Category Archives: Sales

Measuring Ramped and Steady-State Sales Productivity: The Rep Ramp Chart

In prior posts I have discussed how to make a proper sales bookings productivity model and how to use the concept of ramped rep equivalents (RREs) in sales analytics and modeling. When it comes to setting drivers for both, corporate leaders tend to lean towards benchmarks and industry norms for the values.  For example, two such common norms are:

  • Setting steady-state (or terminal) productivity at $1,200K of new ARR per rep in enterprise SaaS businesses
  • Using a {0%, 25%, 50%, 100%} productivity ramp for new salesreps in their {1st, 2nd, 3rd, 4th} quarters with the company (and 100% thereafter)

In this post, I’ll discuss how you can determine if either of those assumptions are reasonable at your company, given its history.

To do so, I’m introducing one of my favorite charts, the Rep Ramp Chart.  Unlike most sales analytics, which align sales along fiscal quarters, this chart aligns sales relative to a rep’s tenure with the company.

You start by listing every rep your company has ever hired [1] in order by hire date.  You then record their sales productivity (typically measured in new ARR bookings [2]) for their series of quarters with the company [3], up to and including their current-quarter forecast (which you shade in green).  Reps who leave the company are shaded black.  Reps who get promoted out of quota-carrying roles (e.g., sales management) are shaded blue.  Future periods are shaded grey.  Add a 4+ quarter average productivity column for each row, and average each of the figures in the columns [4].

Here’s what you get:

full

Despite having only a relatively small amount of data [5], we can still interpret this a little.

  • The relative absence of black lines means we’re pretty good at sales hiring.   I’ve seen real charts with 5 black lines in a row, usually down to a single bad management hire.
  • The absence of black lines that “start late”  — for example {0, 25, 75, 25, 55, black} — is also good.  Our reps are either “failing fast” or succeeding, but things are not dragging on forever when they’re not working.
  • Over average 4Q+ productivity is $308K per quarter, almost exactly $1,200K per year so it does seem valid to use that figure in our modeling.
  • Entering $300K as target productivity then shows the empirical rep ramp as a percent of steady-state productivity, exactly how sales leaders think of it.  In this case, we see a {10%, 38%, 76%, 85%, 98%} empirical ramp across the first five quarters.  If our bookings model assumed {0%, 25%, 50%, 100%, 100%} you’d say our model is a little optimistic in the first two quarters, a little pessimistic in the 3rd, and a little optimistic in the fourth.  If we had more data, we might adjust it a bit based on that.

I love this chart because it presents unadulterated history and lets you examine the validity of two hugely important drivers in your sales bookings capacity model — drivers, by the way, that are often completely unquestioned [6].  For that reason, I encourage everyone to make this a standard slide in your Sales ops review (aka, QBR) template.  Note that since different types of rep ramp differently and hit different steady-state productivity levels, you should create one rep ramp per major type of rep in your company.  For example, corporate (or inside) sales reps will typically ramp more quickly to lower productivity levels than field reps who will ramp more slowly to higher productivity.  Channels reps will ramp differently from direct reps.  International reps may need their own chart as well.

You can download the spreadsheet I used here.

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Notes

[1] Sales management may want to omit those no longer with the company, but that also omits their data, and might omit important patterns of hiring failure, so don’t omit anyone.  You can always exclude certain rows from the analysis without removing them from the chart (i.e., hiding them).

[2] New ARR bookings typically includes new ARR to both new and existing customers.

[3] You’ll need as many columns to do this as your longest tenured rep has been with the company, so it can get wide.  Let it.  There’s data in there.

[4] Ensuring empty cells are not confused with cells whose value is zero.  Excel ignores empty cells in calculating averages but will average your 0’s in when you probably don’t want them.

[5] In order to keep it easily and quickly grasped

[6] Particularly the ramp.

Marketing Exists to Make Sales Easier

Many moons ago when I was young product marketing manager, I heard a new VP of Marketing speak at a marketing all-hands meeting.  He spoke with a kiwi accent and his name was Chris Greendale.  What he said were six words that changed my career:

Marketing exists to make sales easier

While this has clearly been a theme in Kellblog posts over the years, I realized that I’ve actually never done a dedicated post on it, despite having written reductionist mission statement posts for both professional services (“maximize ARR without losing money”) and human resources (“help managers manage”).

