Congratulations, You’ve Created a Category. Now What?

(Revised 06/27/20)

I was talking to an old friend the other day who’s marketing chief at a successful infrastructure startup.  “Congratulations,” I said, “I know it was a long slog, but after about a decade of groundwork it looks like things have really kicked in.  I hear your company’s name all the time, I’m told business is doing great, and Gartner literally can’t stop talking about your technology and category.”

“Yes, we’ve successfully created a category,” he said, “But I have one question.  Now what?”

It reminded me, just for a minute, of the ending of The Candidate.

While it’s definitely a high-class problem, it’s certainly a great question and one you don’t hear very often.  These days a lot of very clever people are out dispensing advice on how to create a category — including some wise folks who first dissuade you from doing so — but nobody’s saying much about what to do once you’ve created one.  That’s the topic of this post. category2

Bad Fates That Can Befall Category Creators
Let’s start with the inverse.  Once you’ve created a category, what bad things can happen to it?

  • It can be superannuated.  Technology advances such that it’s not needed any more.  Think:  buggy whips or record cleaners [1].
  • You can lose it to someone else.  Lotus lost spreadsheets to Microsoft.  IBM lost databases to Oracle [2].  Through a more oblique attack, Siebel lost SFA to Salesforce.  Great categories attract new entrants, often big ones.
  • It can be enveloped, either as a feature by a product or as a sub-product by a suite.  Spellcheckers were enveloped as features by word processing products, which were in turn enveloped by office suites.  See the death of WordPerfect [3].

Given that we don’t want any of these things to happen to your category, what should we do about it?  I’ll answer that after a quick aside on my views on categories.

My Principles of Categories
Here are my principles of enterprise software categories:

  • Companies don’t name categories, analysts do.  Companies might influence analysts in naming a new category, but in the end analysts name categories, not vendors [6].
  • Categories sometimes converge, but not always.  Before the SaaS era, enterprise software categories almost always converged because IT was all-powerful and saw its role as entropy minimization [7].  SaaS empowered line of business buyers to end-run IT because they could simply buy an app without much IT support or approval [8].  This is turn led to category proliferation and serious “riches in the niches” where specific, detailed apps like account reconciliation have born multi-billion-dollar companies.
  • Category convergence is about buyers.  Analysts like predicting category convergence so much they get it wrong sometimes.  For example, while the analyst prediction that BI and Planning apps would converge [9] served as the face that launched 1000 ships for vendor consolidation [10], the reality was that BI was purchased by the VP of Analytics while Planning was purchased by the VP of FP&A.  You could put Brio and Hyperion under one roof via acquisition, but real consolidation never happened [11] [12].  Beware analyst-driven shotgun weddings between categories sold to different buyers.  They won’t result in lasting marriages.
  • In category definition, the buyer is inseparable from the category.  Each category is a two-sided coin that defines the buyer on one side and the software category on the other [13].  For example, when categories converge it’s either because the buyer stayed the same and decided to purchase more broadly or the buyer changed and what they wanted to buy changed along with it.  But if there is no buyer, there is no category.

What’s a Category Creator To Do?  Lead!
Having contemplated the bad things that can happen to your category and reviewed some basic principles of categories, there is one primary answer to the question:  lead.

You need to lead in three ways:

  • Grow like a weed.  Now is the time to invest in driving growth.  Nothing attracts competition like fallow land in a new category.  You created a category, you’re presumably the market share leader in the category, and now your job is to make sure you stay that way.  Now is the time to raise lots of VC and spend up to $1.70 to purchase each new dollar of ARR [13A].
  • Market your category leadership.  Tech buyers love to buy from leaders because buying from leaders is safe.  Reinforce your position as the category leader until you’re tired of hearing it.  Then do it again.  Never get bored with your own marketing.
  • Lead the evolution of your category by talking about your vision and your plan to realize it.  This makes you a safe choice because customers know you’re not resting on your laurels.  It also forces your would-be competitors to shoot at a moving target.

