Category Archives: salesops

Using This/Next/All-Quarter Analysis To Understand Your Pipeline

This is the third in a three-post series focused on forecasting and pipeline.  Part I examined triangulation forecasts to improve forecast accuracy and enable better conversations about the forecast.  After a review of pipeline management fundamentals, part II discussed the use of to-go pipeline coverage to provide clarity on how your pipeline is evolving across the weeks of the quarter.  In this, part III, we’ll introduce what I call this/next/all-quarter pipeline analysis as a way of looking at the entire pipeline that is superior to annual or rolling four-quarter pipeline analysis.

Let’s start by unveiling the last block on the sheet we’ve been using the previous two posts.  Here’s the whole thing:

You’ll see two new sections added:  next-quarter pipeline and all-quarters [1] pipeline.  Here’s what we can do when we see all three of them, taken together:

  • We can see slips.  For example, in week 3 while this-quarter pipeline dropped by $3,275K, next-quarter pipeline increased by $2,000K and all-quarters only dropped by $500K.  While there are many moving parts [2], this says to me that pipeline is likely sloshing around between quarters and not being lost.
  • We can see losses.  Similarly, when this-quarter drops, next-quarter is flat, and all-quarters drop, we are probably looking at deals lost from the pipeline [3].
  • We can see wins.  When you add a row at the bottom with quarter-to-date booked new ARR, if that increases, this-quarter pipeline decreases, next-quarter pipeline stays flat, and all-quarters pipeline decreases, we are likely looking at the best way of reducing pipeline:  by winning deals!
  • We can see how we’re building next-quarter’s pipeline.  This keeps us focused on what matters [4].  If you start every quarter with 3.0x coverage you will be fine in the long run without the risk of a tantalizing four-quarter rolling pipeline where overall coverage looks sufficient, but all the closeable deals are always two to four quarters out [5].

Tantalus and his pipeline where all the closeable deals are always two quarters out

  • We can develop a sense how next-quarter pipeline coverage develops over time and get better at forecasting day-1 next-quarter pipeline coverage, which I believe marketing should habitually do [6].
  • We can look at whether we have enough total pipeline to keep our salesreps busy by not just looking at the total dollar volume, but the total count of oppties.  I think this is the simplest and most intuitive way to answer that question.  Typically 15 to 20 all-quarters oppties is the maximum any salesrep can possibly juggle.
  • Finally, there’s nowhere to hide.  Companies that only examine annual or rolling four-quarter pipeline inadvertently turn their 5+ quarter pipeline into a dumping ground full of fake deals, losses positioned as slips, long-term rolling hairballs [7], and oppties used for account squatting.

I hope you’ve enjoyed this three-part series on forecasting and pipeline.  The spreadsheet used in the examples is available here.

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Notes

[1] Apologies for inconsistences in calling this all-quarter vs. all-quarters pipeline.  I may fix it at some point, but first things first.  Ditto for the inconsistency on this-quarter vs. current-quarter.

[2] You can and should have your salesops leader do the deeper analysis of inflows (including new pipegen) and outflows, but I love the first-order simplicity of saying, “this-quarter dropped by $800K, next-quarter increased by $800K and all-quarters was flat, ergo we are probably sloshing” or “this-quarter dropped by $1M, next-quarter was flat, and all-quarters dropped by $1M, so we probably lost $1M worth of deals.”

[3] Lost here in the broad sense meaning deal lost or no decision (aka, derail).  In the former case, someone else wins the deal; in the latter case, no one does.

[4] How do you make 32 quarters in row?  One at a time.

[5] Tantalus was a figure in Greek mythology, famous for his punishment:  standing for eternity in a pool of water below a fruit tree where each time he ducked to drink the water it would recede and each time he reached for a fruit it was just beyond his grasp.

[6] Even though most companies have four different pipeline sources (marketing/inbound, SDR/outbound, sales/outbound, and partners), marketing should, by default, consider themselves the quarterback of the pipeline as they are usually the majority pipeline source and the most able to take corrective actions.

[7] By my definition a normal rolling hairball always sits in this quarter’s pipeline and slips one quarter every quarter.  A long-term rolling hairball is thus one that sits just beyond your pipeline opportunity scrutiny window (e.g., 5 quarters out) and slips one quarter every quarter.

 

Using To-Go Coverage to Better Understand Pipeline and Improve Forecasting

This is the second in a three-part series focused on forecasting and pipeline.  In part I, we examined triangulation forecasts with a detailed example.  In this, part II, we’ll discuss to-go pipeline coverage, specifically using it in conjunction with what we covered in part I.  In part III, we’ll look at this/next/all-quarter pipeline analysis as a simple way to see what’s happening overall with your pipeline.

Pipeline coverage is a simple enough notion:  take the pipeline in play and divide it by the target and get a coverage ratio.  Most folks say it should be around 3.0, which isn’t a bad rule of thumb.

Before diving in further, let’s quickly remind ourselves of the definition of pipeline:

Pipeline for a period is the sum of the value of all opportunities with a close date in that period.