Being a math type, I love deriving things from first principles and this seemed the perfect first principle from which to derive marketing.  First, you hire a team to build your product.  Then, you hire a team to sell it.  The only reason you need marketing is to help the second team do its job better.

At my next job, I remember bumping into Larry, our fresh from the used-car lot VP of Business Development, who in frustration (as he often was), one day came to work with a bunch of t-shirts that looked something like this

Enterprise software is a two-engine plane and those two engines are quota-carrying salesreps (QCRs) who sell the software and storypoint-burning developers (DEVs) who write it [1].

Everyone else is “the help” — including marketing, finance, sales supporting roles (e.g., SCs, SDRs), engineering-supporting roles (e.g., QA, PM, TPM), customer service, and yes, the CEO.  The faster you understand this, in my humble opinion, the better.

And, while we’re in realization mode, the other thing to internalize is that it costs about twice as much to sell an enterprise software product as it does to build it.  Per KeyBanc, typical S&M spend is 45% of revenue and R&D runs about half that.

But back to the mantra, make sales easier.  Why did I like it so much?

First, it put marketing in its proper place.  At the time, there was something of a power struggle between sales and marketing, and CPG/brand management types were trying to argue that product marketing mangers should be the generals and that sales were just the foot-soldiers.  Looking both around me and at the P&L that just seemed wrong.  Maybe it worked in consumer products [2] but this was enterprise software.  Sales had all the budget and all the power to go with it.  We should help them and, ego aside, there’s nothing wrong with being a helper.

In fact, if you define your mission statement as “help” and remember that “help is defined in the mind of the recipient,” you’ve already gone a long way to aligning your sales and marketing.

Second, there was nothing written in stone that limited the scope of that help. Narrow thinking might limit marketing to a servile role.  That’s not my intent.  Help could take many forms, and while the primary form of requested help has evolved over time, help can include both the tactical and the strategic:

  • Giving sales qualified leads to work on.
  • Building training and tools that helps sales sell more.
  • Providing competitive information that helps win more deals.
  • Creating an ideal customer profile (ICP) that helps sales focus on the most winnable deals.
  • Building industry-specific messaging that helps sell in given verticals
  • Working with PM [3] to build product that is inherently more salable [4].
  • Corporate strategy development to put the company in the right markets with the right offerings.

When I say help, I don’t mean lowercase-h tactical help.  I mean help in all its forms, which can and should include the “tough love” form of help:  “I know you think you want that, but let me demonstrate that I’ve heard your request and now explain why I think it’s not a good idea.”

Being helpful doesn’t mean saying yes to everything.  I hearken back to Miracle on 34th Street whenever I’m drawn into this problem (quote adapted):

Kris Kringle:  No, but don’t you see, dear?  Some <salespeople> wish for things they couldn’t possibly use like real locomotives or B-29s.

If sales is asking you for a real locomotive or a B-29 you need to tell them.

For the rest of my marketing career, I took Greendale’s mantra and made it my own.  If sales were my customer and I were helping them, then:

  • We’d run sales satisfaction surveys to see how happy sales was with marketing and where they wanted us to invest and improve [5].
  • We’d make ourselves accountable.  One of the biggest stresses in the sales/marketing relationship was, to paraphrase an old joke, sales felt like the pig while marketing was the chicken.  We’d publish objectives, measure ourselves, and be honest about hits and misses.
  • We’d bring data to the party.  We’d leverage syndicated and custom research to try and made data-driven as opposed to opinion-driven decisions.
  • We’d stop back-seat drivers.  I’d remind anyone that got too uppity that “quotas are available” and they should go take one [6].
  • We wouldn’t be the marketing police, scolding people for using out-of-date materials.  If sales were using a deck we’d decommissioned quarters ago, our first response wouldn’t be “stop!” but “why?”
  • We’d market marketing.  We’d devote some time to internal marketing to let the sales organization know what we were doing and why.

We’d even do something that tested the limits of HR (particularly when I was in France).  I’d use the sales satisfaction survey to rank every customer-facing marketer on a matrix.