The vision for category evolution typically takes one of three forms:

  • Double down.  Make your thing the best thing in the market.  Stay incredibly close to your customers.  Understand and cater to their precise needs.  Your strategy is thus category defense via customer intimacy.  You simply know the buyer better.  Large companies can’t put their best people on everything, so this works when your best people are better than their average ones, they don’t put a massive investment in the space (instead preferring a good-enough solution), and the buyer cares enough to want to buy the best and can continue to do so [14].
  • Build out (i.e., lateral expansion)Move into adjacent categories, ideally sold to your existing buyer, giving yourself economies of scale in go-to-market and your buyer the ability to buy multiple products on one platform [15].  GainSight’s move into product analytics is one example.  Another is Salesforce’s systematic move across buyers, from VP of Sales to VP of Service to VP of Marketing.  This strategy works when you can afford to build or acquire into the adjacent category and, if the category involves a different buyer, that you can afford to invest in the major transition from being a single-buyer to a multi-buyer firm [16].
  • Build up (i.e., vertical expansion) [17]. Build up from your platform to create one or more applications atop it.  An ancient example would be Oracle expanding from databases into applications [18] which was first attempted via in-house development.  Anaplan is a contemporary example.  They first launched a multidimensional planning platform, had trouble selling the raw engine in finance (a more saturated market with more mature competition), shifted to build sales planning applications atop their platform, and successfully used sales planning as their beachhead market.  Once that vertical (i.e., upward) move from platform to application was successful, they then bridged (now laterally) into finance and later into supply chain applications.

What If You Can’t Afford to Lead?
But say you can’t afford any of those strategies.  Suppose you’re not a particularly well-funded company and your market is being attacked on all sides, by startups and megavendors alike.  What if staving off those attacks is not a viable strategy.  Then what?

If you’re at risk losing leadership in your category, then your strategy needs to be segment.  Pick a segment of the market you created and lead it.  That segment could be on several dimensions.

  • Size, by focusing on SMB, mid-market, or enterprise customers only — this works when requirements (or business model) vary significantly with size.
  • Vertical, by focusing on one or two vertical industries — ideally those with idiosyncratic requirements that can serve as entry barriers to horizontal players.
  • Use-case, by focusing on a specific use-case of a platform that supports multiple use-cases.  For example, what if Ingres, instead of focusing on appdev tools after placing 4th round I of the RDBMS market, instead had focused on data warehousing, a distinct use-case and one to which the technology was well-suited?

Conclusion
If you’re reading this because you’ve created a category, congratulations.  You’ve done an incredibly difficult thing.  Hopefully, this post helps you think about your most important question going forward:  now what?

# #  #

Notes

[1] I struggle to find software examples of this because the far more common fate is envelopment, typically into a feature — e.g., spellchecker.  I suspect it happens more in hardware as the underlying components get smarter, they eliminate the need for higher-level controllers and caches.

[2] Despite both inventing the relational database and being the leader in the prior-generation database market with IMS.

[3] The precise cause of death is still debated and a final lawsuit concluded less than a decade ago.

[4] Software industry evolution led to the SaaS model, which then put huge importance on renewals which in turn led to the creation of the VP of Customer Success role which created both the demand for and buyer of Customer Success software.

[5] And either way, a great company.  (I know both the founder and the CEO, so see my disclaimer.  I can say I’ve also been a customer and a happy one.)

[6] I credit Arnold Silverman with pointing this out to me so clearly.

[7] To reduce the degree of disorder in a company’s software stack, IT had a strong tendency to prefer one-stop-shop value propositions over best-of-breed.  Ergo, vendors incented by economies of scale in go-to-market, were naturally aligned with buyers who wanted to buy more from fewer vendors.  Both forces pushed towards developing suites, either in-house or through acquisition.

[8] As I did in the early 2000s when I was CMO of a $1B company and the CIO said I needed to wait 4 years for lead management in Europe during our CRM deployment.  “That’s funny,” I thought, “we have leads today and if I wait 4 years for lead management, I can assure you of only two things:  I won’t be CMO anymore and the CIO will be the only person coming to my going-away party.”  That’s when I bought Salesforce.

