Category Archives: Finance

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.

Next-Generation Planning and Finance, A Broader and Slightly Deeper Look

This post was prompted by feedback to the last prediction in my 2021 annual predictions post, The Rebirth of Planning and Enterprise Performance Management.  Excerpt:

EPM 1.0 was Hyperion, Arbor, and TM1. EPM 2.0 was Adaptive Insights, Anaplan, and Planful (nee Host Analytics).  EPM 3.0 is being born today.  If you’ve not been tracking this, here a list of next-generation planning startups …

Since that post, I’ve received feedback with several more startups to add to the list and a request for a little more color on each one.  That’s what I’ll cover in this post.  I can say right now this got bigger, and took way longer, than I thought it would at the outset.  That means two things:  there may be more mistakes and omissions than usual and wow if I thought the space was being reborn before, I really think it now.  Look at how many of these firms were founded in the past two years!

Order is alphabetical.  Links are to sources.  All numbers are best I could find as of publication date (and I have no intent to update).  I have added and/or removed companies from the prior post based on feedback and my subjective perception as to whether I think they qualify as “next generation” planning.  Note that I have several and varied relationships with some of these companies (see prior post and disclaimers).  List is surely not inclusive of all relevant companies.

  • Allocadia.  Founded in Vancouver in 2010 by friends from Business Objects / Crystal Reports, this is a marketing performance management company that has raised $24M in capital and has 125 employees.  Marketing planning is a real problem and they’re taking, last I checked, the enterprise approach to it.  They have 93 reviews and 4.1 stars on G2.
  • Causal.  Founded in 2019 in London.  I can’t find them in Crunchbase, but their site shows they have seed capital from Coatue and Passion Capital.  They promise, among other things, to “make finance beautiful” and the whole thing strikes me as a product-led growth strategy for a new tool to build financial models outside of traditional spreadsheets.
  • Decipad.  Co-founded in late 2020 in the UK by friend, former MarkLogic consultant, and serial entrepreneur Nuno Job, Decipad is a seed-stage, currently fewer than 10 employee, startup that, last I checked, was working on a low-code product for planning and modeling for early-stage companies.
  • Finmark.  Raleigh-based, and founded in 2020, this company has raised $5M in seed capital from a bevy of investors including Y Combinator, IDEA Fund, Draper, and Bessemer.  The company has about 50 employees, a product in early access mode, and is a product built “by founders, for founders” to provide integrated finance for startups.
  • Grid.  This company offers a web-based tool that appears to layer atop spreadsheets, using them as a data source to build reports, dashboards and apps.  The company was founded in 2018, has around 20 people, and is based in Reykjavik.  The founder/CEO previously served as head of product management at Qlik and is a “proud data nerd.”  Love it.
  • LiveFlow was founded in 2021, based in Redwood City, has raised about $500K in pre-seed capital from Y Combinator and Seedcamp.  The company offers a spreadsheet that connects to your real-time data, supporting the creation of timely reports and dashboards.  Connectivity appears to be the special sauce here, and it’s definitely a problem that needs to be solved better.
  • OnPlan.  Founded in 2106 in San Francisco by serial entrepreneur and new friend, David Greenbaum, OnPlan is a financial modeling, scenario analysis, and forecasting tool.  The company has raised an undisclosed amount of angel financing and has over 30 employees.  Notably, they are building atop Google Sheets which allows them “stand on the shoulders of giants” and provide a rare option that is, I think, Google-first as opposed to Excel-first or Excel-replacement.
  • Plannuh.  Pronounced with a wicked Southie accent, Plannuh is Boston for Planner, and a marketing planning package that helps marketers create and manage plans and budgets.  Founded by (a fellow) former $1B company CMO, Peter Mahoney, the company has raised $4M and has over 30 employees.  As mentioned, I think marketing planning is a real problem and these guys are taking a velocity approach to it.  They have 5.0 stars on G2 across five reviews.  I’m an advisor and wrote the foreword to their The Next CMO book.
  • Pry.  Founded in San Francisco in 2019 by two startup-experienced Cal grads (Go Bears!), with investment from pre-seed fund Nomo Ventures, Pry has fewer than 10 employees, and a vision to make it simple for early-stage companies to manage their budget, hiring plan, financial models, and cash.
  • Runway.  This company is backed with a $4.5M seed round from the big guns at A16Z.  I can’t find them on Crunchbase and their website has the expected “big thinking but no detail” for a company that’s still in stealth.  Currently at about 10 people.
  • Stratify.  Founded in 2020 in Seattle, this company has raised $5.0M to pursue real-time and collaborative budgeting and forecasting to support “continuous planning” (which is reminiscent of Planful’s messaging).  Both the founder and the lead investor have enterprise roots (with SAP / Concur) and plenty of startup experience.  The company has fewer than 10 employees today.
  • TruePlan.  Founded in 2020, with three employees, and seemingly bootstrapped I may have found these guys on the early side.  While the product appears still in development, the vision looks clear:  dynamic headcount management, that ties together the departmental (budget owner) manager, finance, recruiting, and people ops.  Workforce planning is a real problem, let’s see what they do with it.
  • VaretoFounded in 2020 in Mountain View, with fewer than 10 employees and some pretty well pedigreed founders, the company seeks to help with strategic finance, reporting, and planning.  The website is pretty tight-lipped beyond that and I can’t find any public financing information.

