Fortella, which I’ve served as an advisor over the past year or so, makes a revenue intelligence platform. The company recently published an interesting survey report entitled The State of B2B Marketing: What Sets the Best Marketers Apart? Rahul is super passionate about marketing accountability for revenue and the use of AI and advanced analytics in so doing, which is what drew me to want to work with him the first place. He’s also an avid Kellblog reader, to the point where he often reminds me of things I’ve said but forgotten!
In this webinar we’ll drive a discussion primarily related to two Kellblog posts:
That pipeline isn’t a monolith and that we need to look inside the pipeline to see things by opportunity type (e.g., new vs. expansion), customer type (e.g., size segment, industry segment) and by source (e.g., inbound vs. partners). We also need to remember that certain figures we burn into our heads (e.g., sales cycle length) are merely the averages of a distribution and not impenetrable hard walls.
By decomposing pipeline we can identity that some types close faster (and/or at a higher conversion rate) than others, and ergo focus on those types when we are in a pinch.
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  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 , 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 .
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 . 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 .
Tantalus and his pipeline where all the closeable deals are always two quarters out
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 , 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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 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.
 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.”
 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.
 How do you make 32 quarters in row? One at a time.
 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.
 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.
 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.
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.
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?” 
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.” 
And that is the concept of to-go pipeline coverage . 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 .
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. 
I can easily reverse engineer that we’ve sold only $750K in New ARR to date , 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 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) 
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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 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.
 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.
 “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.
 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.
 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.
 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.
 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.
 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.
CEO: What’s the forecast?
CRO: Same as before, $3,400K.
Director 1: How do you feel about it?
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.
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  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 , 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 .
Our growth is well below planned growth of 37% and decelerating .
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 
The sum of the sales managers’ forecasts 
The stage-weighted expected value (EV) of the pipeline  
The forecast category-weighted expected value of the pipeline 
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  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 .
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 .
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 . 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.
 This is the top of the weekly sheet I recommend CEOs to start their weekly staff meeting.
 A SaaS company is conceptualized as a leaky bucket of ARR.
 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.
 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.
 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.
 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.
 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.
 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?
 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.
 Sometimes embarrassingly so for the CRO whose forecast thus ends up a mathematical negative value-add!
 This is not a great practice IMHO and thus CEOs should not inadvertently incent inflated forecasts by hard-coding expense cuts to the forecast.
 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.)
 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.
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:
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:
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  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.
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 .
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 . 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 . 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 . 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 .
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-servicedemand 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.
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 Meaning that your plan number is larger than your sales productivity capacity. An undesirable, but certainly not unheard of, situation.
 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.
[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.”
 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.
 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.
 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.
 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.
 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.
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups. I am an executive-in-residence (EIR) at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of cloud EPM vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of NoSQL database provider MarkLogic, which we grew from zero to $80M over 6 years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including Bluecore, Cyral, FloQast, GainSight, MongoDB, Recorded Future, and Tableau.
I previously sat on the boards of Granular (agtech, acquired by DuPont), Aster Data (big data, acquired by Teradata), and Nuxeo (content services, acquired by Hyland), and Profisee (MDM, exited to Pamlico).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).