[9] That was the initial use of the category name enterprise performance management (EPM), which later evolved before eventually, and only of late, being retired.  A key point here is that while these categories organ-rejected each other, that took place literally over the course of decades.  Thus, paradoxically, you likely would have been “dead right” as a BI vendor if you rejected the inclusion of financial planning in 2003 .

[10] Cognos acquiring Adaytum, Business Objects acquiring SRC and Cartesys, and Hyperion acquiring Brio, among others.

[11] Meaning you could ask someone who worked in the organization “which side” they worked on, and they would answer without hesitation.  You can’t sell financial planning systems without significant domain expertise that the BI side lacked, and that was more about DNA than training.  (For example, most EPM sales consultants had years of experience working in corporate finance departments before changing careers.)  It was more conglomeration than consolidation.

[12] Amazingly, this pattern repeated itself within EPM in the past decade.  EPM  was redefined as the convergence of financial planning with financial consolidation, both within the finance department, but again sold to different buyers.  Planning is sold to the VP of FP&A, Consolidation to the Corporate Controller.  While both report to the CFO, they are two different roles, typically staffed with two very different people.  Again, the shotgun wedding ended in divorce.

[13] Each category has one primary buyer.  A given buyer may buy in several different categories.  As a marketer, the former statement is 10x more important than the latter.

[13A] See my post on the CAC ratio.  Data source, the KeyBanc 2019 SaaS survey, shows median of $1.14 with mid 50-percentile range of $0.77 to $1.71.

[14] The tension here is between letting, e.g., the VP FP&A purchase their own best-of-breed Planning product versus a good-enough Planning module subsumed into a broader ERP suite decided upon by the CFO.  This is a real example because Planning exists on both sides today; there remain several successful SaaS planning vendors selling best-of-breed outside the context of a financial suite while most ERP vendors bundle good-enough Planning into their suite.

[15] When accomplished via M&A, the single-platform benefits are typically limited to pre-defined integration but can hopefully over time — sometimes a long time (think Oracle Fusion) — become realized.

[16] Typically this means creating product-line general managers along with specialized overlay sales and sales consultants, product management, product marketing, and consulting teams.  It also means the more difficult task of going to market with products at differing levels of maturity, something very hard to master in my experience.  Finally, in apps at least, the more you are multi-buyer, the more IT needs to get involved, and the firm must master not only the art of the sale to the various business buyers, but to IT as well.  Salesforce has done this masterfully.

[17] Vertical in the sense of up, i.e., atop your platform; not vertical in the sense of focusing on vertical markets.

[18] Which, for ancient software historians, was the failed strategy that Oracle gave a mighty try before giving up and acquiring PeopleSoft in 2005, the first in a long series of applications acquisitions.

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Measuring Ramped and Steady-State Sales Productivity: The Rep Ramp Chart

In prior posts I have discussed how to make a proper sales bookings productivity model and how to use the concept of ramped rep equivalents (RREs) in sales analytics and modeling. When it comes to setting drivers for both, corporate leaders tend to lean towards benchmarks and industry norms for the values.  For example, two such common norms are:

  • Setting steady-state (or terminal) productivity at $1,200K of new ARR per rep in enterprise SaaS businesses
  • Using a {0%, 25%, 50%, 100%} productivity ramp for new salesreps in their {1st, 2nd, 3rd, 4th} quarters with the company (and 100% thereafter)

In this post, I’ll discuss how you can determine if either of those assumptions are reasonable at your company, given its history.

To do so, I’m introducing one of my favorite charts, the Rep Ramp Chart.  Unlike most sales analytics, which align sales along fiscal quarters, this chart aligns sales relative to a rep’s tenure with the company.

You start by listing every rep your company has ever hired [1] in order by hire date.  You then record their sales productivity (typically measured in new ARR bookings [2]) for their series of quarters with the company [3], up to and including their current-quarter forecast (which you shade in green).  Reps who leave the company are shaded black.  Reps who get promoted out of quota-carrying roles (e.g., sales management) are shaded blue.  Future periods are shaded grey.  Add a 4+ quarter average productivity column for each row, and average each of the figures in the columns [4].

Here’s what you get:

full

Despite having only a relatively small amount of data [5], we can still interpret this a little.

