existing stores
Opening new stores is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right locations, sometimes new locations just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:
Things look reasonable overall, the company is growing at 17%. But when you dig deeper you see that virtually all of the growth is coming from new stores. Revenue from existing stores is virtually flat at 2%.
It’s for this reason that retailers routinely publish same-store sales in their financial results. So you can see not only overall, blended growth but also understand how much of that growth is coming from new store openings vs. increasing sales at existing stores.
Now, let’s think about enterprise software.
Enterprise software vendors drive growth in two ways:
They hire new salesreps
They increase productivity of existing salesreps
Hiring new salesreps is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right territories, hiring profiles and interviewing process, sometimes new salesreps just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:
If you’re feeling a certain déjà vu, you’re right. I simply copy-and-pasted the text, substituting “enterprise software vendor” for “retailer” and “salesrep” for “store.” It’s exactly the same concept.
The problem is that we, as an industry, have basically no metric that addresses it.
Revenue, bookings, and billings growth are all blended metrics that mix results from existing and new salespeople 
Retention and expansion rates are about cohorts, but cohorts of customers, not cohorts of salespeople 
Sales productivity is typically measured as ARR/salesrep which blends new and existing salesreps 
Sales per ramped rep, measured as ARR/ramped-rep, starts to get close, but it’s not cohort-based, few companies measure it, and those that do often calculate it wrong 
So what we need is a cohort-based metric that compares the productivity of reps here today with those here a year ago . Unlike retail, where stores don’t really ramp , we need to consider ramping in defining the cohort, and thus define the year-ago cohort to include only fully-ramped reps .
So here’s how I define same-rep sales: sales from reps who were fully ramped a year ago and still here.
Here’s an example of presenting it:
The above table shows same-rep sales via an example where overall sales growth is good at 48%, driven by a 17% increase in same-rep sales and an 89% increase in new-rep sales. Note that enterprise software is a business largely built on the back of sales force expansion so — absent an acquisition or new product launch to put something new in sale’s proverbial bag — I view a 17% increase in same-rep sales as pretty good.
Let’s conclude by sharing a table of sales productivity metrics discussed in this post that I think provides a nice view of sales productivity as related to hiring and ramping.
The spreadsheet I used for this post is available for download, here.
# # #
 Billings is a public company SaaS metric and typically a proxy for bookings.
 Public companies never release this but most public and private companies track it.
 By taking overall new ARR (i.e., from all reps) and dividing it by the number of ramped reps, thus blending contribution from both new and existing reps in the numerator. Plus, these are usually calculated on a snapshot (not a cohort) basis.
 This is not survivor-biased in my mind because I am trying to get a productivity metric. By analogy, I believe stores that closed in the interim are not included in same-store sales calculations.
 Or to the extent they do, it takes weeks or months, not quarters. Thus you can simply include all stores open in the year-ago cohort, even if they just opened.
 I am trying to avoid seeing an increase in same-rep sales due to ramping — e.g., someone who just started in the year-ago cohort will have year sales, but should increase to full productivity simply by virtue of ramping.
I’ve seen numerous startups try numerous ways to calculate their sales capacity. Most are too back-of-the-envelope and to top-down for my taste. Such models are, in my humble opinion, dangerous because the combination of relatively small errors in ramping, sales productivity, and sales turnover (with associated ramp resets) can result in a relatively big mistake in setting an operating plan. Building off quota, instead of productivity, is another mistake for many reasons .
Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped). This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters . This should be based in historical data as well.
Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Judgment, the model should have the built-in ability to let the CEO and/or sales VP manually adjust the output and provide analytical support for so doing .
Quota over-assignment, the extent to which you assign more quota at the “street” level (i.e., sum of the reps) beyond the operating plan targets
For extra credit and to help maintain organizational alignment — while you’re making a bookings model, with a little bit of extra math you can set pipeline goals for the company’s core pipeline generation sources , so I recommend doing so.
If your company is large or complex you will probably need to create an overall bookings model that aggregates models for the various pieces of your business. For example, inside sales reps tend to have lower quotas and faster ramps than their external counterparts, so you’d want to make one model for inside sales, another for field sales, and then sum them together for the company model.
In this post, I’ll do two things: I’ll walk you through what I view as a simple-yet-comprehensive productivity model and then I’ll show you two important and arguably clever ways in which to use it.
Walking Through the Model
Let’s take a quick walk through the model. Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model.
