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I’ve written about this topic a lot over the years, but never before integrated my ideas into a single high-level piece that not only provides a solution to the problem, but also derives it from first principles. That’s what I’ll do today. If you’re new to this topic, I strongly recommend reading the articles I link to throughout the post.
Scene: you’re consistently having trouble hitting plan. Finance is blaming sales. Sales is blaming marketing. Marketing is blaming the macro environment. Everyone is blaming SDRs. Alliances is hiding in a foxhole hoping no one remembers to blame them. E-staff meetings resemble a cage fight from Beyond Thunderdome, but it’s a tag-team match with each C-level tapping in their heads of operations when they need a break. Numbers are flying everywhere. The shit is hitting the proverbial fan.
The question for CEOs: what do I do about this mess? Here’s my answer.
First:
Avoid the blame game. That sounds much easier than it is because blame can vary from explicit to subtle and everyone’s blame sensitivity ears are set to eleven. Speak slowly, carefully, and factually when discussing the situation. You might wonder why everyone is pointing fingers, and the reason might well be you.
Solve the problem. Keep everyone focused on solving the problem going forward. Use blameless statements of fact when discussing historical data. For example, say “when we start with less than 2.5x pipeline coverage, we almost always miss plan” as opposed to “when marketing fails on pipeline generation, we miss plan unless sales does their usual heroic job in pipeline conversion.”)
Then reset the pipeline discussion by constantly reminding everyone of these three facts:
How do you make 16 quarters in a row? One at a time.
How do you make one quarter? Start with sufficient pipeline coverage.
And then convert it at your target conversion rate.
This reframes the problem into making one quarter — the right focus if you’ve missed three in a row.
This will force a discussion of what “sufficient” means
That is generally determined by inverting your historical week 3 pipeline conversion rates
And adjusting them as required, for example, to account for the impacts of big deals or other one-time events
This may in turn reveal a conversion rate problem, where actual conversion rates are either below targets and/or simply not viable to produce a sales model that hits the board’s target customer acquisition cost (CAC) ratio. For example, you generally can’t achieve a decent CAC ratio with a 20% conversion rate and 5x pipeline coverage requirement. In this case, you will need to balance your energy on improving both conversion rates and starting coverage. While conversion rates are largely a sales team issue, there is nevertheless plenty that marketing and alliances can do to help: marketing through targeting, tools, enablement, and training; alliances through delivering higher-quality opportunities that often convert at higher rates than either inbound or SDR outbound.
It also says you need to think about each and every quarter. This leads to three critical realizations:
That you must also focus on future pipeline, but segmented into quarters, and not on some rolling basis
That you need to forecast pipeline (e.g., for next quarter, if not also the one after that)
That you need some mechanism for taking action when that forecast is below target
The last point should cause you to create some meeting or committee where the pipeline forecast is reviewed and the owners of each of the four to six pipeline sources (i.e., marketing, AE outbound, SDR outbound, alliances, community, PLG) can discuss and then take remedial measures.
That body should be a team of senior people focused on a single goal: starting every quarter with sufficient pipeline coverage.
It should be chaired by one person who must be seen as wearing two hats: one as their functional role (e.g., CMO) and the other as head of the pipeline task force. That person must be empowered to solve problems when they arise, even when they cross functions.
Think: “OK, we’re forecasting 2.2x starting coverage for next quarter instead of 2.5x, which is a $2M gap. Who can do what to get us that $2M?”
If that means shifting resources, they shift them (e.g., “I’ll defer hiring one SDR to free up $25K to spend on demandgen”).
If that means asking for new resources, they ask (e.g., I’ll tell the CEO and CFO that if we can’t find $50K, then we think we’ve got no chance of hitting next quarter’s starting coverage goals).
If that means rebalancing the go-to-market team, they do it. For example, “we’ve only got enough pipeline to support 8 AEs and we’ve got 12. If we cut two AEs, we can use that money to invest in marketing and SDRs to support the remaining 10.”
Finally, if you need to focus on both pipeline coverage and conversion rates, then this same body, in part two of the meeting, can review progress on actions design to improve conversion.
Teamwork and alignment is not about behaving well in meetings or only politely backstabbing each other outside them. It’s about sitting down together to say, “well, we’re off plan, and what are we going to do about it?” And doing so without any sacred cows in the conversation. Just as no battle plan survives first contact with the enemy, no pipeline plan survives first contact with the market. That’s why you need this group and that’s what it means to align sales, marketing, alliances, and SDRs on pipeline goals. It’s the translation of the popular saying, “pipeline generation is a team sport.”
Notice that I never said to heavily focus on individual pipeline generation (“pipegen”) targets. Yes, you need them and you should set and track them, but we must remember the purpose of pipegen is to hit starting pipeline coverage goals. So just as we shouldn’t overly focus on other upstream metrics — from dials to alliances-meetings to MQLs — we shouldn’t overly focus on pipegen targets to the point where they become the end, not the means. While pipegen is certainly closer to starting coverage than MQLs or dials, it is nevertheless an enabler, in this case, one step removed.
Yes, tracking upstream metrics is important and for marketing I’d track both MQLs and pipegen (via oppty count, not dollars), but I’d neither pop champagne nor tie the CMO to the whipping post based on either MQLs or pipegen alone.