  • The relative absence of black lines means we’re pretty good at sales hiring.   I’ve seen real charts with 5 black lines in a row, usually down to a single bad management hire.
  • The absence of black lines that “start late”  — for example {0, 25, 75, 25, 55, black} — is also good.  Our reps are either “failing fast” or succeeding, but things are not dragging on forever when they’re not working.
  • Over average 4Q+ productivity is $308K per quarter, almost exactly $1,200K per year so it does seem valid to use that figure in our modeling.
  • Entering $300K as target productivity then shows the empirical rep ramp as a percent of steady-state productivity, exactly how sales leaders think of it.  In this case, we see a {10%, 38%, 76%, 85%, 98%} empirical ramp across the first five quarters.  If our bookings model assumed {0%, 25%, 50%, 100%, 100%} you’d say our model is a little optimistic in the first two quarters, a little pessimistic in the 3rd, and a little optimistic in the fourth.  If we had more data, we might adjust it a bit based on that.

I love this chart because it presents unadulterated history and lets you examine the validity of two hugely important drivers in your sales bookings capacity model — drivers, by the way, that are often completely unquestioned [6].  For that reason, I encourage everyone to make this a standard slide in your Sales ops review (aka, QBR) template.  Note that since different types of rep ramp differently and hit different steady-state productivity levels, you should create one rep ramp per major type of rep in your company.  For example, corporate (or inside) sales reps will typically ramp more quickly to lower productivity levels than field reps who will ramp more slowly to higher productivity.  Channels reps will ramp differently from direct reps.  International reps may need their own chart as well.

You can download the spreadsheet I used here.

# # #

Notes

[1] Sales management may want to omit those no longer with the company, but that also omits their data, and might omit important patterns of hiring failure, so don’t omit anyone.  You can always exclude certain rows from the analysis without removing them from the chart (i.e., hiding them).

[2] New ARR bookings typically includes new ARR to both new and existing customers.

[3] You’ll need as many columns to do this as your longest tenured rep has been with the company, so it can get wide.  Let it.  There’s data in there.

[4] Ensuring empty cells are not confused with cells whose value is zero.  Excel ignores empty cells in calculating averages but will average your 0’s in when you probably don’t want them.

[5] In order to keep it easily and quickly grasped

[6] Particularly the ramp.

Kellblog on SaaS Metrics, A Comprehensive Introduction Podcast

I’m pleased to announce that I was recently featured in a six-part SaaS podcast mini-series on SaaShimi hosted by Aznaur Midov, VP at PNC Technology Finance Group, a debt provider who works primarily with private equity (PE) firms for SaaS buyouts, growth capital, and recapitalizations.

Let’s talk first about the mini-series.  It’s quite a line-up:

  • A Brief History of SaaS with Phil Wainewright, co-founder of Diginomica and recognized authority on cloud computing.
  • Key SaaS Metrics with me.
  • Building a Sales Org with Jacco van der Kooij, founder and CEO of Winning by Design
  • Building a Marketing Org with my old friend Tracy Eiler, CMO at InsideView and author of Aligned to Achieve, a book on aligning sales and marketing.
  • Building a Customer Success Org with Ed Daly, SVP of Customer Success and Growth at Okta.
  • Raising Capital with my friend Bruce Cleveland, partner at Wildcat Ventures and former operational executive at Oracle and Siebel.

The series is available on RedCircle, Apple podcasts, and Spotify.

Now, let’s talk about my episode.  The first thing you’ll notice is Aznaur did the interviews live, with a high-quality rig, and you can hear it in the audio which is much higher quality than the typical podcast.

In terms of the content, Aznaur did his homework, came prepared with a great set of questions in a logical order, and you can hear that in the podcast.  His goal was to do an interview that effectively functioned as a “SaaS Metrics 101” class and I think he succeeded.