This gave me hard data on who sales knew in the department and what they thought of them.  If we’re going to make messaging for sales to present to customers, we’d better prepared to — and be good at — presenting it ourselves [7].

Overall, the mantra served me well, taking me from product marketing director to VP of product marketing to VP of corporate marketing to overall VP of marketing and a great run at Business Objects.  I’ve had plenty of people challenge me on it over the years — usually it’s because they understand it as purely tactical.  But it’s served me well and I encourage you to use it as your North Star in leading your marketing team.

After all, who doesn’t like help?

# # #

Notes

[1] You’d be wise to add those two figures to your one-page key metrics.  Somehow it’s always easier to hire the supporting staff than the “engine” staff, so keep an eye on the raw numbers of QCRs and DEVs and, for more fun, track their density in their respective organizations (QCRs/sales and DEVs/eng).

[2] Shout out to my daughter Stephanie who works in brand management on a consumer product and who can now inform me directly of how things work in that world — and it is different.

[3] PM = product management.

[4] Either in the sense of better solves the problem or in the tactical sense of wipes out competitive differentiation.

[5] One of my favorite results was the sales and SCs often wanted exactly the same thing, but that sales wanted it more (i.e., roughly the same priority curve but sales would rank everything even more important than the SCs).

[6] Most didn’t, but a few did, and some did remarkably well.

[7] We were probably a $100M company around the time we started this, so I’m not suggesting it for a 2-PMM startup.  And yes, I’d put myself on the matrix as well.

Should Customer Success Report into the CRO or the CEO?

The CEO.  Thanks for reading.

# # #

I was tempted to stop there because I’ve been writing a lot of long posts lately and because I do believe the answer is that simple.  First let me explain the controversy and then I’ll explain my view on it.

In days of yore, chief revenue officer (CRO) was just a gussied-up title for VP of Sales.  If someone was particularly good, particularly senior, or particularly hard to recruit you might call them CRO.  But the job was always the same:  go sell software.

Back in the pre-subscription era, basically all the revenue — save for a little bit of services and some maintenance that practically renewed itself — came from sales anyway.  Chief revenue officer meant chief sales officer meant VP of Sales.  All basically the same thing.  By the way, as the person responsible for effectively all of the company’s revenue, one heck of a powerful person in the organization.

Then the subscription era came along.  I remember the day at Salesforce when it really hit me.  Frank, the head of Sales, had a $1B number.  But Maria, the head of Customer Success [1], had a $2B number.  There’s a new sheriff in SaaS town, I realized, the person who owns renewals always has a bigger number than the person who runs sales [2], and the bigger you get the larger that difference.

Details of how things worked at Salesforce aside, I realized that the creation of Customer Success — particularly if it owned renewals — represented an opportunity to change the power structure within a software company. It meant Sales could be focused on customer acquisition and that Customer Success could be, definitionally, focused on customer success because it owned renewals.  It presented the opportunity to have an important check and balance in an industry where companies were typically sales-dominated to a fault.  Best of all, the check would be coming not just from a well-meaning person whose mission was to care about customer success, but from someone running a significantly larger amount of revenue than the head of Sales.

Then two complications came along.

The first complication was expansion ARR (annual recurring revenue).  Subscriptions are great, but they’re even better when they get bigger every year — and heck you need a certain amount of that just to offset the natural shrinkage (i.e., churn) that occurs when customers unsubscribe.  Expansion take two forms

  • Incidental:  price increases, extra seats, edition upsells, the kind of “fries with your burger” sales that are a step up from order-taking, but don’t require a lot of salespersonship.
  • Non-incidental:  cross-selling a complementary product, potentially to a different buyer within the account (e.g., selling Service Cloud to a VP of Service where the VP of Sales is using Sales Cloud) or an effectively new sale into different division of an existing account (e.g., selling GE Lighting when GE Aviation is already a customer).