Thanks to Ron Baden, Nuno Job, and Bill Rausch for helping me track down so many companies.

(Added Valsight 2/10/21.)

Appearance on the Sage SaaS Success Series: Best Practices in Forecasting for Fundraising

Just a quick post to highlight that I’ll be speaking in a panel discussion with Mihir Jobalia, managing director of technology investment banking at KPMG, and David Appel, head of the subscription and SaaS vertical at Sage Intacct, on  2/23 at 11AM pacific time.  It’s part of a four-part SaaS Success Series, hosted by Sage Intacct, with episodes including:

  • The 100-Day Ramp Plan for New Finance Hires
  • What is the Next SaaS Finance Technology Stack?
  • 3 Best Practices for Forecasting and Fundraising (our session)
  • How to Plan for Your ASC 606 Revenue Recognition Scenario

They all look super  interesting. Well, except for the last one — just kidding, #revrec matters (and ASC 606 does some interesting things, in particular to subscription-based companies not delivering via an online service).

Thanks to David Appel for inviting me.  I look forward to speaking with David and Mihir on the panel.

I hope you can join us.  Those interested can register for the series here.

(Revised 2/18 to remove speaker who dropped out.)

Are We Due for a SaaSacre?

I was playing around on the enterprise comps [1] section of Meritech‘s website today and a few of the charts I found caught my attention.  Here’s the first one, which shows the progression of the EV/NTM revenue multiple [2] for a set of 50+ high-growth SaaS companies over the past 15 or so years [3].

meritech saas multiples

While the green line (equity-value-weighted [4]) is the most dramatic, the one I gravitate to is the blue line:  the median EV/NTM revenue multiple.  Looking at the blue line, you can see that while it’s pretty volatile, eyeballing it, I’d say it normally runs in the range between 5x and 10x.  Sometimes (e.g., 2008) it can get well below 5x.  Sometimes (e.g., in 2013) it can get well above 10x.  As of the last data point in this series (7/14/20) it stood at 13.8x, down from an all-time high of 14.9x.  Only in 2013 did it get close to these levels.

If you believe in regression to the mean [5], that means you believe the multiples are due to drop back to the 5-10 range over time.  Since mean reversion can come with over-correction (e.g., 2008, 2015) it’s not outrageous to think that multiples could drop towards the middle or bottom of that range, i.e., closer to 5 than 10 [6].

Ceteris paribus, that means the potential for a 33% to 66% downside in these stocks. It also suggests that — barring structural change [7] that moves baseline multiples to a different level — the primary source of potential upside in these stocks is not continued multiple expansion, but positive NTM revenue surprises [8].