Here is a rough outline of the metrics we touched on in the 38-minute episode:

  • ARR vs. ACV (annual recurring revenue vs. annual contract value)
  • ARR vs. MRR (ARR vs. monthly recurring revenue)
  • TCV (total contract value)
  • RPO (remaining performance obligation)
  • Bookings
  • Average contract duration (ACD)
  • Customer acquisition cost
  • Customer acquisition cost (CAC) ratio
  • CAC Payback Period
  • Renewal and churn rates
  • ARR- vs. ATR-based churn rates (ATR = available to renew)
  • Compound vs. standalone metrics
  • Net dollar expansion rate (NDER)
  • Survivor bias in churn rates
  • The problem with long customer lifetimes (due to low churn rates)
  • LTV/CAC (LTV = lifetime value)
  • Net promoter score (NPS)
  • The loose correlation between NPS and renewals
  • Intent to renew
  • Billings
  • Services gross margin
  • Cash burn rate
  • The investor vs. the operator view on metrics

Appearance on the AI and the Future of Work Podcast with Dan Turchin

ai and future of workI’m happy to announce that I was recently interviewed on the AI and the Future of Work podcast, hosted by Dan Turchin, Founder and CEO of PeopleReign, and formerly of Astound.ai, Big Panda, ServiceNow, and Aeroprise.  Dan’s a great technologist, entrepreneur, and visionary so I was happy to sit down with him for this wide-ranging, twenty-five minute chat.

On the podcast, we discuss:

  • A bit of my career history and background
  • How COVID-19 will change work in Silicon Valley
  • Innovation beyond Silicon Valley (one of my favorite topics, given my five years in Europe.)
  • The one most important SaaS metric.  (Hint:  LTV/CAC.)
  • The most misunderstood SaaS metric.  (I can’t remember what I said, but I should have said CAC Payback Period.)
  • A prediction about a workplace activity that is outrageous today but could be commonplace in the future.  (I said salary transparency after struggling a bit.  I suppose face masks and elbow bumps would have been an easier answer.)
  • Thoughts on the best software cultures.  (Keyword:  winning.)
  • My advice to my younger self.  (“Put your hands in the air and step away from the keyboard,” in reference to the various troubles I’ve caused myself over email when I should have either said nothing or called.)

The link to the podcast episode is here.  I hope you get a chance to listen to it and enjoy it if you do.  Thanks for having me Dan.

The First Three Slides of a SaaS Board Deck, with Company Key Metrics

I’m a SaaS metrics nut and I go to a lot of SaaS board meetings, so I’m constantly thinking about (among other things) how to produce a minimal set of metrics that holistically describe a SaaS company.  In a prior post, I made a nice one-slide metrics summary for an investor deck.  Here, I’m changing to board mode and suggesting what I view as a great set of three slides for starting a (post-quarter) board meeting, two of which are loaded with carefully-chosen metrics.

Slide 1:  The Good, The Bad, and the Ugly
The first slide (after you’ve reviewed the agenda) should be a high-level summary of the good and the bad  — with an equal number of each [1] — and should be used both to address issues in real-time and tee-up subsequent discussions of items slated to be covered later in the meeting.  I’d often have the e-staff owner of the relevant bullet provide a thirty- to sixty-second update rather than present everything myself.

slide 0The next slide should be a table of metrics.  While you may think this is an “eye chart,” I’ve never met a venture capitalist (or a CFO) who’s afraid of a table of numbers.  Most visualizations (e.g., Excel charts) have far less information density than a good table of numbers and while sometimes a picture is worth a thousand words, I recommend saving the pictures for the specific cases where they are needed [2].  By default, give me numbers.

Present in Trailing 9 Quarter Format
I always recommend presenting numbers with context, which is the thing that’s almost always missing or in short supply.  What do I mean by context? If you say we did $3,350K (see below) in new ARR in 1Q20, I don’t necessarily know if that’s good or bad.  Independent board members might sit on three to six boards, venture capitalists (VCs) might sit on a dozen.  Good with numbers or not, it’s hard to memorize 12 companies’ quarterly operating plans and historical results across one or two dozen metrics.