This begs questions around definitions for opportunity, value, and close date which I won’t review here, but you can find discussed here.  The most common mistakes I see thinking about the pipeline are:

  • Turning 3.0x into a self-fulfilling prophecy by bludgeoning reps until they have 3.0x coverage, instead of using coverage as an unmanaged indicator
  • Not periodically scrubbing the pipeline according to a defined process and rules, deluding yourself into thinking “we’re always scrubbing the pipeline” (which usually means you never are).
  • Applying hidden filters to the pipeline, such as “oh, sorry, when we say pipeline around here we mean stage-4+ pipeline.”  Thus executives often don’t even understand what they’re analyzing and upstream stages turn into pipeline landfills full of junk opportunities that are left unmanaged.
  • Pausing sales hiring until the pipeline builds, effectively confusing cause and effect in how the pipeline gets built [1].
  • Creating opportunities with placeholder values that pollute the pipeline with fake news [1A], instead of creating them with $0 value until a salesrep socializes price with the customer [2].
  • Conflating milestone-based and cohort-based conversion rates in analyzing the pipeline.
  • Doing analysis primarily on either an annual or rolling four-quarter pipeline, instead of focusing first on this-quarter and next-quarter pipeline.
  • Judging the size of the all-quarter pipeline by looking at dollar value instead of opportunity count and the distribution of oppties across reps [2A].

In this post, I’ll discuss another common mistake, which is not analyzing pipeline on a to-go basis within a quarter.

The idea is simple:

  • Many folks run around thinking, “we need 3.0x pipeline coverage at all times!”  This is ambiguous and begs the questions “of what?” and “when?” [3]
  • With a bit more rigor you can get people thinking, “we need to start the quarter with 3.0x pipeline coverage” which is not a bad rule of thumb.
  • With even a bit more rigor that you can get people thinking, “at all times during the quarter I’d like to have 3.0x coverage of what I have left to sell to hit plan.” [4]

And that is the concept of to-go pipeline coverage [5].  Let’s look at the spreadsheet in the prior post with a new to-go coverage block and see what else we can glean.

Looking at this, I observe:

  • We started this quarter with $12,500 in pipeline and a pretty healthy 3.2x coverage ratio.
  • We started last quarter in a tighter position at 2.8x and we are running behind plan on the year [6].
  • We have been bleeding off pipeline faster than we have been closing business.  To-go coverage has dropped from 3.2x to 2.2x during the first 9 weeks of the quarter.  Not good.  [7]
  • I can easily reverse engineer that we’ve sold only $750K in New ARR to date [8], which is also not good.
  • There was a big drop in the pipeline in week 3 which makes me start to wonder what the gray shading means.

The gray shading is there to remind us that sales management is supposed to scrub the pipeline in weeks 2, 5, and 8 so that the pipeline data presented in weeks 3, 6, and 9 is scrubbed.  The benefits of this are:

  • It eliminates the “always scrubbing means never scrubbing” problem.
  • It draws a deadline for how long sales has to clean up after the end of a quarter:  the end of week 2.  That’s enough time to close out the quarter, take a few days rest, and then get back at it.
  • It provides a basis for snapshotting analytics.  Because pipeline conversion rates vary by week things can get confusing fast.  Thus, to keep it simple I base a lot of my pipeline metrics on week 3 snapshots (e.g., week 3 pipeline conversion rate) [9]
  • It provides an easy way to see if the scrub was actually done.  If the pipeline is flat in weeks 3, 6, and 9, I’m wondering if anyone is scrubbing anything.
  • It lets you see how dirty things got.  In this example, things were pretty dirty:  we bled off $3,275K in pipeline during the week 2 scrub which I would not be happy about.

Thus far, while this quarter is not looking good for SaaSCo, I can’t tell what happened to all that pipeline and what that means for the future.  That’s the subject of the last post in this three-part series.

A link to the spreadsheet I used in the example is here.

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Notes

[1]  In enterprise SaaS at least, you should look at it the other way around:  you don’t build pipeline and then hire reps to sell it, you hire reps and then they build the pipeline, as the linked post discusses.

[1A]  The same is true of close dates.  For example, if you create opportunities with a close date that is 18+ months out, they can always be moved into the more current pipeline.  If you create them 9 months out and automatically assign a $150K value to each, you can end up with a lot air (or fake news/data) in your pipeline.

[2]  For benchmarking purposes, this creates the need for “implied pipeline” which replaces the $0 with a segment-appropriate average sales price (ASP) as most people tend to create oppties with placeholder values.  I’d rather see the “real” pipeline and then inflate it to “implied pipeline” — plus it’s hard to know if $150K is assigned to an oppty as a placeholder that hasn’t been changed or if that’s the real value assigned by the salesrep.

[2A] If you create oppties with a placeholder value then dollar pipeline is a proxy for the oppty count, but a far less intuitive one — e.g., how much dollar volume of pipeline can a rep handle?  Dunno.  How many oppties can they work on effectively at one time?  Maybe 15-20, tops.

[3] “Of what” meaning of what number?  If you’re looking at all-quarters pipeline you may have oppties that are 4, 6, or 8+ quarters out (depending on your rules) and you most certainly don’t have an operating plan number that you’re trying to cover, nor is coverage even meaningful so far in advance.  “When” means when in the quarter?  3.0x plan coverage makes sense on day 1; it makes no sense on day 50.

[4] As it turns out, 3.0x to-go coverage is likely an excessively high bar as you get further into the quarter.  For example, by week 12, the only deals still forecast within the quarter should be very high quality.  So the rule of thumb is always 3.0x, but you can and should watch how it evolves at your firm as you get close to quarter’s end.

[5]  In times when the forecast is materially different from the plan, separating the concepts of to-go to forecast and to-go to plan can be useful.  But, by default, to-go should mean to-go to plan.

[6] I know this from the extra columns presented in the screenshot from the same sheet in the previous post.  We started this quarter at 96% of the ARR plan and while the never explicitly lists our prior-quarter plan performance, it seems a safe guess.