I always love Rule of 40 charts, so the next fun chart that caught my eye was this one.  meritech r40 score While this chart doesn’t speak to valuations over time, it does speak to the relationship between a company’s Rule of 40 Score and its EV/NTM revenue multiple.  Higher valuations primarily just shift the Y axis, as they have done here, uplifting the maximum Y-value by nearly three times since I last blogged about such a chart [9].  The explanatory power of the Rule of 40 in explaining valuation multiple is down since I last looked, by about half from an R-squared of 0.58 to 0.29.  Implied ARR growth alone has a higher explanatory power (0.39) than the Rule of 40.

To me, this all suggests that in these frothy times, the balance of growth and profit (which is what Rule of 40 measures) matters less than other factors, such as growth, leadership, scarcity value and hype, among others.

Finally, to come back to valuation multiples, let’s look at a metric that’s new to me, growth-adjusted EV/R multiples.

meritech r40 growth adjusted

I’ve seen growth-adjusted price/earnings ratios (i.e., PEG ratios) before, but I’ve not seen someone do the same thing with EV/R multiples.  The basic idea is to normalize for growth in looking at a multiple, such as P/E or — why not — EV/R.  For example, Coupa, trading at (a lofty) 40.8x EV/R is growing at 21%, so divide 40.8 by 21 to get 1.98x.  Zoom, by comparison looks to be similarly expensive at 38.3x EV/R but is growing at 139%, so divide 38.3 by 139 to get 0.28x, making Zoom a relative bargain when examined in this light [10].

This is a cool metric.  I like financial metrics that normalize things [11].  I’m surprised I’ve not seen someone do it to EV/R ratios before.  Here’s an interesting observation I just made using it:

  • To the extent a “cheap” PE firm might pay 4x revenues for a company growing 20%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • To the extent a “crazy” VC firm might pay 15x revenues for a company growing at 75%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • The observant reader may notice they are both paying the same ratio for growth-adjusted EV/R. Given this, perhaps the real difference isn’t that one is cheap and the other free-spending, but that they pay the same for growth while taking on very different risk profiles.

The other thing the observant reader will notice is that in both those pseudo-random yet nevertheless realistic examples, the professionals were paying 0.2.  The public market median today is 0.7.

See here for the original charts and data on the Meritech site.

Disclaimer:  I am not a financial analyst and do not make buy/sell recommendations.  I own positions in a wide range of public and private technology companies.  See complete disclaimers in my FAQ.

# # #

Notes 
[1] Comps = comparables.

[2] EV/NTM Revenue = enterprise value / next twelve months revenue, a so-called “forward” multiple.

[3] Per the footer, since Salesforce’s June, 2004 IPO.

[4] As are most stock indexes. See here for more.

[5] And not everybody does.  People often believe “this time it’s different” based on irrational folly, but sometimes this time really is different (e.g., structural change).  For example, software multiples have structurally increased over the past 20 years because the underlying business model changed from one-shot to recurring, ergo increasing the value of the revenue.

[6] And that’s not to mention external risk factors such as pandemic or election uncertainty.  Presumably these are already priced into the market in some way, but changes to how they are priced in could result in swings either direction.

[7] You might argue a scarcity premium for such leaders constitutes a form of structural change. I’m sure there are other arguments as well.

[8] To the extent a stock price is determined by some metric * some multiple, the price goes up either due to increasing the multiple (aka, multiple expansion) or increasing the metric (or both).

[9] While not a scientific way to look at this, the last time I blogged on a Rule of 40 chart, the Y axis topped out at 18x, with the highest data point at nearly 16x.  Here the Y axis tops out at 60x, with the highest data point just above 50x.

[10] In English, to the extent you’re paying for EV/R multiple in order to buy growth, Zoom buys you 7x more growth per EV/R point than Coupa.