Don’t get me wrong — if your model’s correct, it should be impossible to consistently hit starting pipeline coverage targets while consistently failing on pipegen goals. But in any given quarter, maybe the AEs are short and marketing covers or marketing’s short and alliances covers. The point is that if the company hits the starting coverage goal, we’re happy with the pipeline machine and if we don’t, we’re not. Regardless of whether individual pipeline source X or Y hit their pipegen goals in a quarter. Ultimately, this point of view drives better teamwork because there’s no shame in forecasting a light result against target or shame in asking for help to cover it.
Finally, I’d note an odd situation I sometimes see that looks like this:
Sales consistently achieves bookings targets, but just by a hair
For example, sales consistently converts pipeline at 25% off 4x coverage and that 25% conversion rate is just enough to hit plan. But, because the CRO likes cushion, he forces the CMO to sign up for 5x coverage. Marketing then consistently fails to deliver that 5x coverage, delivering 4x coverage instead.
This is an unhealthy situation because sales is consistently succeeding while marketing is consistently failing. If you believe, as I do, that if sales is consistently hitting plan then, definitionally marketing has provided everything it needs to (from pipeline to messaging to enablement), then you can see how pathological this situation is. Sales is simply looking out for itself at the expense of marketing. That’s good for the company in the short term because you’re consistently hitting plan, but bad in the long term because there will be high turnover in the marketing department that should impede their ability to deliver sufficient pipeline in the future.
So, you’re missing plan and revenue growth is down. Well, welcome to the club. You’re certainly not alone in these times.
In this post, I’ll discuss what you can do about it – specifically, how you can apply some of the ideas I’ve discussed in Kellblog to troubleshoot go-to-market (GTM) performance. I’ll focus on troubleshooting new business (“newbiz”) ARR plan attainment, the area where most companies seem to be having the most trouble [1].
Don’t Knee-Jerk Blame the Plan
The immediate temptation when missing plan is to blame the plan. “It’s not realistic.” “It was driven by the fundraise, not the bottom-up.” But blaming plan is a poor place to start for two reasons.
First, you signed up for the plan when you submitted it to the board for approval. Next time, if you don’t believe in a proposed plan, don’t be so quick to fold in the face of internal pressure. Remember the old Fram oil filter commercial and think, “you can fire me now or fire me later” so if you’re asking me to sign up for a plan that I don’t think I can achieve, you might as well fire me now [2]. The need to make such difficult judgments is the price of admission to the sales leadership role. Cop out at your own peril, because they will indeed fire you later.
Second, when you follow the approach in this post, if the plan is unachievable it will emerge from the data. So, bite your tongue, avoid any initial temptation to blame plan, and instead go look at the funnel.
The Two Questions and Two Metrics
Recall in this post, I argued that you should ask two questions when you’re missing plan. Every quarter:
Are we giving sales the chance to hit the number?
Is sales converting enough of the pipeline to hit the number?
That’s it. Everything comes down to these two questions. No matter the root problem, it will be revealed in answering them. Remember, the way to make plan for twelve consecutive quarters is one at a time. So why not focus on next quarter? And if you’re chronically missing plan, why not make a steady-state assumption to simplify things further? [3]
Starting with the above two questions makes things simple by breaking the entire funnel in two. Simplifying the problem is important because you can quickly and irrecoverably descend into analytical quicksand. When I first meet them, many companies are neck-deep in such quicksand, comparing dashboard clips, reports, and spreadsheets derived from different systems, lost in an endless sea of non-footing detail, having completely lost the business forest for the salesops trees.
Note that neither of the two above questions assigns blame. As a consultant, I have the distinct advantage of not caring where the trouble is, making me a disinterested party, de facto impartial. I encourage CXOs to adopt a similar approach, simply stating facts, avoiding blame (e.g., inferred causes), and acting as dispassionate analysts when analyzing GTM problems. While you will eventually need to ask why you have certain problems, it’s always best to start with simple statements of fact, get agreement on them, and build from there. For example:
“We consistently start quarters with insufficient pipeline coverage” is a blameless statement of fact. It does not say whose job it is to generate pipeline (if that’s even been detailed out across sources) or why they are failing to do so.
“We are converting a below-normal percentage of our week 3 pipeline,” less obviously, is also a blameless statement of fact. While it’s clearly the job of sales to convert pipeline, the statement makes no assertion as to why we are seeing abnormally low conversion rates (e.g., pipeline quality, change in competitive market, sales execution).
When it comes to metrics, the first of the above questions is measured by pipeline coverage, more precisely week-3 pipeline coverage [4] [5]. The second is measured by a conversion rate, specifically week-3 pipeline conversion rate. Notably, this is not a win rate, and please read this post to ensure that you understand why.
Are We Giving Sales a Chance to Hit the Number?
Make a chart like this one to answer this question.
Here you see, for newbiz ARR for the trailing nine quarters, week 3 pipeline dollars, week 3 pipeline coverage (pipeline/plan), ARR booked, week-3 pipeline conversion, and the pipeline coverage target implied by the week-3 conversion rate (i.e., its inverse). Pipeline conversion rates are more interesting when viewed in conjunction with plan attainment, so I’ve added ARR plan and plan attainment as well.