[7]  If to-go coverage increases, we are closing business faster than we are losing it.  If to-go coverage decreases we are “losing” (broadly defined as slip, lost, no decision) business faster than we are closing it.  If the ratio remains constant we are closing business at the same ratio as we started the quarter at.

[8]  A good sheet will list this explicitly, but you can calculate it pretty fast.  If you have a pipeline of $7,000, a plan of $3,900, and coverage of 2.2x then:  7,000/2.2 (rounded) = 3,150 to go, with a plan of 3,900 means you have sold 750.

[9] An important metric that can be used as an additional triangulation forecast and is New ARR / Week 3 Pipeline.

 

Using Triangulation Forecasts For Improved Forecast Accuracy and Better Conversations

Ever been in this meeting?

CEO:  What’s the forecast?
CRO:  Same as before, $3,400K.
Director 1:  How do you feel about it?
CRO:  Good.
Director 2:  Where will we really land?
CRO:  $3,400K.  That’s why that’s the forecast.
Director 1:  But best case, where do we land?
CRO:  Best case, $3,800K.
Director 2:  How do you define best case?
CRO:  If the stars align.

Not very productive, is it?

I’ve already blogged about one way to solve this problem:  encouraging your CRO think probabilistically about the forecast.  But that’s a big ask.  It’s not easy to change how sales leaders think, and it’s not always the right time to ask.  So, somewhat independent of that, in this series I’ll introduce three concepts that help ensure that we have better conversations about the forecast and ultimately forecast better as a result:  triangulation forecasts, to-go pipeline coverage, and this/next/all-quarter pipeline analysis.  In this post, we’ll cover triangulation forecasts.

Triangulation Forecasts

The simplest way to have better conversations about the forecast is to have more than one forecast to discuss.  Towards that end, much as we might take three or four bearings to triangulate our position when we’re lost in the backcountry, let’s look at three or four bearings to triangulate our position on the new annual recurring revenue (ARR) forecast for the quarter.

In this example [1] we track the forecast and its evolution along with some important context such as the plan and our actuals from the previous and year-ago quarters.  We’ve placed the New ARR forecast in its leaky bucket context [2], in bold so it stands out.  Just scanning across the New ARR row, we can see a few things:

  • We sold $3,000K in New ARR last quarter, $2,850K last year, and the plan for this quarter is $3,900K.
  • The CRO is currently forecasting $3,400K, or 87% of the New ARR plan.  This is not great.
  • The CRO’s forecast has been on a steady decline since week 3, from its high of $3,800K.  This makes me nervous.
  • The CRO is likely pressuring the VP of Customer Success to cut the churn forecast to protect Net New ARR [3].
  • Our growth is well below planned growth of 37% and decelerating [4].

I’m always impressed with how much information you can extract from that top block alone if you’re used to looking at it.  But can we make it better?  Can we enable much more interesting conversations?  Yes.  Look at the second block, which includes four rows:

  • The sum of the sales reps’ forecasts [5]
  • The sum of the sales managers’ forecasts [6]
  • The stage-weighted expected value (EV) of the pipeline [7] [8]
  • The forecast category-weighted expected value of the pipeline [9]

Each of these tells you something different.

  • The rep-level forecast tells you what you’d sell if every rep hit their current forecast.  It tends to be optimistic, as reps tend to be optimistic.
  • The manager-level forecast tells you how much we’d sell if every CRO direct report hit their forecast.  This tends to be the most accurate [10] in my experience.
  • The stage-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each stage. A $1M pipeline consisting of 10 stage 2 $100K oppties has a much lower EV than a $1M pipeline with 10 stage 5 $100K oppties — even though they are both “$1M pipelines.”
  • The forecast category-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each forecast category, such as commit, forecast, or upside.

These triangulation forecasts provide different bearings that can help you understand your pipeline better, know where to focus your efforts, and improve the accuracy of predicting where you’ll land.

For example, if the rep- and manager-level forecasts are well below the CRO’s, it’s often because the CRO knows about some big deal they can pull forward to make up any gap.  Or, more sinisterly, because the CRO’s expense budget is automatically cut to preserve a target operating margin and thus they are choosing to be “upside down” rather face an immediate expense cut [11].

If the stage-weighted forecast is much lower than the others, it indicates that while we may have the right volume of pipeline that it’s not far enough along in its evolution, and ergo we should focus on velocity.

Now, looking at our sample data, let’s make some observations about the state of the quarter at SaaSCo.

  • The reps are calling $3,400K vs. a $3,900K plan and their aggregate forecast has been fairly consistently deteriorating.  Not good.
  • The managers, who we might notice called last quarter nearly perfectly ($2,975K vs. $3,000K) have pretty consistently been calling $3,000K, or $900K below plan.  Worrisome.
  • The stage-weighted EV was pessimistic last quarter ($2,500K vs. $3,000K) and may need updated probabilities.  That said, it’s been consistently predicting around $2,600K which, if it’s 20% low (like it was last quarter), it suggests a result of $3,240K [12].
  • The forecast category-weighted expected value, which was a perfect predictor last quarter, is calling $2,950K.  Note that it’s jumped up from earlier in the quarter, which we’ll get to later.

Just by these numbers, if I were running SaaSCo I’d be thinking that we’re going to land between $2,800K and $3,200K [13].  But remember our goal here:  to have better conversations about the forecast.  What questions might I ask the CRO looking at this data?