[11] As an operator, I don’t like compound operational metrics because you need to un-tangle them to figure out what to fix (e.g., is a broken LTV/CAC due to LTV or CAC?), but as investor I like compound metrics as much as the next person.

 

The Pipeline Chicken or Egg Problem

The other day I heard a startup executive say, “we will start to accelerate sales hiring — hiring reps beyond the current staffing levels and the current plan — once we start to see the pipeline to support it.”

What comes first: the pipeline or the egg?  Or, to unmix metaphors, what comes first:  the pipeline or the reps to prosecute it?  Unlike the chicken or the egg problem, I think this one has a clear answer: the reps.

My answer comes part from experience and part from math.

First, the experience part:  long ago I noticed that the number of opportunities in the pipeline of a software company tends to be a linear function of the number of reps, with a slope in the 12-18 range as a function of business model [1].  That is, in my 12 years of being a startup CEO, my all-quarters, scrubbed [2] pipeline usually had somewhere between 12 and 18 opportunities per rep and the primary way it went up was not by doing more marketing, but by hiring more reps.

Put differently, I see pipeline as a lagging indicator driven by your capacity and not a leading indicator driven by opportunity creation in your marketing funnel.

Why?  Because of the human factor:  whether they realize it or not, reps and their managers tend to apply a floating bar on opportunity acceptance that keeps them operating around their opportunity-handling capacity.  Why’s that?  It’s partially due to the self-fulfilling 3x pipeline prophecy:  if you’re not carrying enough pipeline, someone’s going to yell at you until you do, which will tend to drop your bar on opportunity acceptance.  On the flip side, if you’re carrying more opportunities than your capacity — and anyone is paying attention — your manager might take opportunities away from you, or worse yet hire another rep and split your territory.  These factors tends to raise the bar, so reps cherry pick the best opportunities and reject lesser ones that they’d might otherwise accept in a tougher environment.

So unless you’re running a real machine with air-tight definitions and little/no discretion (which I wouldn’t advise), the number of opportunities in your pipeline is going to be some constant times the number of reps.

Second, the math part.  If you’re running a reasonably tight ship, you have a financial model and an inverted funnel model that goes along with it.  You’re using historical costs and conversion rates along with future ARR targets to say, roughly, “if we need $4.0M in New ARR in 3 quarters, and we insert a bunch of math, then we’re going to need to generate 400 SALs this quarter and $X of marketing budget to do it.”  So unless there’s some discontinuity in your business, your pipeline generation doesn’t reflect market demand; it reflects your financial and demandgen funnel models.

To paraphrase Chester Karrass, you don’t get the pipeline you deserve, you get the one you plan for.  Sure, if your execution is bad you might fall significantly short on achieving your pipeline generation goal.  But it’s quite rare to come in way over it.

So what should be your trigger for hiring more reps?  That’s probably the subject of another post, but I’d look first externally at market share (are you gaining or losing, and how fast) and then internally at the CAC ratio.

CAC is the ultimate measure of your sales & marketing efficiency and looking at it should eliminate the need to look more deeply at quota attainment percentages, close rates, opportunity cost generation, etc.  If one or more of those things are badly out of whack, it will show up in your CAC.

So I’d say my quick rule is if your CAC is normal (1.5 or less in enterprise), your churn is normal (<10% gross), and your net dollar expansion rate is good enough (105%+), then you should probably hire more reps.  But we’ll dive more into that in another post.

# # #

Notes

[1]  It’s a broad range, but it gets tighter when you break it down by business model.  In my experience, roughly speaking in:

  • Classic enterprise on-premises ($350K ASP with elephants over $1M), it runs closer to 8-10
  • Medium ARR SaaS ($75K ASP), it runs from 12-15
  • Corporate ARR SaaS ($25K ASP) where it ran 16-20

[2] The scrubbed part is super important.  I’ve seen companies with 100x pipeline coverage and 1% conversation rates. That just means a total lack of pipeline discipline and ergo meaningless metrics.  You should have written definitions of how to manage pipeline and enforce them through periodic scrubs.  Otherwise you’re building analytic castles in the sand.