Analyzing this chart, we can see a few things:
From 1Q22 through 1Q23 we converted about 33% of the pipeline
We were also consistently hitting plan in that timeframe
Starting 2Q23 we started with only 2.3x coverage, converted a healthy 40% of it, but still came up short, at 91% of plan.
That rough pattern continued in 3Q23 and 4Q23
1Q24 started with the weakest coverage in the past nine quarters (1.9x)
While sales is forecasting record conversion of that pipeline (45%), we are nevertheless forecasting to land at only 86% of plan
I’m not sure I believe the forecast because 45% conversion is borderline unrealistic and could simply be the CRO trying their best to hold the line
I conclude that this company is starting with insufficient pipeline. That is, they’re not giving sales a chance to hit the number. How do I conclude that?
By comparison to pipeline coverage benchmarks. 3.0x is the typical pipeline coverage goal and you’ll note that in the good times (1Q22 through 1Q23) we consistently started with 3.0x+ and we consistently made plan.
By comparison to pipeline conversion benchmarks. 33% is a standard conversion rate. Here we are running at 40%+, which is best-in-class conversion. Pipeline conversion is not the problem.
More importantly, by comparison to ourselves. In our recent history, we consistently made plan when we started with 3.0x+ coverage and missed it when we started with 2.3 to 2.4x. This quarter (1Q23) we’re starting with 1.9x, forecasting record conversion, and still only 86% of plan.
Here are some of the high-level steps in making that plan:
Define pipeline generation targets across the four major pipeline sources. It’s surprising how many companies don’t start with this basic step. For bonus points, over-allocate the goals to target 110% of what you need. [6]
I prefer to set these targets by opportunity count, not pipeline dollars, because I think it’s more visceral and less easily gamed [7].
Do a cost/oppty analysis across your pipeline sources to get an idea of how much money any given pipeline source (e.g., alliances, demandgen) would need to create, for example, 20 more oppties next quarter. Remember to focus on variable, not average, cost [8].
Be sure to check with the leader of each pipeline source on their ability to absorb extra money to generate more pipeline. If you have 12 SDRs reporting to one manager, they may need to bring in another manager before hiring 3 more SDRs. Alternatively, sellers may have extra time on their hands and the ability to put more time into outbound. Alliances may have a hot candidate they want to hire, but no open headcount, and could execute quickly if one were opened. It’s not just about money; it’s about the ability to productively spend it.
Accept that you may be overallocated to sales versus pipeline generation. In this case, the best solution might well be to terminate the bottom N sellers and convert the newly liberated budget to pipeline generation — so that everyone else has a chance at success. This is painful, but sometimes necessary, and after you’ve had to do it once, you’ll be more careful to plan holistically in the future.
This all goes without saying that no pipeline analytics will work if you lack basic pipeline discipline – i.e., if you don’t have clear definitions for stages, close dates, oppty values, and forecast categories, and if you don’t regularly enforce them via periodic pipeline scrubs.
The Floating Bar Problem
Before diving into pipeline conversion, let’s address a special case of insufficient pipeline: one where the pipeline initially looks sufficient but burns off at an above-average rate across the quarter. You can see this by looking weekly at to-go pipeline coverage.
What’s usually happening in these cases is that some material percentage of your week-3 pipeline is effectively fake. This happens because, when pipeline is scarce and if sellers are under pressure to each carry 3x coverage [9], they will take lower-quality opportunities into their pipeline. For example, long-shot oppties that appear rigged for the competition, immature oppties where sellers hope to create a buying timeline, or self-nurture leads that may only become real oppies in the future.
I call the tendency to work on lower quality oppties in tough times, the “floating bar problem” because sales silently lowers (or in good times, raises) the bar for admission into the pipeline. This is insidious because the result is fake pipeline that creates an illusion of coverage which disappears as the quarter progresses.
The solution to his problem is simple in theory, but hard in practice.
Sales management needs to hold the line on what gets into the pipeline, applying the same standards in tough times as good ones.
If sales management wants to allow sellers to work on low probability “oppties,” that’s fine but, well, get them out of the opportunity management system. Use tasks to track work. But only promote a lead to an oppty when it meets the standard for being an oppty.
If, for example, SDRs are passing low quality stage-1 oppties to sales that should not show up in the numbers as a reduced pipeline conversion rate. Instead, it should show up in a higher stage-2 rejection rate. This point is completely lost on most sales managers so please make sure you understand it. If you maintain pipeline discipline, lower quality oppties should show up not as a reduced stage-2 to close rate, but as an increased stage-2 rejection rate. And pipeline discipline starts at stage 2 – where sales decides to accept or reject oppties. It’s wrong to accept sub-standard oppties, pollute the oppty management system with fake pipeline, convert little of it, miss plan, and wreck the company’s pipeline analytics in the process.
I’m not trying to prevent sales from working on whatever sales management wants them to work on. But I am saying one thing: whatever they are, don’t call them oppties in “my” oppty management system if they don’t meet the defined standards for oppties [10].
Is Sales Converting Enough of the Pipeline?
While it’s the job of sales to convert pipeline into ARR, that doesn’t mean sales execution is the only factor that drives conversion rates.