  • Why are you upside-down relative to your manager’s forecast?
  • In other quarters was the manager-level forecast the most accurate, and if so, why you are not heeding it better now?
  • Why is the stage-weighted forecast calling such a low number?
  • What’s happened since week 5 such that the reps have dropped their aggregate forecast by over $600K?
  • Why is the churn forecast going down?  Was it too high to begin with, are we getting positive information on deals, or are we pressuring Customer Success to help close the gap?
  • What big/lumpy deals are in these numbers that could lead to large positive or negative surprises?
  • Why has your forecast been moving so much across the quarter?  Just 5 weeks ago you were calling $3,800K and how you’re calling $3,400K and headed in the wrong direction?
  • Have you cut your forecast sufficiently to handle additional bad news, or should I expect it to go down again next week?
  • If so, why are you not following the fairly standard rule that when you must cut your forecast you cut it deeply enough so your next move is up?  You’ve broken that rule four times this quarter.

In our next post in the series we’ll discuss to-go pipeline coverage.  A link to the spreadsheet used to the example is here.

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Notes

[1] This is the top of the weekly sheet I recommend CEOs to start their weekly staff meeting.

[2] A SaaS company is conceptualized as a leaky bucket of ARR.

[3] I cheated and look one row down to see the churn forecast was $500K in weeks 1-6 and only started coming down (i.e., improving) as the CRO continued to cut their New ARR forecast.  This makes me suspicious, particularly if the VP of Customer Success reports to the CRO.

[4] I cheated and looked one row up to see starting ARR growing at 58% which is not going to sustain if New ARR is only growing at ~20%.  I also had to calculate planned growth (3900/2850 = 1.37) as it’s not done for me on the sheet.

[5] Assumes a world where managers do not forecast for their reps and/or otherwise cajole reps into forecasting what the manager thinks is appropriate, instead preferring for managers to make their own forecast, loosely coupling rep-level and the manager-level forecasts.

[6]  Typically, the sum of the forecasts from the CRO’s direct reports.  An equally, if perhaps not more, interesting measure would be the sum of the first-line managers’ forecasts.

[7] Expected value is math-speak for probability * value.  For example, if we had one $100K oppty with a 20% close probability, then its expected value would be $100K * 0.2 = $20K.

[8] A stage-weighted expected value of the (current quarter) pipeline is calculated by summing the expected value of each opportunity in the pipeline, using probabilities assigned to each stage.  For example, if we had only three stages (e.g., prospect, short-list, and vendor of choice) and assigned a probability to each (e.g., 10%, 30%, 70%) and then multiplied the new ARR value of each oppty by its corresponding probability and summed them, then we would have the stage-weighted expected value of the pipeline.  Note that in a more advanced world those probabilities are week-specific (and, due to quarterly seasonality, maybe week-within-quarter specific) but we’ll ignore that here for now.  Typically, one way I sidestep some of that hassle is to focus my quarterly analytics by snapshotting week 3, creating in effect week 3 conversion rates which I know will work better earlier in the quarter than later.  In the real world, these are often eyeballed initially and then calculated from regressions later on — i.e., in the last 8 quarters, what % of week 3, stage 2 oppties closed?

[9]  The forecast category-weighted expected value of the pipeline is the same the stage-weighted one, except instead of using stage we use forecast category as the basis for the calculation.  For example, if we have forecast categories of upside, forecast, commit we might assign probabilities of 0.3, 0.7, and 0.9 to each oppty in that respective category.

[10] Sometimes embarrassingly so for the CRO whose forecast thus ends up a mathematical negative value-add!

[11] This is not a great practice IMHO and thus CEOs should not inadvertently incent inflated forecasts by hard-coding expense cuts to the forecast.

[12] The point being there are two ways to fix this problem.  One is to revise the probabilities via regression.  The other is to apply a correction factor to the calculated result.  (Methods with consistent errors are good predictors that are just miscalibrated.)

[13]  In what I’d consider a 80% confidence interval — i.e., 10% chance we’re below $2,800K and 10% chance we’re above $3,200K.

What To Do When You Need Pipeline in a Hurry

It’s that time of year, I suppose.  You’ve hopefully approved your 2021 operating plan by now — even if you’re on an increasingly popular 1/31 fiscal year end.  You’ve signed up for some big numbers to meet your aggressive goals (and fund those aggressive spending plans).  And now you might well be thinking one thing:

“Oh shit, we need some pipeline.  Fast.”

To really help you — in the long-term — we’ll need to have a stern talking to about driver-based planning, sales capacity models (particularly if you’re upside-down [1] on sales capacity), inverted funnel models to calculate the demandgen budget, and time-based closed rates to forecast conversion from your existing pipeline (and, I’ve increasingly seen, conversion from to-be-generated pipeline [2]).

And we’ll also need to review the seven words Mike Moritz said to me when I started as CEO of MarkLogic:  “make a plan that you can beat.”

But, I hear you thinking:  that all sounds great and I’m sure I should do it one day — but right now I have a problem.  I need some pipeline, fast.

Got it.  So here are three high-level things you need to do:

  1. Declare general quarters — all hands to battle stations.  You should never waste a good crisis, so call an all-hands meeting, start it with this audio file, and tell everyone you want them working on the problem.  You want zero complacency [3] or fatalism:  we don’t need people cueing the quartet to play Nearer My God To Thee [3a] when there are still lots of things we can do to affect the outcome.
  2. Focus on winning the opportunities you can win.  You think you need pipeline, but what you actually need is the new ARR that comes from it.  Let’s not forget that.  In math terms, we’re going to need high to record-high conversion of the opportunities (oppties) that are in the pipeline today.  So let’s put sales and executive management attention on identifying the winnable oppties and fighting like never before to win them — including potentially re-assigning your best oppties to your best reps [4].
  3. Focus on finding new opportunities that move fast.  Remember that nine-month sales cycle is an average; some opportunities close a lot faster.  Expansion oppties tend to move a lot faster than new logo oppties.  SMB oppties tend to move faster than enterprise ones.  Get salesops to figure out which ones move faster for you — remember you don’t need just any pipeline, you need fast-moving (and high-converting) pipeline.