How Startup CEOs Should Think About the Coronavirus, Part II

[Updated 3/10 12:09]

This is part II in this series. Part I is here and covers the basics of management education, employee communications, and simple steps to help slow virus transmission while keeping the business moving forward.

In this part, we’ll provide:

  • A short list of links to what other companies are doing, largely when it comes to travel and in-office work policies.
  • A discussion of financial planning and scenario analysis to help you financially navigate these tricky waters.

I have broken out the list of useful information links and resources (that was formerly in this post) to a separate, part III of this series.

What Other Companies are Saying and Doing

Relatively few companies have made public statements about their response policies. Here are a few of the ones who have:

Financial Planning and Scenario Analysis: Extending the Runway

It’s also time to break out your driver-based financial model, and if you don’t have one, then it’s time to have your head of finance (or financial planning & analysis) build one.

Cash is oxygen for startups and if there are going to be some rough times before this threat clears, your job is to make absolutely sure you have the cash to get through it. Remember one of my favorite all-time startup quotes from Sequoia founder Don Valentine: “all companies go out of business for the same reason. They run out of money.”

In my opinion you should model three scenarios for three years, that look roughly like:

  • No impact. You execute your current 2020 operating plan. Then think about the odds of that happening. They’re probably pretty low unless you’re in a counter-cyclical business like videoconferencing (in which case you probably increase targets) or a semi-counter-cyclical one like analytics/BI (in which case maybe you hold them flat).
  • 20% bookings impact in 2020. You miss plan bookings targets by 20%. Decide if you should apply this 20% miss to new bookings (from new customers), expansion bookings (new sales to existing customers), renewal bookings — or all three. Or model a different percent miss on each of those targets as it makes sense for your business. The point here is to take a moderately severe scenario and then determine how much shorter this makes your cash runway. Then think about steps you can take to get that lost runway back, such as holding costs flat, reducing costs, raising debt, or — if you’re lucky and/or have strong insiders — raising equity.
  • 40% bookings impact in 2020. Do the same analysis as in the prior paragraph but with a truly major bookings miss. Again, decide whether and to what extent that miss hits new bookings, expansion bookings, and renewal bookings. Then go look at your cash runway. If you have debt make sure you have all covenant compliance tests built into your model that display green/red — you shouldn’t have to notice a broken covenant, it should light up in big letters (YES/NO) in a good model. Then, as in the prior step, think about how to get that lost runway back.

Once you have looked at and internalized these models, it’s time for you and your CFO to call your lead investors to discuss your findings. And then schedule a discussion of the scenario analysis at your next board meeting.

Please note that it’s not lost on me that accelerating out of the turn when things improve can be an excellent way to grab share in your market. But in order to so, you need to have lots of cash ready to spend in, say, 6-12 months when that happens. Coming out of the corner on fumes isn’t going to let you do that. And, as many once-prodigal, now-thrifty founders have told me: “the shitty thing is that once you’ve spent the money you can’t get it back.” Without dilution. With debt. Maybe without undesirable structure and terms.

Now is the time to think realistically about how much fuel you have in the tank, if you can get more, how long should it last, and how much you want in the tank 6-12 months out.

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

I’ve seen numerous startups try numerous ways to calculate their sales capacity.  Most are too back-of-the-envelope and to top-down for my taste.  Such models are, in my humble opinion, dangerous because the combination of relatively small errors in ramping, sales productivity, and sales turnover (with associated ramp resets) can result in a relatively big mistake in setting an operating plan.  Building off quota, instead of productivity, is another mistake for many reasons [1].  

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

What does such a model need to take into account?

  • Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped).  This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement.
  • Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
  • Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters [2].  This should be based in historical data as well.
  • Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
  • Judgment, the model should have the built-in ability to let the CEO and/or sales VP manually adjust the output and provide analytical support for so doing [3].
  • Quota over-assignment, the extent to which you assign more quota at the “street” level (i.e., sum of the reps) beyond the operating plan targets
  • For extra credit and to help maintain organizational alignment — while you’re making a bookings model, with a little bit of extra math you can set pipeline goals for the company’s core pipeline generation sources [4], so I recommend doing so.