Here you see conversion rates plummeting, dropping by 11 percentage points between 1Q23 and 2Q23 and then by another 5 percentage points by 4Q23. By the 1Q24 forecast, the pipeline conversion rate has been effectively cut in half from ~32% to ~16%. Note that during the recent dark times (from 2Q23 to 1Q24) we have been starting with ~3.0x pipeline coverage, but converting so little that we’re landing in the dismal range of 47% to 65% of plan.
Let’s assume we have the operational basics covered, so this is real pipeline, validated and scrubbed by sales management, and held to consistent standards over time. But we’re converting a lot less of it than we used to. Thus, I conclude that the company’s problem is pipeline conversion, not pipeline coverage.
What possible factors could be driving reduce pipeline conversion rates? Well, there are a lot of them, so we’ll talk about each.
Changes in averages (i.e., ceteris non paribus). Most productivity models assume a constant average sales price (ASP) and average sales cycle length (ASC). If ASPs go down, you will hit your count-based targets, but miss your dollar-based ones. If ASCs increase you may preserve your eventual close rates, but stretch them out over time, reducing quarterly conversion rates and plan attainment.
ASP decreases. Typically, due to budgetary pressure and increased price competition, but also can be due to an overreliance on discounting. Some of this is inevitable in a downturn. You can mitigate it through pricing and packaging changes (e.g., new add-ons to preserve price and/or offset churn at renewal).
Slip rate increases. When ASCs lengthen, more deals slip to the following quarter(s). Pipeline scrubs can provide early detection and deals reviews can offer re-acceleration strategies. The biggest risk is that these deals never close at all and simply hit no-decision or derail.
Win rate decreases. Win rates usually decrease when a new competitor enters the market or when an existing competitor leapfrogs your product or your market position (e.g., passes you in market share). Competitive research, sales training, and selling the roadmap are the usual responses.
An absence of big deals. Some CROs run their business as a mix of baseline deals to hit say 60-80% of plan, topped up by big deals that provide the rest. During a downturn those big deals may evaporate leaving only the run-rate business. The usual response is a strategic accounts program to focus on generating big deals and a focus on pipeline generation in the run-rate business to cover the gap.
Pipeline substitution. This is a subtle problem due to a change in pipeline mix, with low-converting pipeline substituting for high-converting pipeline. This is dangerous because you “look covered” at the start of the quarter but end up below plan at the end. Let’s drill in a bit here.
Pipeline Substitution
Not all pipeline is created equal. Pipeline for certain products often converts at a higher rate than others. Pipeline conversion rates typically vary by source, e.g., with outbound SDRs typically converting at a low rate and alliances converting at a high rate. Pipeline conversion might also vary by geography, with established geographies delivering high conversion rates than emerging ones.
See this chart for an example:
In this example, we start every quarter with $10M in pipeline. In 1Q23 through 3Q23 we convert 25% of it, but in 4Q23 we convert only 20%. What happened? The pipeline mix changed. Starting in 4Q23, we substituted $2M in high-converting pipeline (from sales/outbound and alliances) with $2M in low-converting pipeline (from SDR/outbound). Blended pipeline conversion thus dropped from 25% to 20% as a result of this change, effectively substituting nutrient-rich pipeline for nutrient-poor pipeline while keeping the overall amount the same.
Identifying these problems is a lot of work because you’ll need to segment pipeline by multiple variables — such as pipeline source, product, geography, business segment (e.g., enterprise vs. corporate accounts) – to get historical average conversion rates and percent mix, and then see if changes in pipeline composition are driving reductions in conversion rates. If so, the usual solution is to re-aim your pipeline generation back to the high-converting segments.
In this post, we have shown how you can troubleshoot go-to-market problems by splitting the funnel in two and focusing on two questions:
Are we giving sales the change to hit the number each quarter, as measured by pipeline coverage.
Is sales converting enough of the pipeline to hit the number, as measured by pipeline conversion.
I’ve also provided numerous notes and links that you can use to deepen your knowledge of how to solve these problems.
# # #
Notes
[1] The same analysis approach can easily apply to expansion ARR, which should be analyzed independently via its own funnel because it typically has different conversion rates and shorter sales cycles.
[2] Deadheads will understand that I had to resist writing, “nothing else shaking, so you might just as well.”
[3] Think: given that we’re off rails, forget the plan for a minute and let’s analyze what do we need to do to add $4M in newbiz ARR every quarter? This liberates you from needless, complexifying math that makes it harder to see the answer and is a great way to start in the crawl-walk-run exercise of getting back on track.
[4] More precisely, day-1, week-3, current-quarter pipeline coverage. Snapshotting Sunday night before the start of week 3 gives you a consistent point to compare across quarters. Waiting until the start of week 3 gives sales (more than) enough time to clean up the pipeline after the end of the prior quarter but is still early enough to be considered “starting pipeline.” Note that you may need to apply corrections for any deals that close in the first two weeks of the quarter. A high-class problem, at least.
[6] There is a cost to this type of insurance; it’s not great for your CAC ratio if you don’t end up over-performing plan (which ceteris paribus, starting with 110% of your pipeline target, you should). But it does reduce the risk of missing plan. To me, the correct sequence is to focus on making plan first, before focusing on efficiency — but you need to have the cash to underwrite that philosophy.