In addition, if you’re not doing it already, you need marketing to start forecasting next-quarter’s day-one pipeline as of about week 3 of the current quarter, so we can increase our lead time on finding out about these problems next time.

Now, let’s dive a bit deeper into ways to accelerate existing pipeline and how to generate new, fast-moving pipeline when you need some more.

Pipeline Acceleration Tactics
Here is a list of common pipeline patterns and how you can use them and/or workaround them to accelerate your pipeline.

  • Expansion pipeline moves faster than new logo pipeline.  So have AEs, CSMs, or SDRs contact existing customers to discuss expansion opportunities.
  • It’s easier to accelerate planned expansions than create new ones.  Look at out-quarter expansion pipeline and have AEs reach out to customers to discuss moving them forward and/or offering incentives to do so.
  • Partner-sourced pipeline usually moves faster than marketing- or sales-sourced pipeline.  It also typically closes at a higher rate.  Now is a great time to sit down with partners to review opportunities and see what can be accelerated and what incentives you can offer them to help out.
  • Proofs of concept (POCs) stall oppties in the pipeline.  To remove them from your sales cycle try to substitute highly relevant customer references as alternative proof.  It’s a win/win:  you get your deal faster and the customer gets the benefits of your system faster.  Alternatively, reduce the customer’s need for up-front proof by offering a back-end guarantee [5].  Either way, we might be able to cut 90+ days out of your sales cycle.
  • Reheated, old pipeline often moves faster than new.  I’ve often quipped that the best patch in the company is the no-decision pile [6].  Now is a great time to have AEs and SDRs call up no-decision oppties.  “So, whatever happened with that evaluation you were doing?”  Hey, while we’re at it, let’s call up lost oppties as well.  “So, did you end up buying from Badco?  How’d that work out?”  Both types of reheated oppties have the potential to move faster than net new ones.
  • SDRs can delay entry into the pipeline.  We love our SDRs and they’re great for funnel optimization when times are good.  But when times are tough, selectively cut them out of the loop [7].  For example, make a rule that says for accounts of size X (or on list Y), when we get a contact with title Z, pass them directly to the salesrep.  Not only might you accelerate pipeline entry by a week or two, but the AE will likely do a better job in discovery.
  • Legal can stall you out on the two-yard line.  Get your legal team involved in your red zone offense by creating a fast-turn version of your contract that contains only your minimum required terms.  Then inform the customer that you’re giving them toned-down paperwork and incent them to turn quickly with you on signing it [8].

Techniques to Generate New, Fast-Moving Pipeline
When nothing other than net new pipeline will do, then here are some things you can do:

  • Run marketing campaigns to find existing evaluations.  If you can’t make your own party, then why not sneak into someone else’s?  Run a webinar entitled, “How to Evaluate a Blah” or “Five Things to Look for in a Blah.”  Record and transcribe it to get draft 1 of an e-book you can use as a gated asset.
  • Use search advertising to find existing evaluations.  Buy competitive search terms (brand names), evaluation-related search terms (“how to evaluate”), comparison search terms (e.g., “Gong vs. Chorus,” “Oracle alternatives”), or late-funnel search terms (e.g., “Clari pricing”).
  • Look for warm accounts, not just warm contacts.  Sometimes you can see more if you step back a bit.  Instead of looking at the lead/contact level, do an analysis of which accounts have high levels of activity across all their contacts.  That might be a good clue there’s an evaluation happening or starting.
  • Buy intent data. Several vendors — including 6Sense, Bombora, Demandbase, G2, TechTarget, and Zoominfo — look for data that signals companies are investigating given product categories.  Let someone else do the company-finding for you and then market to (and/or SDR outbound call) them.
  • Buy meetings.  While I’ve always heard mixed reviews about appointment-setting firms, I also know they’re a go-to resource when you’re in trouble — particularly if you’re bottlenecked up-funnel in marketing or SDRs.  Consider a service like Televerde or By Appointment Only.  While these vendors started out in appointment-setting, both have broadened into more full-service demand generation that can help you in many ways.
  • Stalk old customers in new jobs.  Applications like UserGems let you track customers as they change jobs.  What could be faster than selling an existing happy customer when they’re in a new position?  It won’t hit every time (e.g., if they already have and are happy with another system), but they’re certainly leads that can turn into fast-moving pipeline.  You can do roughly the same thing yourself manually with LinkedIn Sales Navigator.
  • Do LinkedIn targeted advertising.   I’m always surprised how many colleagues say LinkedIn doesn’t work that well despite its superior targeting abilities.  Perhaps that’s like anglers saying the “fishing is OK” regardless of  the action.  If you know who to target and think that target can move fast, then go for it.
  • Call blitzes.  Remember we said to never waste a good crisis.  It’s a great time to set up dedicated call blitzes to prospects or existing customers.  Just make sure we know who’s blitzing whom so the same person doesn’t get hit by an AE, an SDR, and a CSM all at once.
  • Contests and prizes.  Finally, why not make it fun?!  Nothing gets the sales blood flowing like competition and incentives.