If your company is large or complex you will probably need to create an overall bookings model that aggregates models for the various pieces of your business.  For example, inside sales reps tend to have lower quotas and faster ramps than their external counterparts, so you’d want to make one model for inside sales, another for field sales, and then sum them together for the company model.

In this post, I’ll do two things:  I’ll walk you through what I view as a simple-yet-comprehensive productivity model and then I’ll show you two important and arguably clever ways in which to use it.

Walking Through the Model

Let’s take a quick walk through the model.  Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model.

You need to enter data into the model for 1Q20 (let’s pretend we’re making the model in December 2019) by entering what we expect to start the year with in terms of sales reps by tenure (column D).  The “first/hired quarter” row represents our hiring plans for the year.  The rest of this block is a waterfall that ages the rep downward as we move across quarters.  Next to the block ramp assumption, which expresses, as a percentage of steady-state productivity, how much we expect a rep to sell as their tenure increases with the company.  I’ve modeled a pretty slow ramp that takes five quarters to get to 100% productivity.

To the right of that we have more assumptions:

  • Annual turnover, the annual rate at which sales reps leave the company for any reason.  This drives attriting reps in row 12 which silently assumes that every departing rep was at steady state, a tacit fairly conservative assumption in the model.
  • Steady-state productivity, how much we expect a rep to actually sell per year once they are fully ramped.
  • Quota over-assignment.  I believe it’s best to start with a productivity model and uplift it to generate quotas [5]. 

The next block down calculates ramped rep equivalents (RREs), a very handy concept that far too few organizations use to convert the ramp-state to a single number equivalent to the number of fully ramped reps.  The steady-state row shows the number of fully ramped reps, a row that board members and investors will frequently ask about, particularly if you’re not proactively showing them RREs.

After that we calculate “productivity capacity,” which is a mouthful, but I want to disambiguate it from quota capacity, so it’s worth the extra syllables.  After that, I add a critical row called judgment, which allows the Sales VP or CEO to play with the model so that they’re not potentially signing up for targets that are straight model output, but instead also informed by their knowledge of the state of the deals and the pipeline.  Judgment can be negative (reducing targets), positive (increasing targets) or zero-sum where you have the same annual target but allocate it differently across quarters.

The section in italics, linearity and growth analysis, is there to help the Sales VP analyze the results of using the judgment row.  After changing targets, he/she can quickly see how the target is spread out across quarters and halves, and how any modifications affect both sequential and quarterly growth rates. I have spent many hours tweaking an operating plan using this part of the sheet, before presenting it to the board.

The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model.  This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment.  Ideally this figure dovetails into a quota-assignment model.

Finally, while we’re at it, we’re only a few clicks away from generating the day-one pipeline coverage / contribution goals from our major pipeline sources: marketing, alliances, and outbound SDRs.  In this model, I start by assuming that sales or customer success managers (CSMs) generate the pipeline for upsell (i.e., sales to existing customers).  Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers).  So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages [6].  

Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.

Two Clever Ways to Use the Model

The sad reality is that this kind of model gets a lot attention at the end of a fiscal year (while you’re making the plan for next year) and then typically gets thrown in the closet and ignored until it’s planning season again. 

That’s too bad because this model can be used both as an evaluation tool and a predictive tool throughout the year.

Let’s show that via an all-too-common example.  Let’s say we start 2020 with a new VP of Sales we just hired in November 2019 with hiring and performance targets in our original model (above) but with judgment set to zero so plan is equal to the capacity model.