With a trailing nine quarter (T9Q) format, I get plenty of context.  I know we came up short of the new ARR plan because the plan % column shows we’re at 96%.  I can look back to 1Q19 and see $2,250K, so we’ve grown new ARR, nearly 50% YoY.  I can look across the row and see  a nice general progression, with only a slight down-dip from 4Q19 to 1Q20, pretty good in enterprise software. Or, I can look at the bottom of the block and see ending ARR and its growth — the two best numbers for valuing a SaaS company — are $32.6M and 42% respectively.  This format gives me two full years to compare so I can look at both sequential and year-over-year (YoY) trends, which is critical because enterprise software is a seasonal business.

What’s more, if you distribute (or keep handy during the meeting) the underlying spreadsheet, you’ll see that I did everyone the courtesy of hiding a fair bit of next-level detail with grouped rows — so we get a clean summary here, but are one-click away from answering obvious next-level questions, like how did new ARR split between new logos and upsell?

Slide 2:  Key Operating Metrics

Since annual recurring revenue (ARR) is everything in a SaaS company, this slide starts with the SaaS leaky bucket, starting ARR + new ARR – churn ARR = ending ARR.

After that, I show net new ARR, an interesting metric for a financial investor (e.g., your VCs), but somewhat less interesting as an operator.  Financially, I want to know how much the company spent on S&M to increase the “water level” in the leaky bucket by what amount [3].  As an operator, I don’t like net new ARR because it’s a compound metric that’s great for telling me there is a problem somewhere (e.g., it didn’t go up enough) but provides no value in telling me why [4].

After that, I show upsell ARR as a percent of new ARR, so we can see how much we’re selling to new vs. existing customers in a single row.  Then, I do the math for the reader on new ARR YoY growth [5].  Ultimately, we want to judge sales by how fast they are increasing the water they dump into the bucket — new ARR growth (and not net new ARR growth which mixes in how effective customer success is at preventing leakage).

The next block shows the CAC ratio, the amount the company pays in sales & marketing cost for $1 of new ARR.  Then we show the churn rate, in its toughest form — gross churn ARR divided not by the entire starting ARR pool, but only by that part which is available-to-renew (ATR) in the current period. No smoothing or anything that could hide fluctuations — after all, it’s the fluctuations we’re primarily interested in [6] [7].  We finish this customer-centric block with the number of customers and the net promoter score (NPS) of your primary buyer persona [8].

Moving to the next block we start by showing the ending period quota-carrying sales reps (QCRs) and code-writing developers (DEVs).  These are critical numbers because they are, in a sense, the two engines of the SaaS airplane and they’re often the two areas where you fall furthest behind in your hiring.  Finally, we keep track of total employees, an area where high-growth companies often fall way behind, and employee satisfaction either via NPS or an engagement score. [9]

Slide 3:  P&L and Cash Metrics

slide 3 newYour next (and final [10]) key metrics slide should include metrics from the P&L and about cash.

We start with revenue split by license vs. professional services and do the math for the reader on the mix — I think a typical enterprise SaaS company should run between 10% and 20% services revenue.  We then show gross margins on both lines of business, so we can see if our subscription margins are normal (70% to 80%) and to see if we’re losing money in services and to what extent [11].

We then show the three major opex lines as a percent of revenue, so we can see the trend and how it’s converging.  These are commonly benchmarked numbers so I’m showing them in % of revenue form in the summary, but in the underlying sheet you can ungroup to find actual dollars.

Moving to the final block, we show cashflow from operations (i.e., burn rate) as well as ending cash which, depending on your favorite metaphor is either the altimeter of the SaaS plane or the amount of oxygen left in the scuba tank.  We then show Rule of 40 Score a popular measure of balancing growth vs. profitability [12].  We conclude with CAC Payback Period, a popular compound measure among VCs, that I could have put on the operating metrics but put here because you need several P&L metrics to build it.

I encourage you to take these three slides as a starting point and make them your own, aligning with your strategy — but keeping the key ideas of what and how to present them to your board.

You can download the spreadsheet here.

# # #

Notes

[1] I do believe showing a balance is important to avoid getting labeled as having a half-empty or hall-full perspective.

[2] I am certainly not anti-visualization or anti-chart.  However, most people don’t make good ones so I’d take a table numbers over almost any chart I’ve ever seen in a board meeting.  Yes, there is a time and a place for powerful visualizations but, e.g., presenting single numbers as dials wastes space without adding value.

