Category Archives: Sales

The Most Important Chart for Managing the Pipeline: The Opportunity Histogram

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 [1].
  • High-activity salesreps [2] 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:

oppty histo

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 [3].
  • 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 [4] — 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 [5].

You can download the spreadsheet for this post, here.

# # #

Notes

[1] 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).

[2] 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!

[3] 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.

[4] 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.”

[5] 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.

Do We Have Enough Pipeline? The One Simple Metric Many Folks Forget.

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 [1]
  • Pipeline coverage for the quarter and whether a company has the magical 3x coverage ratio that most require [2]
  • Pipeline with and without the high funnel (i.e., pipeline excluding stage 1 and stage 2 opportunities) [3]
  • Pipeline scrubbing and the process a company uses to keep its pipeline from getting inflated full of junk including, among other things, rolling hairballs.
  • 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 [4].

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 [5]:

  • 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 [6].  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 [7].  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 [8].  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.

# # #

Notes

[1] 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.

[2] 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.

[3] 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.

[4] The High Crime of a company slowing down its own sales cycles!  Never forget the sales adage:  “time kills all deals.”

[5] 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.

[6] The anemic pipeline hidden, on an ARR basis, by (unrealistically) large deal sizes.

[7] And they actually first went to HR seeking advice about what to do, because they didn’t want “rat out” the offending salesrep.

[8] 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.

I’ve Got a Crazy Idea:  How About We Focus on Next-Quarter’s Pipeline?

I’m frankly shocked by how many startups treat pipeline as a monolith.

Sample CMO:  “we’re in great shape because we have a total pipeline of $32M covering a forward-four-quarter (F4Q) sales target of $10M, so 3.2x coverage.  Next slide, please.”

Regardless of your view on the appropriate magic pipeline coverage number (e.g., 2x, 3x, 4x), I’ve got a slew of serious problems with this.  What do I think when someone says this?

“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 [1] 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 [2] 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 [3] 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 [4].

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 [5] 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-Quarter Pipeline
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 [6].

this qtr togo

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” [7], next quarter is effectively eternity from the point of view of most salesreps, so they tend to dump troubled deals in next-quarter [8] 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 [9].)
  • 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 [10] at the same rate – hence the coverage ratio is running in a stable, if somewhat tight, range.

Next-Quarter Pipeline
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:

next qtr pipe

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 [11].

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 [12]

  • 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 [13]

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.

fc next qtr

All-Quarters Pipeline
Finally, let’s close with how I think about all-quarters pipeline.

all qtr

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) [14].
  • 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 [15].  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.

# # #

Notes

[1] Assuming you’re in the first few weeks of the quarter, for now.

[2] Which is usually done using forward four quarters.

[3] And ASC follows a normal distribution.

[4] Typically, they are smaller deals, or deals at smaller companies, or upsells to existing customers.  But they’re out there.

[5] Or, whatever your favorite coverage ratio is.  Debating that is not the point of this post.

[6] Once you build up some history you can use coverage ratios to predict sales as a way of triangulating on the forecast.

[7] 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.

[8] Or sometimes, fourth-quarter which is another popular pipeline dumping ground.  (As is first-quarter next year for the truly crafty.)

[9] 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.

[10] 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.

[11] This depends massively on your specific business (and sales cycle length) so you really need to build up your own history.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

Kellblog’s Greatest Hits 2016-2019 per the Appealie SaaS Awards

I’ll be speaking at the APPEALIE 2019 SaaS Conference and Awards in San Francisco on September 25th and I noticed that in their promotions the folks at APPEALIE had assembled their own Kellblog’s Greatest Hits album from 2016 to 2019, complete with its own cover art.

appealie

When I looked at the posts they picked, I thought they did a good job of identifying the best material, so I thought I’d share their list here.  They also called me “a GOAT software blogger” and after playing around with acronyms for about half an hour — maybe Groove, OpenView, AngelVC, Tunguz? — my younger son swung by and said, “they called you a GOAT?  Cool.  It means greatest of all time.”  Cool, indeed.  Thanks.