While it was usually quite clear that Sales owned new customer acquisition and Customer Success owned renewals, expansion threw a monkey wrench in the machinery.  New sales models, and new metaphors to go with them, emerged. For example:

  • Hunter-only.  Sales does everything, new customer acquisition, both types of expansion, and even works on renewals.  Customer success is more focused on adoption and technical support.
  • Hunter/farmer.  Sales does new customer acquisition and non-incidental expansion and Customer Success does renewals and incidental expansion.
  • Hunter/hunter.  Where Sales itself is effectively split in two, with one team owning new customer acquisition after which accounts are quickly passed to a very sales-y customer success team whose primary job is to expand the account.
  • Farmers with shotguns.  A variation of hunter/hunter where an initial penetration Sales team focuses on “land” (e.g, with a $25K deal) and then passes the account to a high-end enterprise “expand” team chartered with major expansions (e.g., to $1M).

While different circumstances call for different models, expansion significantly complicated the picture.

The second complication was the rise of the chief revenue officer (CRO).  Generally speaking, sales leaders:

  • Didn’t like their diminished status, owning only a portion of company revenue
  • Were attracted to the buffer value in managing the ARR pool [3]
  • Witnessed too many incidents where Customer Success (who they often viewed as overgrown support people) bungled expansion opportunities and/or failed to maximize deals
  • Could exploit the fact that the check-and-balance between Sales and Customer Success resulted in the CEO getting sucked into a lot of messy operational issues

On this basis, Sales leaders increasingly (if not selflessly) argued that it was better for the CEO and the company if all revenue rolled up under a single person (i.e., me).  A lot of CEOs bought it.  While I’ve run it both ways, I was never one of them.

I think Customer Success should report into the CEO in early- and mid-stage startups.  Why?

  • I want the sales team focused on sales.  Not account management.  Not adoption.  Not renewals.  Not incidental expansion.  I want them focused on winning new deals either at new customers or different divisions of existing customers (non-incidental expansion).  Sales is hard.  They need to be focused on selling.  New ARR is their metric.
  • I want the check and balance.  Sales can be tempted in SaaS companies to book business that they know probably won’t renew.  A smart SaaS company does not want that business.  Since the VP of Customer Success is going to be measured, inter alia, on gross churn, they have a strong incentive call sales out and, if needed, put processes in place to prevent inception churnThe only thing worse than dealing with the problems caused by this check and balance is not hearing about those problems.  When one exec owns pouring water into the bucket and a different one owns stopping it from leaking out, you create a healthy tension within the organization.
  • They can work together without reporting to a single person.  Or, better put, they are always going to report to a single person (you or the CRO) so the question is who?  If you build compensation plans and operational models correctly, Customer Success will flip major expansions to Sales and Sales will flip incidental expansions back to Customer Success.  Remember the two rules in building a Customer Success model — never pair our farmer against the competitor’s hunter, and never use a hunter when a farmer will do.
  • I want the training ground for sales.  A lot of companies take fresh sales development reps (SDRs) and promote them directly to salesreps.  While it sometimes works, it’s risky.  Why not have two paths?  One where they can move directly into sales and one where they can move into Customer Success, close 12 deals per quarter instead of 3, hone their skills on incidental expansion, and, if you have the right model, close any non-incidental expansion the salesrep thinks they can handle?
  • I want the Customer Success team to be more sales-y than support-y.  Ironically, when Customer Success is in Sales you often end up with a more support-oriented Customer Success team.  Why?  The salesreps have all the power; they want to keep everything sales-y to themselves, and Customer Success gets relegated to a more support-like role.  It doesn’t have to be this way; it just often is.  In my generally preferred model, Customer Success is renewals- and expansion-focused, not support-focused, and that enables them to add more value to the business.  For example, when a customer is facing a non-support technical challenge (e.g., making a new set of reports), their first instinct will be to sell them professional services, not simply build it for the customer themselves.  To latter is to turn Customer Success into free consulting and support, starting a cycle that only spirals.  The former is keep Customer Success focused on leveraging the resources of the company and its partners to drive adoption, successful achievement of business objectives, renewals, and expansion.

Does this mean a SaaS company can’t have a CRO role if Customer Success does not report into them?  No.  You can call the person chartered with hitting new ARR goals whatever you want to — EVP of Sales, CRO, Santa Claus, Chief Sales Officer, or even President/CRO if you must.  You just shouldn’t have Customer Success report into them.