[3] Kind of a more demanding CAC ratio, calculated on net new ARR as opposed simply to new ARR.  For public companies you have to calculate that way because you don’t know new and churn ARR.  For private ones, I like staying pure and keeping CAC the measure of what it costs to add a $1 of ARR to the bucket, regardless of whether it stays in for a long time or quickly leaks out.

[4] Did sales have a bad quarter getting new logos, did account management fail at expansion ARR, or did customer success let too much churn leak out in the form of failed or shrinking renewals?  You can’t tell from this one number.

[5] There are a lot of judgement calls here in what math you for the reader vs. bloating the spreadsheet.  For things that split in two and add to 100% I often present only one (e.g., % upsell) because the other is trivial to calculate.  I chose to do the math on new ARR YoY growth because I think that’s the best single measure of sales effectiveness.  (Plan performance would be second, but is subject to negotiation and gaming.  Raw growth is a purer measure of performance in some sense.)

[6] Plus, if I want to smooth something, I can select sections in the underlying spreadsheet using the status bar to get averages and/or do my own calculations.  Smoothing something is way easier than un-smoothing it.

[7] Problems are hard to hide in this format anyway because churn ARR is clearly listed in the first block.

[8] Time your quarterly NPS survey so that fresh data arrives in time for your post-quarter ops reviews (aka, QBRs) and the typically-ensuing post-quarter board meeting.

[9] Taking a sort of balanced scorecard of financial, customer, and employee measures.

[10] Before handing off to the team for select departmental review, where your execs will present their own metrics.

[11] Some SaaS companies have heavily negative services gross margins, to the point where investors may want to move those expenses to another department, such as sales (ergo increasing the CAC) or subscription COGS (ergo depressing subscription margins), depending on what the services team is doing.

[12] With the underlying measures (revenue growth, free cashflow margin) available in the sheet as grouped data that’s collapsed in this view.

Will Your CEO Search Produce the Best Candidate or the Least Objectionable?

I was talking to a founder friend of mine the other day, and she made a comment about her startup’s search for its first non-founder (aka, “professional”) CEO.  She said the following about the nearly one-year recruiting process:

“Because every person in the search process had veto power, the process was inadvertently designed to go slowly and not produce the best candidate.  We passed on plenty of candidates superior to the person we eventually hired because someone had a problem with them and we assumed we’d find someone better in the future.  Eventually, the combination of search fatigue with dwindling cash compelled us to act so we locked in to the best person we had in-process at the time.  In effect, the process wasn’t designed to hire the most qualified candidate, but the least objectionable one.”

Character Ceo Talking From Tribune Set Vector

In this case the failed process was catastrophic.  The candidate they selected took the company in a different direction and my friend the founder was pushed out a few months later.

Here are some thoughts on how to create a CEO search process that produces the best, as opposed to the least objectionable, candidate:

  • Set up a search committee that does not include the whole board, so you are not creating a process with a large number of people who have veto power.
  • Write down what you want in a candidate in the form of a must-have, nice-to-have list.  Don’t delegate writing the core of the job spec for your new CEO to an associate at your search firm, cutting and pasting from the last spec.
  • Be mindful about the sequencing and timing of candidates.  Ask to see calibration candidates first to get people warmed up.  Try to cluster candidates.  Try to have sure candidates see the search committee in a different orders.  Slow down highly-qualified early candidates and speed up highly-qualified late entrants.  Like it or not, timing matters enormously.
  • Check some references before passing candidates beyond the committee.  Do some blind reference checking before moving candidates to the next step in the process.  There’s no point in having the group falling in love with a candidate only to discover they have poor reputation or dubious claims on their resume.
  • Let candidates ask for additional interviews beyond a relatively small core team  instead of defining a process where every candidate automatically sees every board member and executive staffer.  You can learn a ton about candidates by who they ask, and don’t ask, to see.
  • Ask candidates to present their plans for the company.  While all of them should include 90 days of learning and assessment (think:  “seek first to understand”) before taking action, virtually any qualified and engaged candidate has an 80% developed plan in their mind, so ask them to share it with you.