Hopefully these ideas stimulated some thoughts to help you get the pipeline you need.  And, even more hopefully, realize that we should build many of these now-crisis activities as healthy habits going forward.

# # #

Notes
[1] Meaning that your plan number is larger than your sales productivity capacity.  An undesirable, but certainly not unheard of, situation.

[2] As I’m increasingly seeing time-based closed rates used, something to my surprise.  I’d really created the technique for short- to mid-term gap analysis.  I generally make an marketing budget purely off an inverted funnel model.  But that said, using time-based closed rates by pipeline source would be more accurate.

[3] If for no other reason to avoid the common fallacious complacency of “well, with a nine-month sales cycle, if we’re short of pipeline now there’s nothing we can do, so let’s just accept that we’re going to hit the iceberg.

[3a] While I make light of it in the post, it’s actually both an amazing and touching story.  “Sometime around 2:10 a.m. as the Titanic began settling more quickly into the icy North Altantic, the sounds of ragtime, familiar dance tunes and popular waltzes that had floated reassuringly across her decks suddenly stopped as Bandmaster Wallace Hartley tapped his bow against his violin. Hartley and his musicians, all wearing their lifebelts now, were standing back at the base of the second funnel, on the roof of the First Class Lounge, where they had been playing for the better part of an hour. There were a few moments of silence, then the solemn strains of the hymn “Nearer My God to Thee” began drifting across the water. It was with a perhaps unintended irony that Hartley chose a hymn that pleaded for the mercy of the Almighty, as the ultimate material conceit of the Edwardian Age, the ship that “God Himself couldn’t sink,” foundered beneath his feet.”  Hartley concluded in saying, “Gentlemen, it has been a privilege playing with you tonight.”

[4] Most compensation plans allow midstream territory changes and while moving oppties from bad reps to good reps cuts against the grain for most sales managers, well, we are in an emergency, andd we all know that the odds of an oppty closing are highly related to who’s working on it.  Perhaps soften the sting by uplifting and then splitting the quota.  Or just fire the bad rep.  But win the deal.

[5] Introduce a 90- or 120-day acceptance clause.  This will likely have accounting and/or bookings policy ramifications, but we are in an emergency.  Better to hit your target with a few customers on acceptance (especially if you’re sure you can deliver against the criteria) than to miss.

[6] That is, the oppties that were marked by their owners as neither won nor lost, but no decision.  Sometimes also called derailed oppties.  If you have discipline about reason codes you can find the right ones even faster.

[7] Perhaps using the freed-up time to prospect within the installed base, if your CSMs are not salesy.  Or doing longer-shot outbound into named accounts.

[8] I’m a little dusty legally, but the ultimate form of this was a clickwrap which, in a pinch, was sometimes used (with the consent of the customer) to work around the customer’s oft-bottlenecked legal department and get a baseline agreement in place that can later be revised or replaced.

The Holy Grail of Enterprise Sales: Proving a Repeatable Sales Process

(This is the second in a three-part restructuring and build-out of a previous post.  See note [1] for details.)

In the prior post we introduced repeatable sales process as the Holy Grail of enterprise software sales and, unlike some who toss the term around rather casually, we defined a repeatable sales process as meaning you have six things:

  1. Standard hiring profile
  2. Standard onboarding program
  3. Standard support ratios
  4. Standard patch
  5. Standard kit
  6. Standard sales methodology

The point of this, of course, is to demonstrate that given these six standard elements you can consistently deliver a desirable, standard result.

The surprisingly elusive question is then, how to measure that?

  • Making plan?  This should be a necessary but not sufficient condition for proving repeatability.  As we’ll see below, you can make plan in healthy as well as unhealthy ways (e.g., off a small number of reps, off disproportionate expansion and weak new logo sales).
  • Realizing some percentage of your sales capacity?  I love this — and it’s quite useful if you’ve just lost or cut a big chunk of your salesforce and are ergo in the midst of a ramp reset — but it doesn’t prove repeatability because you can achieve it in both good and bad ways [2].
  • Having 80% of your salesreps at 100%+ of quota?  While I think percent of reps hitting quota is the right way to look at things, I think 80% at 100% is the wrong bar.

Why is defaulting to 80% of reps at 100%+ of quota the wrong bar?

  • The attainment percentage should vary as function of business model: with a velocity model, monthly quotas, and a $25K ARR average sales price (ASP), it’s a lot more applicable than with an enterprise model, annual quotas, and a $300K ASP.
  • 80% at 100%+ means you beat plan even if no one overperforms [3] – and that hopefully rarely happens.
  • There is a difference between annual and quarterly performance, so while 80% at 100% might be reasonable in some cases on an annual basis, on a quarterly basis it might be more like 50% — see the spreadsheet below for an example.
  • The reality of enterprise software is that performance is way more volatile than you might like it to be when you’re sitting in the board room
  • When we’re looking at overall productivity we might look at the entire salesforce, but when we’re looking at repeatability we should look at recently hired cohorts. Does 80% of your third-year reps at quota tell you as much about repeatability – and the presumed performance of new hires – as 80% of your first-year reps cohort?

Long story short, in enterprise software, I’d say 80% of salesreps at 80% of quota is healthy, providing the company is making plan.  I’d look at the most recent one-year and two-year cohorts more than the overall salesforce.  Most importantly, to limit survivor bias, I’d look at the attrition rate on each cohort and hope for nothing more than 20%/year.  What good is 80% at 80% of quota if 50% of the salesreps flamed out in the first year?  Tools like my salesrep ramp chart help with this analysis.