Here’s the APPEALIE Kellblog’s Greatest Hits 2016-2019 list:

 

Kellblog's Greatest Hits 2016-2019 per the Appealie SaaS Awards

I’ll be speaking at the APPEALIE 2019 SaaS Conference and Awards in San Francisco on September 25th and I noticed that in their promotions the folks at APPEALIE had assembled their own Kellblog’s Greatest Hits album from 2016 to 2019, complete with its own cover art.
appealie
When I looked at the posts they picked, I thought they did a good job of identifying the best material, so I thought I’d share their list here.  They also called me “a GOAT software blogger” and after playing around with acronyms for about half an hour — maybe Groove, OpenView, AngelVC, Tunguz? — my younger son swung by and said, “they called you a GOAT?  Cool.  It means greatest of all time.”  Cool, indeed.  Thanks.
Here’s the APPEALIE Kellblog’s Greatest Hits 2016-2019 list:

 

Stopping Inception Churn: The Prospective Customer Success Review

I think for many sales-aggressive enterprise SaaS startups, a fair amount of churn actually happens at inception.  For example, back in 2013, shortly after I joined Host Analytics, I discovered that there were a number of deals that sales had signed with customers that our professional services (PS) team had flat out refused to implement.  (Huh?)  Sales being sales, they found partners willing to do the implementations and simply rode over the objections of our quite qualified PS team.

When I asked our generally sales-supportive PS team why they refused to do these implementations, they said, “because there was a 0% chance that the customer could be successful.”  And they, of course, were right.  100% of those customers failed in implementation and 100% of them churned.

I call this “inception churn,” because it’s churn that’s effectively built-in from inception — the customer is sent, along with a partner, on a doomed journey to solve a problem that the system was never designed to solve.  Sales may be in optimistic denial.  Pre-sales consulting knows deep down that there’s a problem, but doesn’t want to admit it — after all, they usually work in the Sales team. Professional services can see the upcoming trainwreck but doesn’t know how to stop it so they are either forced to try and catch the falling anvil or, better yet, duck out and a let partner — particularly a new one who doesn’t know any better — try to do so themselves.

In startups that are largely driven by short-term, sales-oriented metrics, there will always be the temptation to take a high-risk deal today, live to fight another day, and hope that someone can make it work before it renews.  This problem is compounded when customers sign two- or three-year deals [1] because the eventual day of reckoning is pushed into the distant future, perhaps beyond the mean survival expectation of the chief revenue officer (CRO) [2].

Quality startups simply cannot allow these deals to happen:

  • They burn money because you don’t earn back your CAC.  If your customer acquisition cost ratio is 1.5 and your gross margins are 75%, it takes you two years simply to breakeven on the cost of sale.  When a 100-unit customer fails to renew after one year, you spent 175 units [3], receive 100 units, and thus have lost 75 units on the transaction — not even looking at G&A costs.
  • They burn money in professional services.  Let’s say your PS can’t refuse to the implementation.  You take a 100-unit customer, sell them 75 units of PS to do the implementation, probably spend 150 units of PS trying to get the doomed project to succeed, eventually fail, and lose another 75 units in PS.  (And that’s only if they actually pay you for the first 75.)  So on a 100-unit sale, you are now down 150 to 225 units.
  • They destroy your reputation in the market. SaaS startup markets are small.  Even if the eventual TAM is large, the early market is small in the sense that you are probably selling to a close-knit group of professionals, all in the same geography, all doing the same job.  They read the same blogs.  They talk to the same analysts and consultants.  They meet each other at periodic conferences and cocktail parties.  You burn one of these people and they’re going to tell their friends — either via these old-school methods over drinks or via more modern methods such as social media platforms (e.g., Twitter) or software review sites (e.g., G2).
  • They burn out your professional services and customer success teams. Your PS consultants get burned out trying to make the system do something they know it wasn’t designed to do.  Your customer success managers (CSMs) get tired of being handed customers who are DOA (dead on arrival) where there’s virtually zero chance of avoiding churn.
  • They wreck your SaaS metrics and put future financings in danger. These deals drive up your churn rate, reduce your expansion rate, and reduce your customer lifetime value.  If you mix enough of them into an otherwise-healthy SaaS business, it starts looking sick real fast.