[7] For example, one big deal that masks what’s otherwise a pipeline starvation situation. If you’re going to set targets on dollars (which typically involves using some placeholder value) then you should create the oppties with a close date far in the future (e.g., one year) that sales can pull forward once they further qualify the account. The alternative is usually generating lots of fake pipeline that is auto-dumped into next quarter that gets pushed out in the first weeks of the quarter. Also, see this for more on ensuring pipeline coverage by seller, and not just in aggregate.
[8] You’re not going to hire an extra CMO, an extra PR agency, and an extra product marketer to generate 20 more oppties. Those costs are effectively fixed.
[9] And putting them under such pressure can run in diametric opposition to pipeline discipline and enforcing pipeline standards by encouraging reps to enter dubious deals as pipeline to get their manager off their backs.
[10] I say “my” oppty management system to remind people that carrying sub-standard oppties has impacts well beyond themselves and that oppty management system is the company’s property, not theirs. For old movie fans, when speaking of the oppties in “my” oppty management system, I’m always reminded of Cool Hand Luke: “what’s your dirt doing in Boss Keen’s ditch?”
I was reading a SaaS benchmark report the other day and encountered this line:
“Win rates declining [over the two-year period] from 23% to 19% might not seem all that significant. But in terms of required pipeline, it represents a dramatic shift from 4.3x to 5.3x coverage.”
It’s the kind of sentence that you might read, nod your head in hasty agreement, and then keep going. But you’d be wrong to do that. Quite wrong. And a lot of people make this mistake.
Thus, in this post, I’ll explain why it’s wrong to invert win rate to calculate target pipeline coverage, demonstrate that with a spreadsheet, and then give you a better way to determine target pipeline coverage.
Before diving into the math, let’s take a second to sanity check the conclusion reached above: you’re going to need 5.3x pipeline coverage [1]. Given that the rule of thumb for pipeline coverage is 3.0x, how do we feel about requiring 5.3x? My thoughts [2]:
I wonder who’s going to generate that? In many companies, it’s primarily marketing. So this potentially passing the buck: “hey marketing, we’re not closing as much as we used to, so we need more coverage.” It’s your problem, now.
At what cost? Let’s say that it costs $4K to generate a sales-accepted (aka, stage 2) opportunity [3]. If we needed 3x coverage before — e.g., 30 opportunities (“oppties”) to generate 10 deals — now we are going to need 53. That’s 23 more oppties at an incremental cost of $92K. Who’s going to pay for that? What’s that going to do to our CAC ratio and CPP?
Why do we lose so much? Sales is telling us that they can win only 19% of the oppties that they accept as valid sales oppties? That strikes me as low. If a tougher macro environment means lower quality stage 1 oppties, then why is sales accepting them? Lower quality stage 1 opportunities should show up in a higher stage 2 rejection rate, not a lower win rate [4].
So, if the answer is that we need 5.3x pipeline coverage to make plan, I’m going to have a lot of questions without doing any math at all. But now, let’s cut to the math.
What is Win Rate?
Most people define win rate as follows:
For all oppties that reached a terminal state during the quarter, win rate = wins / (wins + losses). I call this narrow win rate because it excludes no-decisions (also known as derails) where an oppty hits a terminal state without anyone winning it — for example, where the customer decided to stick with the status quo or the whole evaluation gets derailed by a surprise merger [5]. Because derails can happen a lot [6], I define an additional metric, broad win rate = wins / (wins + losses + derails).
Note that both of these win rates exclude slips, when the close date for an opportunity is moved out of the current quarter into a future one. Slips happen a lot. In fact, my basic rule of thumb is you win a third, you lose a third, and you slip a third [7]. Also note that I’m doing this on a count basis, not a dollar basis, which is my default preference [8].
You should already see why inverting win rate is not a great way to determine pipeline coverage requirements:
It’s ambiguous. Which win rate, narrow or broad?
Slips are common, but excluded from win rates. (Definitionally, because slipped oppties do not hit a terminal state in the quarter.)
The timing is wrong. We use pipeline coverage at the start of the quarter to see if we have a chance at hitting the number. But win rates are based on when oppties die, not their start-of-quarter status.
What is Close Rate?
I define close rate as a cohort-based metric that answers the question: given a set of oppties, what percent of them do we close/win [9] in some time period. For example, the six-quarter close rate for the cohort of stage-2 oppties created in 1Q22 = oppties in the cohort closed in the period [1Q22 to 3Q23] / oppties created in 1Q22. Let’s show it with an example:
The first block shows oppty count, the second shows percent. Here, we see a 27% six-quarter close rate. You can also run a cumulative rate along the bottom of the table that would show, for example, that the four-quarter close rate is 23%.
Win rates are period metrics that tell you what happened to the oppties that a hit a terminal state in a given period. Close rates are cohort metrics that tell you, in the fullness of time, the percent of a set of oppties that we win.
They are different.
They are both valuable.
Win rates are great for tracking progress against the enemy.
Neither is good if you want to invert something to find required pipeline coverage.
Week 3 Pipeline Conversion Rate
Let’s look at a different metric. Instead of starting with the fate of oppties in the pipeline, let’s start with an early-quarter snapshot of the current-quarter pipeline and then compare it to how much we close. Ideally, we’d take the snapshot on day one of the quarter, but that’s not realistic because sales invariably needs some clean-up time after the end of a quarter. Ergo, I typically use week-3 starting pipeline. If you have a monthly cadence, I’d suggest doing this same analysis on a monthly basis and using day-3 starting pipeline [10]. You can then calculate week-3 pipeline conversion rate = new ARR closed / week-3 starting pipeline. See [11] for some notes on this metric.
