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.
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.
CEO: Well, some of them are new and not fully productive yet.
VC: How long does it take for them to fully ramp?
CEO: Well, to full productivity, four quarters.
VC: So how many fully-ramped reps do you have?
CEO: 9 fully ramped, but we have 15 in various stages of ramping, and 1 who’s brand new …
There’s a better way to have this conversion, to perform your sales analytics, and to build your bookings capacity waterfall model. That better way involves creating a new metric called ramped rep equivalents (RREs). Let’s build up to talking about RREs by first looking at a classical sales bookings waterfall model.
I love building these models and they’re a lot of fun to play with, doing what-if analysis, varying the drivers (which are in the orange cells) and looking at the results. This is a simplified version of what most sales VPs look at when trying to decide next year’s hiring, next year’s quotas , and next year’s targets. This model assumes one type of salesrep ; a distribution of existing reps by tenure as 1 first-quarter, 3 second-quarter, 5 third-quarter, 7 fourth-quarter, and 9 steady-state reps; a hiring pattern of 1, 2, 4, 6 reps across the four quarters of 2019; and a salesrep productivity ramp whereby reps are expected to sell 0% of steady-state productivity in their first quarter with the company, and then 25%, 50%, 75% in quarters 2 through 4 and then become fully productive at quarter 5, selling at the steady-state productivity level of $1,000K in new ARR per year .
Using this model, a typical sales VP — provided they believed the productivity assumptions  and that they could realistically set quotas about 20% above the target productivity — would typically sign up for around a $22M new ARR bookings target for the coming year.
While these models work just fine, I have always felt like the second block (bookings capacity by tenure), while needed for intermediate calculations, is not terribly meaningful by itself. The lost opportunity here is that we’re not creating any concept to more easily think about, discuss, and analyze the productivity we get from reps as they ramp.
Enter the Ramped Rep Equivalent (RRE)
Rather than thinking about the partial productivity of whole reps, we can think about partial reps against whole productivity — and build the model that way, instead. This has the by-product of creating a very useful number, the RRE. Then, to get bookings capacity just multiply the number of RREs times the steady-state productivity. Let’s see an example below:
This provides a far more intuitive way of thinking about salesrep ramping. In 1Q19, the company has 25 reps, only 9 of whom are fully ramped, and rest combine to give the productivity of 8.5 additional reps, resulting in an RRE total of 17.5.
“We have 25 reps on board, but thanks to ramping, we only have the capacity equivalent to 17.5 fully-ramped reps at this time.”
This also spits out three interesting metrics:
RRE/QCR ratio: an effective vs. nominal capacity ratio — in 1Q19, nominally we have 25 reps, but we have only the effective capacity of 17.5 reps. 17.5/25 = 70%.
Capacity lost to ramping (dollars): to make the prior figure more visceral, think of the sales capacity lost due to ramping (i.e., the delta between your nominal and effective capacity) expressed in dollars. In this case, in 1Q19 we’re losing $1,875K of our bookings capacity due to ramping.
Capacity lost to ramping (percent): the same concept as the prior metric, simply expressed in percentage terms. In this case, in 1Q19 we’re losing 30% of our bookings capacity due to ramping.
Impacts and Cautions
If you want to move to an RRE mindset, here are a few tips:
RREs are useful for analytics, like sales productivity. When looking at actuals you can measure sales productivity not just by starting-period or average-period reps, but by RRE. It will provide a much more meaningful metric.
You can use RREs to measure sales effectiveness. At the start of each quarter recalculate your theoretical capacity based on your actual staffing. Then divide your actuals by that start-of-quarter theoretical capacity and you will get a measure of how well you are performing, i.e., the utilization of the quarterly starting capacity in your sales force. When you’re missing sales targets it is typically for one of two reasons: you don’t have enough capacity or you’re not making use of the capacity you have. This helps you determine which.
Beware that if you have multiple types of reps (e.g., corporate and field), you be tempted to blend them in the same way you do whole reps today –i.e., when asked “how many reps do you have?” most people say “15” and not “9 enterprise plus 6 corporate.” You have the same problem with RREs. While it’s OK to present a blended RRE figure, just remember that it’s blended and if you want to calculate capacity from it, you should calculate RREs by rep type and then get capacity by multiplying the RRE for each rep type by their respective steady-state productivity.
I recommend moving to an RRE mindset for modeling and analyzing sales capacity. If you want to play with the spreadsheet I made for this post, you can find it here.
Thanks to my friend Paul Albright for being the first person to introduce me to this idea.
# # #
 This is actually a productivity model, based on actual sales productivity — how much people have historically sold (and ergo should require little/no cushion before sales signs up for it). Most people I know work with a productivity model and then uplift the desired productivity by 15 to 25% to set quotas.
 Most companies have two or three types (e.g., corporate vs. field), so you typically need to build a waterfall for each type of rep.
 To build this model, you also need to know the aging of your existing salesreps — i.e., how many second-, third-, fourth-, and steady-state-quarter reps you have at the start of the year.
 The glaring omission from this model is sales turnover. In order to keep it simple, it’s not factored in here. While some people try to factor in sales turnover by using reduced sales productivity figures, I greatly prefer to model realistic sales productivity and explicitly model sales turnover in creating a sales bookings capacity model.
 This is one reason it’s so expensive to build an enterprise software sales force. For several quarters you often get 100% of the cost and 50% of the sales capacity.
 Which should be an weighted average productivity by type of rep weighted by number of reps of each type.
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).