You need to enter data into the model for 1Q20 (let’s pretend we’re making the model in December 2019) by entering what we expect to start the year with in terms of sales reps by tenure (column D). The “first/hired quarter” row represents our hiring plans for the year. The rest of this block is a waterfall that ages the rep downward as we move across quarters. Next to the block ramp assumption, which expresses, as a percentage of steady-state productivity, how much we expect a rep to sell as their tenure increases with the company. I’ve modeled a pretty slow ramp that takes five quarters to get to 100% productivity.
To the right of that we have more assumptions:
Annual turnover, the annual rate at which sales reps leave the company for any reason. This drives attriting reps in row 12 which silently assumes that every departing rep was at steady state, a tacit fairly conservative assumption in the model.
Steady-state productivity, how much we expect a rep to actually sell per year once they are fully ramped.
Quota over-assignment. I believe it’s best to start with a productivity model and uplift it to generate quotas .
The next block down calculates ramped rep equivalents (RREs), a very handy concept that far too few organizations use to convert the ramp-state to a single number equivalent to the number of fully ramped reps. The steady-state row shows the number of fully ramped reps, a row that board members and investors will frequently ask about, particularly if you’re not proactively showing them RREs.
After that we calculate “productivity capacity,” which is a mouthful, but I want to disambiguate it from quota capacity, so it’s worth the extra syllables. After that, I add a critical row called judgment, which allows the Sales VP or CEO to play with the model so that they’re not potentially signing up for targets that are straight model output, but instead also informed by their knowledge of the state of the deals and the pipeline. Judgment can be negative (reducing targets), positive (increasing targets) or zero-sum where you have the same annual target but allocate it differently across quarters.
The section in italics, linearity and growth analysis, is there to help the Sales VP analyze the results of using the judgment row. After changing targets, he/she can quickly see how the target is spread out across quarters and halves, and how any modifications affect both sequential and quarterly growth rates. I have spent many hours tweaking an operating plan using this part of the sheet, before presenting it to the board.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model.
Finally, while we’re at it, we’re only a few clicks away from generating the day-one pipeline coverage / contribution goals from our major pipeline sources: marketing, alliances, and outbound SDRs. In this model, I start by assuming that sales or customer success managers (CSMs) generate the pipeline for upsell (i.e., sales to existing customers). Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers). So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages .
Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
Two Clever Ways to Use the Model
The sad reality is that this kind of model gets a lot attention at the end of a fiscal year (while you’re making the plan for next year) and then typically gets thrown in the closet and ignored until it’s planning season again.
That’s too bad because this model can be used both as an evaluation tool and a predictive tool throughout the year.
Let’s show that via an all-too-common example. Let’s say we start 2020 with a new VP of Sales we just hired in November 2019 with hiring and performance targets in our original model (above) but with judgment set to zero so plan is equal to the capacity model.
Our “world-class” VP immediately proceeds to drive out a large number of salespeople. While he hires 3 “all-star” reps during 1Q20, all 5 reps hired by his predecessor in the past 6 months leave the company along with, worse yet, two fully ramped reps. Thus, instead of ending the quarter with 20 reps, we end with 12. Worse yet, the VP delivers new ARR of $2,000K vs. a target of $3,125K, 64% of plan. Realizing she has a disaster on her hands, the CEO “fails fast” and fires the newly hired VP of sales after 5 months. She then appoints the RVP of Central, Joe, to acting VP of Sales on 4/2. Joe proceeds to deliver 59%, 67%, and 75% of plan in 2Q20, 3Q20, and 4Q20.
Our question: is Joe doing a good job?
At first blush, he appears more zero than hero: 59%, 67%, and 75% of plan is no way to go through life.
But to really answer this question we cannot reasonably evaluate Joe relative to the original operating plan. He was handed a demoralized organization that was about 60% of its target size on 4/2. In order to evaluate Joe’s performance, we need to compare it not to the original operating plan, but to the capacity model re-run with the actual rep hiring and aging at the start of each quarter.
When you do this you see, for example, that while Joe is constantly underperforming plan, he is also constantly outperforming the capacity model, delivering 101%, 103%, and 109% of model capacity in 2Q through 4Q.
If you looked at Joe the way most companies look at key metrics, he’d be fired. But if you read this chart to the bottom you finally get the complete picture. Joe is running a significantly smaller sales organization at above-model efficiency. While Joe got handed an organization that was 8 heads under plan, he did more than double the organization to 26 heads and consistently outperformed the capacity model. Joe is a hero, not a zero. But you’d never know if you didn’t look at his performance relative to the actual sales capacity he was managing.