Personally, I’ve always preferred Sales leaders who like the word “sales” in their title.  That way, as one of my favorites always said, “they’re not surprised when I ask for money.”

# # #

[1] At Salesforce then called Customers for Life.

[2] Corner cases aside and assuming either annual contracts or that ownership is ownership, even if every customer technically isn’t renewing every year.

[3] Ending ARR is usually a far less volatile metric than new ARR.

Stopping the Sales & Marketing Double Drowning

I earned my spending money in high school and partially paid for college by working as a lifeguard and water safety instructor. Working at a lovely suburban country club you don’t make a lot of saves. One day, working from the deep-end chair, I noticed two little kids hanging on a lane line. That was against the rules. I blew my whistle and shouted, “off!”

Still young enough to be obedient (i.e., under 11), the two kids let go of the line. The trouble was they couldn’t swim. Each grabbed the other and they sank to the bottom. “Oh my God,” I thought as I dove off the chair to make the save, “I just provoked a double drowning.”

While that was happily the last actual (and yes, averted) double drowning I have witnessed, I’ve seen a lot of metaphorical ones since. They involve adults, not kids. And it’s always the VP of Sales in a deadly embrace with the VP of Marketing. Sure, it may not be an exactly simultaneous death — sometimes they might leave a few months apart — but make no mistake, in the end they’re both gone and they drowned each other.

How To Recognize the Deadly Embrace

I believe the hardest job in software is the VP of Sales in an early-stage startup. Why? Because almost everything is unknown.

  • Is the product salable?
  • How much will people pay for it?
  • What’s a good lead?
  • Who should we call on?
  • What’s the ideal customer profile?
  • What should we say / message?
  • Who else is being evaluated?
  • What are their strengths/weaknesses?
  • What profile of rep should I hire?
  • How much can they be expected to sell?
  • What tools do they need?
  • Which use-cases should we sell to?
  • What “plays” should we run?

You might argue every startup less then $50M in ARR is still figuring out some of this. Yes, you get product-market fit in the single-digit millions (or not at all). But to get a truly repeatable, debugged sales model takes a lot longer.

This painful period presents a great opportunity for sales and marketing to blow each other up. It all begins with sales signing up for (or being coerced into) an unrealistic number. Then, there aren’t enough leads. Or, if there are, the leads are weak. Or the leads don’t become pipeline. Or pipeline doesn’t close.

At each step one side can easily blame the other.

Sales SaysMarketing Says
There aren’t enough leadsThere are, but they’re all stuck with your “generation Z” SDRs
The SDRs are great, I hired themThe SQL acceptance rate says they are passing garbage to sales.
The SQLs aren’t bad, there just aren’t enough of themYour reps are greasing the SDRs by accepting bad SQLs
We’re not getting 80% of pipeline from marketingWe’re delivering our target of 70% and then some
But the pipeline is low quality, look at the poor close rateThe close rate is poor because of your knuckleheaded sellers
Those knuckleheads all crushed it at my last companyYour derail rate’s insane
Lots of deals in this space end up no-decisionMaybe they derail because we don’t follow-up fast enough
Our message isn’t crisp or consistentOur messaging is fine, the analysts love it
We’re the greatest thing nobody’s ever heard of We’ve got a superior product that your team can’t sell
We’re being out-marketed!We’re being out-sold!

Once this ping-pong match starts, it’s hard to stop. People feel blamed. People get defensive. Anecdotal bloody shirts are waived in front of the organization — e.g., “marketing counted five grad students who visited the booth as MQLs!” or “we lost an opportunity at BigCo because our seller was late for the big meeting!”

With each claim and counter-claim sales and marketing tighten the deadly embrace. Often the struggling CRO is fired for missing too many quarters, guns still blazing as he/she dies. (Or even beyond the grave if they continue to trash the CMO post departure.) Sometimes the besieged CMO quits in anticipation of termination. Heck, I even had one quit after I explicitly told them “I know you’re under attack, but it’s unfair and I’ve got your back.”

Either way, in whatever order, they go down together. Each one mortally wounds the spirit, the confidence, or the pleasure-in-work of the other.