Because the conversion rates often differ significantly between new and expansion business, most people segment week-3 pipeline conversion rate by new business (newbiz) vs. expansion. In my endless desire to keep things simple, I always start with the total, unsegmented pipeline and break it out later if I need to. The reality is that while the conversion rates are different, if the mix remains roughly constant, it all comes out in the wash.
Here’s a table to show this concept at work:
To get implied target pipeline coverage, I take a trailing nine-quarter average of the week-3 pipeline conversion rate (34%) and then invert it to get 2.86. You could also have fun with the percent-of-plan row, asking questions like: what pipeline coverage do we need to hit plan 90% of the time?
In this post, I’ve hopefully blown a hole in the conventional wisdom that you can invert win rate to get target pipeline coverage. And I’ve provided a far better metric for accomplishing that task: week-3 pipeline conversion rate.
[1] When I used to help my kids with math homework, I’d always include a sanity check review of the answer. If you’re calculating the mean summer temperature in Alaska and the answer is 451 degrees, then go back and check your work.
[2] And I find that rule of thumb high in many situations. At the last company I ran, we could consistently hit plan with 2.5x coverage.
[3] In practice, the average cost of a stage 2 oppty varies considerably. I think a range of $2K to $10K probably covers 90% of cases, with a mean around $4-5K. These are mid-market and enterprise figures. SMB is presumably cheaper. These are sales-accepted so the cost is equivalent to your stage 1 oppty cost dividied by your stage 2 acceptance rate (typically 60-80%).
[4] Yes, I’m aware of the “desperation effect” whereby sellers with weak pipeline accept lower-quality opportunities, but sales management must fight to hold some objective quality bar to preserve pipeline discipline, to ensure resources are only put against quality oppties, and to ensure the validity of pipeline analysis. So yes, the effect is real, but it’s sales management’s job to limit it. (See the “floating bar” problem discussed here.)
[5] Many people code no-decisions as losses and then have a reason code for no-decision. I think this potentially blurs up win/loss analysis because losing to a competitor is different from a no-decision. (Plus, it usually precludes putting no-decision codes on no-decisions which I also want.) The fact is they are two different cases: losing to a competitor vs. an evaluation process ending without selecting a winner.
[6] Particularly in new markets where people are primarily exploring whether they want to buy one at all. In more developed markets — where the customer is more likely thinking, “I’m going to buy one, the question is which” — you should see lower derail rates. And those derails should be more surprise-driven — e.g., we got acquired, the CFO quit, we missed a quarter, we failed an audit, we’re being sued, etc.
[7] Which implies in a 50% narrow win rate, a 33% broad win rate, a 33% slip rate. This is realistic if you are one of two competitors going head-to-head in a market segment. If it’s more of a horse race, I’d expect to see a lower rate. Also, the “a third, a third, a third” rule excludes derails which you can skim off the top. For example, if 20% derail and the balance split by thirds, then you win 27%, lose 27%, and slip 27% of your deals.
[8] I prefer counts to dollars because they’re more visceral and less messed up by big deals. If you are running two sales motions (e.g., corporate and enterprise), I’d first try to stay count-based, but segment the analysis before going to a dollar basis. But there’s a time and a place for both.
[9] Which some might prefer to think of as a “closed/won rate,” but that’s too many syllables for me.
[10] Both generously allows about 10% plus or minus of the period to elapse before snapshotting: 3 days out of 30 (10%) and, depending on how you calculate weeks and what day the quarter starts on, up to 14 days out of 91 (15%).
[11] This assumes (a) sales cycles much longer than the period (e.g., 6-12 months) and (b) no sales are made prior to the snapshot. It ignores (a) deal expansion or shrinkage after week 3, and (b) where closed/won deals came from (e.g., they may be in the week-3 snapshot, created after it, or pulled-forward from a future quarter). This asymmetry bothers some people but it’s really supposed to be a macro measure. The real risk you face using it is when ceteris aren’t paribus.
Quota attainment can get confusing quickly. It’s simple concept, but:
Do you mean percent of reps at 100% of their quota or above some lower bar? Board members bark rules like, “we should shoot for 80% of our reps at 100% of quota,” almost hearing the unspoken words, “because that’s what we did back in the day at GreatCo.” How much of that is nostalgia I’m not sure, but here in the real world, I almost never see 80% at 100% [1]. In fact, getting to 80% at 80% is actually quite an achievement.
Do you mean on a quarterly or annual basis? The telltale of a dilettante is when asked they reply: ”uh, well, we need 80% at 100% on a quarterly basis.” In my enterprise B2B world, that literally never happens. Getting to 80% at 100% on an annual basis is nearly impossible. On a quarterly basis, it’s absolutely impossible. See this post, and the spreadsheet at the end of it, to demonstrate this point quite tangibly [2].