Second, I’ll say the other clever way to use a capacity model is as a forecasting tool. I have found a good capacity model, re-run at the start of the quarter with then-current sales hiring/aging is a very valuable predictive tool, often predicting the quarterly sales result better than my VP of Sales. Along with rep-level, manager-level, and VP-level forecasts and stage-weighted and forecast-category-weighted expected pipeline values, you can use the re-run sales capacity model as a great tool to triangulate on the sales forecast.
You can download the four-tab spreadsheet model I built for this post, here.
# # #
 Starting with quota starts you in the wrong mental place — what you want people to do, as opposed to productivity (what they have historically done). Additionally, there are clear instances where quotas get assigned against which we have little to no actual productivity assumption (e.g., a second-quarter rep typically has zero productivity but will nevertheless be assigned some partial quota). Sales most certainly has a quota-allocation problem, but that should be a separate, second exercise after building a corporate sales productivity model on which to base the operating plan.
 A typically such vector might be (0%, 25%, 50%, 100%) or (0%, 33%, 66%, 100%) reflecting the percentage of steady-state productivity they are expected to achieve in their first, second, third, and fourth quarters of employment.
 Without such a row, the plan is either de-linked from the model or the plan is the pure output of the model without any human judgement attached. This row is typically used to re-balance the annual number across quarters and/or to either add or subtract cushion relative to the model.
 Back in the day at Salesforce, we called pipeline generation sources “horsemen” I think (in a rather bad joke) because there were four of them (marketing, alliances, sales, and SDRs/outbound). That term was later dropped probably both because of the apocalypse reference and its non gender-neutrality. However, I’ve never known what to call them since, other than the rather sterile, “pipeline sources.”
 Many salesops people do it the reverse way — I think because they see the problem as allocating quota whereas I see the the problem as building an achievable operating plan. Starting with quota poses several problems, from the semantic (lopping 20% off quota is not 20% over-assignment, it’s actually 25% because over-assignment is relative to the smaller number) to the mathematical (first-quarter reps get assigned quota but we can realistically expect a 0% yield) to the procedural (quotas should be custom-tailored based on known state of the territory and this cannot really be built into a productivity model).
 One advantages of having those percentages here is they are placed front-and-center in the company’s bookings model which will force discussion and agreement. Otherwise, if not documented centrally, they will end up in different models across the organization with no real idea of whether they either foot to the bookings model or even sum to 100% across sources.
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.
Happily, in the past several years startups are increasingly recognizing the value of strong sales enablement and sales productivity teams. So it’s no surprise that I hear a lot about high-growth companies building onboarding programs to enable successfully scaling their sales organizations and sustain their growth. What’s disappointing, however, is how little I hear about the hiring profiles of the people that we want to put into these programs.
Everyone loves to talk about onboarding, but everybody hates to talk about hiring profiles. It doesn’t make sense. It’s like talking about a machine — how it works and what it produces — without ever talking about what you feed into it. Obviously, when you step back and think about it, the success of any onboarding program is going to be a function of both the program and people you feed into it. So we are we so eager to talk about the former and so unwilling to talk about the latter?
Talking about the program is fairly easy. It’s a constructive exercise in building something that many folks have built before — so it’s about content structuring, best practice sharing, and the like. Talking about hiring profiles — i.e., the kind of people we want to feed into it — is harder because:
It’s constraining. “Well, an ideal new hire might look like X, but we’re not always going to find that. If that one profile was all I could hire, I could never build the sales team fast enough.”
It’s a matter of opinion. “Success around here comes in many shapes and sizes. There is not just one profile.”
It’s unscientific. “I can just tell who has the sales gene and who doesn’t. That’s the hardest thing to hire for. And I just know when they have it.”
It’s controversial. “Turns out none of my six first-line sales managers really agree on what it takes — e.g., we have an endless debate on whether domain-knowledge actually hurts or helps.”
It’s early days. “Frankly, we just don’t know what the key success criteria are, and we’re working off a pretty small sample.”
You have conflicting data. “Most of the ex-Oracle veterans we’ve hired have been fish out of water, but two of them did really well.”
There are invariably outliers. “Look at Joe, we’d never hire him today — he looks nothing like the proposed profile — but he’s one of our top people.”