So what can we do about all this?  Clearly, some sort of check-and-balance is needed, but what?

  • Pay salespeople on the renewal, so they care if the customer is successful?  Maybe this could work, but most companies want to keep salespeople focused on new sales.
  • Pay the CRO on renewal, so he/she keeps an honest eye on sales and sales management?  This might help, but again, if a CRO is missing new sales targets, he/she is probably in a lot more trouble than missing renewals — especially if he/she can pin the renewal failures on the product, professional services, or partners.
  • Separate the CRO and CCO (Chief Customer Officer) jobs as two independent direct reports to the CEO.  I am a big believer in this because now you have a powerful, independent voice representing customer success and renewals outside of the sales team.  This is a great structure, but it only tells you about the problems after, sometimes quarters or years after, they occur.  You need a process that tells you about them before they occur.

The Prospective Customer Success Review Committee
Detecting and stopping inception churn is hard, because there is so much pressure on new sales in startups and I’m proposing to literally create the normally fictitious “sales prevention team” — which is how sales sometimes refers to corporate in general, making corporate the butt of many jokes.  More precisely, however, I’m saying to create the bad sales prevention team.

To do so, I’m taking an idea from Japanese manufacturing, the Andon Cord, and attaching a committee to it [4].  The Andon Cord is a cord that runs the length of an assembly line that gives the power to anyone working along the line to stop it in order to address problems.  If you see a car where the dashboard is not properly installed, rather than letting it just move down the line, you can pull the cord, stop the line, and get the problem fixed upstream, rather than hoping QA finds it later or shipping a defective product to a customer.

To prevent inception churn, we need two things:

  • A group of people who can look holistically at a high-risk deal and decide if it’s worth taking.  I call that group the Prospective Customer Success Review Committee (the PCSRC).  It should have high-level members from sales, presales, professional services, customer success, and finance.
  • And a means of flagging a deal for review by that committee — that’s the Andon Cord idea.  You need to let everyone who works on deals know that there is a mechanism (e.g., an email list, a field in SFDC) by which they can flag a deal for PCSRC review.  Your typical flaggers will be in either pre-sales or post-sales consulting.

I know there are lots of potential problems with this.  The committee might fail to do its job and yield to pressure to always say yes.  Worse, sales can start to punish those who flag deals such that suspect deals are never flagged and/or that people feel they need an anonymous way to flag them [5].  But these are manageable problems in a healthy culture.

Moreover, simply calling the group together to talk about high-risk deals has two, potentially non-obvious, benefits:

  • In some cases, lower risk alternatives can be proposed and presented back to the customer, to get the deal more into the known success envelope.
  • In other cases, sales will simply stop working on bad deals early, knowing that they’ll likely end up in the PCSRC.  In many ways, I think this the actual success metric — the number of deals that we not only didn’t sign, but where we stopped work early, because we knew the customer had little to no chance of success.

I don’t claim to have either fully deployed or been 100% successful with this concept.  I do know we made great strides in reducing inception churn at Host and I think this was part of it.  But I’m also happy to hear your ideas on either approaching the problem from scratch and/or improving on the basic framework I’ve started here.

# # #

Notes

[1] Especially if they are prepaid.

[2] If CROs last on average only 19 to 26 months, then how much does a potentially struggling CRO actually care about a high-risk deal that’s going to renew in 24 months?

[3] 150 units in S&M to acquire them and 25 units in cost of goods sold to support their operations.

[4] I can’t claim to have gotten this idea working at more than 30-40% at Host.  For example, I’m pretty sure you could find people at the company who didn’t know about the PCSR committee or the Andon Cord idea; i.e., we never got it fully ingrained.  However, we did have success in reducing inception churn and I’m a believer that success in such matters is subtle.  We shouldn’t measure success by how many deals we reject at the meeting, but instead by how much we reduce inception churn by not signing deals that we never should have been signed.

[5] Anonymous can work if it needs to.  But I hope in your company it wouldn’t be required.

Slides From My Presentation at a Private Equity S&M Summit

Just a quick post to share a slide deck I created for a session I did with the top S&M executives at a private equity group’s sales and marketing summit.  We discussed some of my favorite topics, including:

Here are the slides.  Enjoy.