Our “world-class” VP immediately proceeds to drive out a large number of salespeople.  While he hires 3 “all-star” reps during 1Q20, all 5 reps hired by his predecessor in the past 6 months leave the company along with, worse yet, two fully ramped reps.  Thus, instead of ending the quarter with 20 reps, we end with 12.  Worse yet, the VP delivers new ARR of $2,000K vs. a target of $3,125K, 64% of plan.  Realizing she has a disaster on her hands, the CEO “fails fast” and fires the newly hired VP of sales after 5 months.  She then appoints the RVP of Central, Joe, to acting VP of Sales on 4/2.  Joe proceeds to deliver 59%, 67%, and 75% of plan in 2Q20, 3Q20, and 4Q20.

Our question:  is Joe doing a good job?

At first blush, he appears more zero than hero:  59%, 67%, and 75% of plan is no way to go through life.

But to really answer this question we cannot reasonably evaluate Joe relative to the original operating plan.  He was handed a demoralized organization that was about 60% of its target size on 4/2.  In order to evaluate Joe’s performance, we need to compare it not to the original operating plan, but to the capacity model re-run with the actual rep hiring and aging at the start of each quarter.

When you do this you see, for example, that while Joe is constantly underperforming plan, he is also constantly outperforming the capacity model, delivering 101%, 103%, and 109% of model capacity in 2Q through 4Q.

If you looked at Joe the way most companies look at key metrics, he’d be fired.  But if you read this chart to the bottom you finally get the complete picture.  Joe is running a significantly smaller sales organization at above-model efficiency.  While Joe got handed an organization that was 8 heads under plan, he did more than double the organization to 26 heads and consistently outperformed the capacity model.  Joe is a hero, not a zero.  But you’d never know if you didn’t look at his performance relative to the actual sales capacity he was managing.

Second, I’ll say the other clever way to use a capacity model is as a forecasting tool. I have found a good capacity model, re-run at the start of the quarter with then-current sales hiring/aging is a very valuable predictive tool, often predicting the quarterly sales result better than my VP of Sales. Along with rep-level, manager-level, and VP-level forecasts and stage-weighted and forecast-category-weighted expected pipeline values, you can use the re-run sales capacity model as a great tool to triangulate on the sales forecast.

You can download the four-tab spreadsheet model I built for this post, here.

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Notes

[1] Starting with quota starts you in the wrong mental place — what you want people to do, as opposed to productivity (what they have historically done). Additionally, there are clear instances where quotas get assigned against which we have little to no actual productivity assumption (e.g., a second-quarter rep typically has zero productivity but will nevertheless be assigned some partial quota). Sales most certainly has a quota-allocation problem, but that should be a separate, second exercise after building a corporate sales productivity model on which to base the operating plan.

[2] A typically such vector might be (0%, 25%, 50%, 100%) or (0%, 33%, 66%, 100%) reflecting the percentage of steady-state productivity they are expected to achieve in their first, second, third, and fourth quarters of employment.

[3] Without such a row, the plan is either de-linked from the model or the plan is the pure output of the model without any human judgement attached. This row is typically used to re-balance the annual number across quarters and/or to either add or subtract cushion relative to the model.

[4] Back in the day at Salesforce, we called pipeline generation sources “horsemen” I think (in a rather bad joke) because there were four of them (marketing, alliances, sales, and SDRs/outbound). That term was later dropped probably both because of the apocalypse reference and its non gender-neutrality. However, I’ve never known what to call them since, other than the rather sterile, “pipeline sources.”

[5] Many salesops people do it the reverse way — I think because they see the problem as allocating quota whereas I see the the problem as building an achievable operating plan. Starting with quota poses several problems, from the semantic (lopping 20% off quota is not 20% over-assignment, it’s actually 25% because over-assignment is relative to the smaller number) to the mathematical (first-quarter reps get assigned quota but we can realistically expect a 0% yield) to the procedural (quotas should be custom-tailored based on known state of the territory and this cannot really be built into a productivity model).

[6] One advantages of having those percentages here is they are placed front-and-center in the company’s bookings model which will force discussion and agreement. Otherwise, if not documented centrally, they will end up in different models across the organization with no real idea of whether they either foot to the bookings model or even sum to 100% across sources.