Just to make the point visceral, I’ll finish by showing a spreadsheet with a concrete example of what it looks like to make plan in a healthy vs. unhealthy way, and demonstrate that setting the bar at 80% of reps at 100% of quota is generally not realistic (particularly in a world of over-assignment).

If you look at the analysis near the bottom, you see the healthy company lands at 105% of plan, with 80% of reps at 80%+ of quota, and with only 40% of reps at 100%+ of quota.  The unhealthy company produces the same sales — landing the company at 105% of plan — but due to a more skewed distribution of performance gets there with only 47% of reps at 80%+ and only a mere 20% at 100%+.

In our final post in this series, we’ll ask the question:  is repeatability enough?

# # #

Notes

[1] I have a bad habit, which I’ve been slowly overcoming, to accidently put real meat on one topic into an aside of a post on a different one.  After reading the original post, I realized that I’d buried the definition of a repeatable sales model and the tests for having one into a post that was really about applying CMMI to the sales model.  Ergo, as my penance, as a service to future readers, and to help my SEO, I am decomposing that post into three parts and elaborating on it during the restructuring process.

[2] Unless you’ve had either late hiring or unexpected attrition, 80% of your notional sales capacity should roughly be your operating plan targets.  So this is point is normally subtly equivalent to the prior one.

[3] Per the prior point, the typical over-assignment cushion is around 20%

The Holy Grail of Enterprise Sales: Defining the Repeatable Sales Process

(This is the first in a three-part restructuring and build-out of the prior post.  See note [1] for details.)

The number one question go-to-market question in any enterprise software startup is:  “do you have a repeatable sales process?” or, in more contemporary Silicon Valley patois, “do you have a repeatable sales motion?”

It’s one of the key milestones in startup evolution, which proceed roughly like:

  • Do you have a concept?
  • Do you have a working product?
  • Do you have any customer traction (e.g., $1M in ARR)?
  • Have you established product-market fit?
  • Do you have a repeatable sales process?

Now, when pressed to define “repeatable sales process,” I suspect many of those asking might reply along the same lines as the US Supreme Court in defining pornography:

“I shall not today attempt further to define the kinds of material I understand to be embraced… but I know it when I see it …”

That is, in my estimation, a lot of people throw the term around without defining it, so in the Kelloggian spirit of rigor, I thought I’d offer my definition:

A repeatable sales process means you have six things:

  1. Standard hiring profile
  2. Standard onboarding program
  3. Standard support ratios
  4. Standard patch
  5. Standard kit
  6. Standard sales methodology

All of which contribute to delivering a desirable, standard result.  Let’s take a deeper look at each:

  1. You hire salesreps with a standard hiring profile, including items such as years of experience, prior target employers or spaces, requisite skills, and personality assessments (e.g., DiSC, Hogan, CCAT).
  2. You give them a standard onboarding program, typically built by a dedicated director of sales productivity, using industry best practices, one to three weeks in length, and accompanied by ongoing clinics.
  3. You have standard support ratios (e.g., each rep gets 1/2 of a sales consultant, 1/3 of an SDR, and 1/6 of a sales manager).  As you grow, your sales model should also use ratios to staff more indirect forms of support such as alliances, salesops, and sales productivity.
  4. You have a standard patch (territory), and a method for creating one, where the rep can be successful.  This is typically a quantitative exercise done by salesops and ideally is accompanied by a patch-warming program [2] such that new reps don’t inherit cold patches.
  5. You have standard kit including tools such as collateral, presentations, demos, templates.  I strongly prefer fewer, better deliverables that reps actually know how to use to the more common deep piles of tools that make marketing feel productive, but that are misunderstood by sales and ineffective.
  6. You have a standard sales methodology that includes how you define and execute the sales process.  These include programs ranging from the boutique (e.g., Selling through Curiosity) to the mainstream (e.g., Force Management) to the classic (e.g., Customer-Centric Selling) and many more.  The purpose of these programs is two-fold:  to standardize language and process across the organization and to remind sales — in a technology feature-driven world — that customers buy products as solutions to problems, i.e., they buy 1/4″ holes, not 1/4″ bits.

And, most important, you can demonstrate that all of the above is delivering some desirable standard result, which will be the topic of the next post.

# # #

Notes

[1] I have a bad habit, which I’ve been slowly overcoming, to accidently put real meat on one topic into an aside of a post on a different one.  My favorite example:  it took me ~15 years to create a post on my marketing credo (marketing exists to make sales easier) despite mentioning it in passing in numerous posts.  After reading the prior post, I realized that I’d buried the definition of a repeatable sales model and the tests for having one into a post that was really about applying CMMI to the sales model.  Ergo, as my penance, as a service to future readers, and to help my SEO, I am decomposing that post into three parts and elaborating on it during the restructuring process.

[2] I think of patch-warming as field marketing for fallow patches.  Much as field marketing works to help existing reps in colder patches, why can’t we apply the same concepts to patches that will soon be occupied?  This is an important, yet often completely overlooked, aspect of reducing rep ramping time.

The Holy Grail of the Repeatable Sales Process: Is Repeatability Enough?

Most of us are familiar with Mark Leslie’s classic Sales Learning Curve and its implications for building the early salesforce at an enterprise startup.  In short, it argues that too many startups put “the pedal to the metal” on sales hiring too early – before they have enough knowledge, process, and infrastructure in place – and end up with a pattern that looks like:

  1. Hire 1 salesrep, which seems to be working so we …
  2. Hire 2 more salesreps, which seems to be mostly working so we think “Eureka!” and we …
  3. Hire 10 more salesreps overnight

With the result that 8 of the 10 salesreps hired in phase three flame out within a year.  You end up missing numbers and hiring a new VP of Sales who inherits a smoldering rubble of a salesforce which they must rebuild, nearly from scratch.  The cost:  $3-5M of wasted capital [1] and, more importantly, 12-18 months of lost time.