Do you mean productivity or quota? Quota is the target we assign to reps. Productivity is what we expect them to actually sell. Many people build a quota model and then subtract a cushion to get productivity. I prefer to build a productivity model [3] and then uplift to quota. Note that both the thickness and layer-by-layer allocation of that cushion is an important sales culture issue. But, back to our main point: when you say 80% or 100%, my question is: of what? [4]
Do you mean rep-by-rep achievement or overall realization of assigned quota? Most people mean rep-by-rep. But it’s also quite interesting to view realization as professional services teams do. If we model a consultant to bill 2,000 hours per year at a list price of $200/hour, they should bill $400,000 per year. If, due to beach time, discounts, and rework, they end up billing $300,000, we would say their realization is 75%. We realized only 75% of their theoretical billings. By analogy, we can say that if we assign $10M in street-level quota [5] and sell $7.5M that we realized 75% of our assigned quota.
In short, there’s enough potential for confusion here that I recommend three things:
Track percent of reps below 50%, above 80%, and above 100% of quota [6].
Track realization of overall, street-level quota.
Make the conversation concrete by building and playing with a simple quota attainment model [7].
I have embedded such a model below, and you can download it here.
Let’s quickly have some fun looking at the scenarios I created:
Scenario 1 realizes 100% of assigned quota and does that with three stars and one superstar. 20% of reps are less than 50% of quota [8]. Fun, but rarely happens.
Scenario 2 is what I view as realistic for a high-performing company. We made plan. We’ve got 80% of reps at 80%. Only 20% are below 50%. 30% at 100%, which might be a tad light for most sales managers. (That’s why I started making it green at 40%.)
Scenario 3 explores highly uneven achievement. We’re still making plan, but half of total sales are coming from one rep. 50% are below 50%. I lived a less dramatic version of this scenario and it’s unpleasant. The board will ignore that you’re making plan and complain endlessly about the unhealthy distribution of achievement [9].
Scenario 4 will never happen in real life but it shows you what happens when everyone is at 80%. The good news is you’re 80% at 80% and 0% below 50%. The bad news is you’re 0% at 100%. Some people wish for this, but I’m not sure they actually understand what they’re wishing for.
Scenario 5 is a more realistic version of scenario 3. Again we make plan and this time we get 30% at 100%. But save for one person at 90%, everyone else is in various degrees of trouble. 50% are below 50%. Half the salesforce is thinking of quitting.
Scenario 6 is an utter disaster, sadly not uncommon in early-stage startups where quotas are mis-set. Either we have hired 10 bad reps or we are setting quotas too high. Change the quota to $600K and watch everything change [10].
Scenario 7 demonstrates that you can’t actually have 80% at 100% without overperforming. Only rep 7 is over quota (and by only $200K) and the other $600K comes from the contributions of the stragglers.
If you find yourself in conversations about attainment and things start to get confusing, I’d whip out a model like this and start playing with scenarios. You can download this sheet here.
# # #
Notes
[1] Nomenclature: X% at Y% means X% of reps at or above Y% of their quota.
[2] The title is about “proving a repeatable sales process” because a common use of attainment statistics is to prove sales model repeatability.
[3] When done my way (i.e., based on productivity) every number in the model (except the one row with quota) is a realistic take on what we expect to sell. The alternative is to have every number uplifted by 20-30% and then need to do constant mental discounts. That’s too much work that’s too easily forgotten. Start your model on Earth.
[4] Some companies run two layers of cushion: quota to productivity (to account for the fact that 100% of quota is rarely realized) and then productivity to plan (to add extra cushion to increase the odds of hitting plan).
[5] The sum of the quotas for each of the reps, forgetting management layers and cushions, which can complicate things endlessly.
[6] “At” here means “at or above.” I wanted to make many sentences less wordy.
[7] People aren’t great at statistics and distributions and often screw up even simple mental math. For example, if you have a 20% cushion between quota and productivity and you say we need 80% of reps at 100% of plan, then you are also saying that the plan is to beat plan. While that might sound like a great locker room speech, it’s bad analytics. If 80% are at 100%, you hit plan. Any over-performance (and there always is) by the hundred-plus percenters takes you above plan, and any contributions from the 20% of reps below quota take you beyond that.
[8] My intent at picking 50% is both that it’s an unacceptable performance and, while you should model it out for your company, they are likely unprofitable to carry, depending on cost of sales and marketing support resources. Reps at 80% aren’t achieving plan but they are usually squarely profitable.
[9] And they’re not wrong to do so, but well, you did make plan.
[10] Orange cells are drivers/input cells that you can type in. One only hopes their OTE is $150K so it’s inline with a 4x+ quota/OTE ratio and that they don’t require heavy support resources. Then, resetting the quotas might just be the solution.
When a company is transitioning from founder-led sales (FLS) to sales-led sales (SLS), you hear the word “playbook” a lot. For early-stage companies, this rubs me the wrong way because when I hear playbook, it conjures up an image of:
A large sales enablement team
A hefty three-ring binder full of paper (or its digital equivalent)
A formal onboarding program that teaches playbook contents
And perhaps a formal sales process (e.g., MEDDIC) or methodology
That’s all great when you’re $100M+ in ARR and you’re trying to institutionalize a model that you know works — from repeated experience with scores of reps over many quarters. But for an early-stage company with less than a dozen reps and that’s still highly dependent on the founder(s) to sell software, it’s overkill.
So when these companies say they need a playbook my retort is, “no you don’t — you don’t need a playbook; you just need a handful of plays.”
What is a Playbook?