How to Break Out of It

Like real double drownings, it’s hard for one of the participants to do an escape maneuver. The good news is that it’s not hard to know there’s a problem because the mess is clearly visible to the entire organization. Everyone sees the double downing. Heck, employees’ spouses probably even know about it. However, only the CEO can stop it and — trust me — everyone’s waiting for them to do so.

The CEO has four basic options:

  • Take some pressure off. If the primary reason you’re missing plan is because the plan is too aggressive, go to the board and reduce the targets. (Yes, even if it means reducing some expense budget as well.) As Mike Moritz said to me when I started at MarkLogic: “make a plan that you can beat.” Tell them both that you’re taking off the pressure, them them why (because they’re not collaborating), and tell them that you’ve done your part and now it’s time for them to do theirs: collaborate non-defensively to solve problems.
  • Force them to work together. This the old “this shit needs to stop and I’m going to fire one of the two of you, maybe both, if you can’t work together” meeting. A derivation is to put both in a room and tell them not to leave until either they agree to work together or come out with a piece of paper with one name on it (i.e., the one who’s leaving). The key here for them to understand that you are sufficiently committed to ending the bullshit that you are willing to fire one or both of them to end it. In my experience this option tends not to work, I think because each secretly believes they will be the winner if you are forced to choose.
  • Fire one of the participants. This has the effect of rewarding the survivor as the victor. If done too late (before death but after the mortal wound — i.e., after the victor is far along in finding another job), it can still result in the loss of both. To the extent one person clearly picked the fight, my tendency is to want to reward the victim, not the aggressor — but that discounts the possibility the aggressor is either correct and/or more highly skilled. If they are both equally skilled and equally at fault, a rational alternative is to flip a coin and tell them: “I value you both, you are unable to work together, I think you’re equally to blame, so I’m going to flip a coin and fire one of you: heads or tails.” An alternative is to fire one and demote the other — that way it’s very clear to all involved that there was no winner. If fights have winners, you’re incenting fighting.
  • Fire both. I love this option. While it’s not always practical, boy does it send a strong message about collaboration to the rest of the organization: “if you fight, are asked to stop, and you don’t — you’re gone.” Put differently: “I’m not firing them for fighting, I’m firing them for insubordination because I told them not to fight.” Odds are you might lose both anyway so one could argue this is simply a proactive way of dealing with the inevitable.

One of the hardest things for executives is to maintain the balance between healthy cross-functional tension and accountability and unhealthy in-fighting and politics. It’s the CEO’s job to set the tone for collaboration in the company. While Larry Ellison and his disciplines may love “two execs enter, one exec leaves” cage fights as a form of corporate Darwinism, most CEOs prefer a tone of professional collaboration. When that breaks down, weak CEOs get frustrated and complain about their executive team. Strong ones take definitive action to define what is and what isn’t acceptable behavior in the organization and put clear actions behind their words.

Does Enterprise SaaS Need a Same-Store Sales Metric?

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 [1]
  • Retention and expansion rates are about cohorts, but cohorts of customers, not cohorts of salespeople [2]
  • Sales productivity is typically measured as ARR/salesrep which blends new and existing salesreps [3]
  • 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 [4]

So what we need is a cohort-based metric that compares the productivity of reps here today with those here a year ago [5]. Unlike retail, where stores don’t really ramp [6], we need to consider ramping in defining the cohort, and thus define the year-ago cohort to include only fully-ramped reps [6].

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.

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Notes

[1] Billings is a public company SaaS metric and typically a proxy for bookings.

[2] See here for my thoughts on churn

[3] Public companies never release this but most public and private companies track it.

[4] 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.

[5] 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.

[6] 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.

[6] 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.

How to Make and Use a Proper Sales Bookings Productivity and Quota Capacity Model

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 [1].  

Thus, to me, everything needs to begin with a sales productivity model that is Einsteinian in the sense that it is as simple as possible but no simpler.

What does such a model need to take into account?

  • 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 [2].  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 [3].
  • 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 [4], 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 [5]. 

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 [6].  

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.

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Notes

[1] 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.

[2] 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.

[3] 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.

[4] 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.”

[5] 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).

[6] 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.

Why Every Startup Needs an Inverted Demand Generation Funnel, Part II

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.

Other Considerations

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.