But let’s say you heed Leslie’s lessons and get through this phase.  Once you’re up to 20-30 reps, you don’t just need sales to be working, you need to prove that you have attained the Holy Grail of startup sales:  a repeatable sales process.

Everyone has their own definition of what “repeatable sales process” means and how to measure if you’ve attained it.  Here are mine.

A repeatable sales process means:

  1. You hire salesreps with a standard hiring profile
  2. You give them a standard onboarding program
  3. You have standard support ratios (e.g., each rep gets 1/2 of a sales consultant, 1/3 of a sales development rep (SDR), and 1/6 of a sales manager)
  4. You have a standard patch (and a method for creating one) where the rep can be successful
  5. You have standard kit including tools such as collateral, presentations, demos, templates
  6. You have a standard sales methodology that includes how you define and execute the sales process

And, of course, it’s demonstrating some repeatable result.  While many folks instinctively drift to “80% of salesreps at 100% (or more) of their quota” they forget a few things:

  • The percentage should vary as function of business model: with a velocity model, monthly quotas, and a $25K ARR average sales price (ASP), it’s a lot more applicable than with an enterprise model, annual quotas, and a $300K ASP
  • 80% at 100% means you beat plan even if no one overperforms [2] – and that hopefully rarely happens
  • There is a difference between annual and quarterly performance, so while 80% at 100% might be reasonable in some cases on an annual basis, on a quarterly basis it might be more like 50%
  • The reality of enterprise software is that performance is way more volatile than you might like it to be when you’re sitting in the board room
  • When we’re looking at overall productivity we might look at the entire salesforce, but when we’re looking at repeatability we should look at recently hired cohorts. Does 80% of your third-year reps at quota tell you as much about repeatability – and the presumed performance of new hires – as 80% of your first-year reps cohort?

Long story short, in enterprise software, I’d say 80% of salesreps at 80% of quota is healthy, providing the company is making plan.  I’d look at the most recent one-year and two-year cohorts more than the overall salesforce.  Most importantly, to limit survivor bias, I’d look at the attrition rate on each cohort and hope for nothing more than 20%/year.  What good is 80% at 80% of quota if 50% of the salesreps flamed out in the first year?  Tools like my salesrep ramp chart help with this analysis.

But all that was just the warm-up for the big idea in this post:  is repeatability enough?  Turns out, the other day I was re-reading my favorite book on data governance, Non-Invasive Data Governance by Bob Seiner, and it reminded me of the Capability Maturity Model, from Carnegie Mellon’s Software Engineering Institute.

Here’s the picture that triggered my thinking:

Did you see it?  Repeatable is level two in a five-level model.  Here we are in sales and marketing striving to achieve what our engineering counterparts would call 40% of the way there.  Doesn’t that explain a lot?

To think about what we should strive for, I’m going to switch models, to CMMI, which later replaced CMM.   While it lacks a level called “repeatable” – which is what got me thinking about the whole topic – I think it’s a better model for thinking about sales [3].

Here’s a picture of CMMI:

I’d say that most of what I defined above as a repeatable sales process fits into the CMMI model as level 3, defined.  What’s above that?

  • Level 4, quantitively managed. While most salesforces are great about quantitative measurement of the result – tracking and potentially segmenting metrics like quota performance, average sales price, expansion rates, win rates – fewer actually track and measure the sales process [2].  For example, time spent at each stage, activity monitoring by stage, conversion by stage, and leakage reason by stage.  Better yet, why just track these variables when you can act on them?  For example, put rules in place to take squatted opportunities from reps and give them to someone else [3], or create excess stage-aging reports that will be reviewed in management meetings.
  • Level 5, optimizing. The idea here is that once the process is defined and managed (not just tracked) quantitatively, then we should be in a mode where we are constantly improving the process.  To me, this means both analytics on the existing process as well as qualitative feedback and debate about how to make it better.  That is, we are not only in continual improvement mode when it comes to sales execution, but also when it comes to sale process.  We want to constantly strive to execute the process as best we can and also strive to improve the process.  This, in my estimation, is both a matter of culture and focus.  You need a culture that process- and process-improvement-oriented.  You need to take the time – as it’s often very hard to do in sales – to focus not just on results, but on the process and how to constantly improve it.

To answer my own question:  is repeatability enough?  No, it’s not.  It’s a great first step in the industrialization of your sales process, but it quickly then becomes the platform on which you start quantitative management and optimization.

So the new question should be not “is your sales process repeatable?” but “is it optimizing?”  And never “optimized,” because you’re never done.

# # #

Notes

[1] Back when that used to be a lot of money

[2] You typically model a 20% cushion between quota and expected productivity.

[3] The nuance is that in CMM you could have a process that was repeatable without being (formally) defined.  CMMI gets rid of this notion which, for whatever it’s worth, I think is pretty real in sales.  That is, without any formal definition, certain motions get repeated informally and through word of mouth.

[4] With the notable exception of average sales cycle length, which just about everyone tracks – but this just looks at the whole process, end to end.  (And some folks start it late, e.g., from-demo as opposed to from-acceptance.)

[5] Where squatting means accepting an opportunity but not working on it, either at all or sufficiently to keep it moving.