While the term gets bandied about, few seem to define it. Many companies will tell you how to make a sales playbook. For example, Pipedrive does so in a not-so-mere 4,500 words. But if the how-to-make-one guide is nine, single-spaced pages, then how big are the playbooks themselves? Usually, big. Per Pipedrive:
A sales playbook is a document that outlines your sales processes, procedures, and best practices. By following the strategies in a playbook, sales reps can increase their productivity, improve their win rates and drive revenue growth for the company. Sales playbooks typically include […] target customer profiles, stages of the sales process, how to handle customer objections, sales methodologies, sales tools and technologies, key performance indicators (KPIs), and strategic objectives.
Pipedrive’s how-to guide is a fine piece of work. It’s just way too heavy for early-stage startups. These startups can’t make large playbooks, nor should they. They don’t have the resources to build them, but far more importantly, they don’t know what to say — they simply don’t have enough experience to know what works across a wide range of buyers and situations. Sure, you can pay an intern to fill in templates, but you don’t have quality content.
That said, what’s my definition of a playbook?
A playbook is a collection of plays.
What is a Play?
My definition begs the question: what, then, is a play? So let’s define that, too.
A play is a series of steps to make in a given situation to help you win a deal.
The keywords are:
Steps: the things that the sales team needs to do. While different team members may do different things at different times, the quarterback of the deal is always the seller.
Situation: the situation for which the play is designed. For example, you might have play for leaving a deal that you don’t think is qualified (the Polite Walk Away) or for saving a deal you know you’re losing (the Hail Mary).
Win: the purpose of the play is to win the deal. As James Mason said of lawyers in The Verdict, “you’re not paid to do your best, you’re paid to win.” The same is true is in sales. The purpose of the play is to win.
An Example Play
Because I find the notion of play still somewhat amorphous, I’ll provide a concrete example.
Situation. You sell BI tools. You are competing against a hot competitor with a slick user interface that’s generally preferred by end-users to your own. One feature, in particular, gets audible wows when demoed. Your product and engineering team has recently released a similar but inferior version of that feature to help. Because the competitor knows they will win in end-user demos, they encourage selection committees to “let the users decide” by having a large end-user demo near the conclusion of the selection process. Your competitor calls their play the “End Run” because they’re running around the IT group charged with the selection to the end-users.
Steps. You take the following steps in this situation.
Build or re-use the slickest available demo of the product that you can find.
Request an end-user demo session for your company, too, justified by basic process fairness.
Demonstrate the “wow” feature several times. Know that you are likely to still lose with the end-users, but that’s not the point. You are trying to minimize the perceived gap and convince the end-users that — even if they don’t see your solution as “best” — that it’s certainly “good enough” to get the job done.
Call a meeting with the IT team to discuss security and administration. Convince them of the importance of security and the cost of administration. Show that your product, rightfully, is superior in both these areas.
Get IT to reframe the end-user vote as “input” (versus “selection”) and that they should ask the end-users two questions: which is your preferred solution and can both solutions do the job?
Win the deal when IT selects your product based on security and adminstration with the end-users’ consent that your solution is good enough to do the job.
That is a play. It’s not complicated. It’s easily taught. You can and should build tools to support its execution — e.g., the wow demo and a security and adminstration white paper.
Plays Are Applied Marketing
Are plays marketing or sales? While plays are always executed by sales, I think of building plays as applied marketing. We start with what we know about the customer and market. We add what we know about the competition — both in terms of product strengths/weaknesses and common sales tactics. Then we apply that knowledge into making a play (i.e., a series of steps) to beat them.
What Plays Do You Need?
I tihnk most startups need 3 or 4 plays, each of which can be described in less than a page (if not a single paragraph):
Replicate success. This is your primary play. If you have a few big insurance companies using your product for use-case X, then you need a play for replicating that. Who to call. What to ask. What to say. How to tell the story of your existing references. How to overcome objections. How to close.
Replace BigCo. If you have newer, better, faster, cheaper technology than an established (now “legacy”) vendor, you need a play for how to replace them. Who to call. What to ask. What to say. How to qualify. How to win. When to give up.
Beat archrival startup. If you have a head-to-head startup rival, you’ll need a play for how to beat them. This is usually a mix of product differentiators tied to use-cases combined with vision/roadmap to address objections along with strong messaging on safety, company/investor quality, and early market leadership.
Polite walk alway. As an early-stage startup you should walk away from plenty of deals, so you should get good at it. The deals you qualify out today are next year’s opportunities so treat them well and get good at slow nurture.
I’m Dave Kellogg, advisor, director, blogger, and podcaster. I am an EIR at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to enterprise software, having more than ten years’ experience in each of the CEO, CMO, and independent director roles in companies from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of Host Analytics, where we quintupled ARR while halving customer acquisition costs, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of MarkLogic, which we grew from zero to $80M over six years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues.
I love disruptive startups and and have had the pleasure of working in varied capacities with companies including Bluecore, FloQast, Gainsight, Hex, Logikcull, MongoDB, Pigment, Recorded Future, Tableau, and Unaric.
I currently serve on the boards of Cyber Guru, Scoro, TechWolf, Vic.ai, and Widewail. I have previously served on the boards of Alation, Aster Data, Granular, Nuxeo, Profisee, and SMA Technologies.