That’s why most sales managers would probably prefer discussing revenue recognition rules to hiring profiles. “I’ll just hire great sales athletes and the rest will take care of itself.” But will it?
In fact, the nonsensicality of the fairly typical approach to building a startup sales force becomes most clear when viewed through the onboarding lens.
Imagine you’re the VP of sales enablement:
“Wait a minute. I suppose it’s OK if you want to let every sales manager hire to their own criteria because we’re small and don’t really know for sure what the formula is. But how am I supposed to build a training program that has a mix of people with completely different backgrounds:
Some have <5 years, some have 5-10 years, and some have 15+ years of enterprise sales experience?
Some know the domain cold and have sold in the category for years whereas others have never sold in our category before?
Some have experience selling platforms (which we do) but some have only sold applications?
Some are transactional closers, some are relationship builders, and some are challenger-type solution sellers?”
I understand that your company may have different sales roles (e.g., inside sales, enterprise sales)  and that you will have different hiring profiles per role. But you if you want to scale your sales force — and a big part of scaling is onboarding — then you’re going to need to recruit cohorts that are sufficiently homogeneous that you can actually build an effective training program. I’d argue there are many other great reasons to define and enforce hiring profiles , but the clearest and simplest one is: if you’re going to hire a completely heterogeneous group of sales folks, how in the heck are you going to train them?
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 Though I’d argue that many startups over-diversify these roles too early. Concretely put, if you have less than 25 quota-carrying reps, you should have no more than two roles.
 Which can include conscious, deliberate experiments outside them.
I had the pleasure of working with Elay Cohen during my circa year at Salesforce.com and I reviewed SalesHood, his first book, over four years ago. We were early and happy customers of the SalesHood application at Host Analytics. I’m basically a big fan of Elay’s and what he does. With the average enterprise SaaS startup spending somewhere between 40% to 80%+ of revenue on sales, doesn’t it make sense to carve off some portion of that money into a Sales Enablement team, to make sure the rest is well spent? It sure does to me.
I was pleased to hear that Elay had written a second book, Enablement Mastery, and even more pleased to be invited to the book launch in San Francisco several weeks back. Here’s a photo of Cloudwords CEO Michael Meinhardt and me at the event.
I have to say I simply love salesops and sales productivity people. They’re uniformly smart, positive, results-oriented, and — unlikely many salespeople — process-oriented. A big part of the value of working with SalesHood, for a savvy customer, is to tap into the network of amazing sales enablement professionals Elay has built and whose stories are profiled in Enablement Mastery.
I read the book after the event and liked it. I would call it a holistic primer on sales enablement which, since it’s a relatively new and somewhat misunderstood discipline, is greatly in need in the market.
Elay’s a great story-teller so the book is littered with stories and examples, from his own considerable experience building the impressive Salesforce.com sales productivity team, to the many stories of his friends and colleagues profiled in the book.
Some of the more interesting questions Elay examines in Enablement Mastery include:
Why sales enablement?
Where to plug it organizationally? (With pros and cons of several choices.)
What to do in your first 90 days in a new sales enablement role?
What to look for when hiring sales enablement professionals?
How to get organizational (and ideally strong CEO) buy-in to the sales enablement program?
How sales enablement can work best with marketing? (Hint: there is often tension here.)
What is a holistic process map for the sales enablement function?
How to measure the sales enablement function? (And it better be more than instructor ratings on the bootcamp.)
How to enable front-line managers to be accountable for their role enabling and developing their teams? (Elay wrote a whole chapter on this topic.)
What core deliverables need to be produced by the marketing and sales productivity teams?
Elay, never one to forget to celebrate achievement and facilitate peer-level knowledge sharing, also offers tips on how to runs sales kickoffs and quota clubs.
Overall, I’d highly recommend Enablement Mastery as a quick read that provides a great, practical overview of an important subject. If you’re going to scale your startup and your sales force, sales enablement is going to be an important part of the equation.
I’m Dave Kellogg, consultant, independent director, advisor, and blogger focused on enterprise software startups.
I bring a unique perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
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 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, which we grew from zero to $80M in run-rate revenues during my tenure. 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 Cyral, FloQast, GainSight, Kelda, MongoDB, Plannuh, Recorded Future, and Tableau. I currently sit on the boards of Alation (data catalogs), Nuxeo (content management) and Profisee (master data management). I 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).