What Are The Units On Your Lead SaaS Metric — And What Does That Say About Your Culture

Quick:

  • How big is the Acme deal?  $250K.
  • What’s Joe’s forecast for the quarter?  $500K
  • What’s the number this year?  Duh.  $7,500K.

Awesome.  By the way:  $250K what?  $500K what?  $7,500K what?  ARR, ACV, bookings, TCV, new ARR, net new ARR, committed ARR, contracted ARR, terminal ARR, or something else?

Defining those terms isn’t the point of this post, so see note [1] below if interested.

The point is that these ambiguous, unitless conversations happen all the time in enterprise software companies.  This isn’t a post about confusion; the vast majority of the time, everyone understands exactly what is being said.  Thus what those implicit units really tell you about is culture.

Since there can be only one lead metric, every company, typically silently, decides what it is.  And what you pick says a lot about what you’re focused on.

  • New ARR means you’re focused on sales adding water to the SaaS leaky bucket — regardless of whether it’s from new or existing customers.
  • Net New ARR means you’re focused the change in water level in the SaaS leaky bucket — balancing new sales and churn — and presumably means you hold AEs accountable for both sales and renewals within their patch.
  • New Logo ARR means you’re focused on new ARR from new customers.  That is, you’re focused on “lands” [2].
  • Bookings means you’re focused on cash [3], bringing in dollars regardless of whether they’re from subscription or services, or potentially something else [4].
  • TCV, which became a four-letter word after management teams too often conflated it with ARR, is probably still best avoided in polite company.  Use RPO for a similar, if not identical, concept.
  • Committed ARR usually means somebody important is a fan of Bessemer metrics, and means the company is (as with Net New ARR) focused on new ARR net of actual and projected churn.
  • Terminal ARR means you’re focused on the final-year ARR of multi-year contracts, implying you sign contracts with built-in expansion, not a bad idea in an NDR-focused world, I might add.
  • Contracted ARR can be a synonym for either committed or terminal ARR, so I’d refer to the appropriate bullet above as the case may be.

While your choice of lead metric certainly affects the calculations of other metrics (a bookings CAC or a terminal-ARR CAC) that’s not today’s point, either.  Today’s point is simple.  What you pick says a lot about you and what you want your organization focused on.

  • What number do you celebrate at the all hands meeting?
  • What number do you tell employees is “the number” for the year?

For example, in my opinion:

  • A strong sales culture should focus on New ARR.  Yes, the CFO and CEO care about Ending ARR and thus Net New ARR, but the job of sales is to fill the bucket.  Someone else typically worries about what leaks out.
  • A shareholder value culture would focus on Ending ARR, and ergo Net New ARR.  After all, the company’s value is typically a linear function of its Ending ARR (with slope determined by growth).
  • A strong land-and-expand culture might focus on Terminal ARR, thinking, regardless of precisely when they come in, we have contracts that converge to a given total ARR value over time [5].
  • Conversely, a strong land and expand-through-usage culture might focus on New Logo ARR (i.e., “land”) ARR, especially if the downstream, usage-based expansion is seen as somewhat automatic [6].
  • A cash-focused culture (and I hope you’re bootstrapped) would focus on bookings.  Think:  we eat what we kill.

This isn’t about a right or wrong answer [7].  It’s about a choice for your organization, and one that likely changes as you scale.  It’s about mindfulness in making a subtle choice that actually makes a big statement about what you value.

# # #

Notes
[1] For clarity’s sake, ARR is annual recurring revenue, the annual subscription value.  ACV is annual contract value which, while some treat as identical to ARR, others treat as first-year total contract value, i.e., first-year ARR plus year-one services.  Bookings is usually used as a proxy for cash and ergo would include any effects of multi-year prepayments, e.g., a two-year, prepaid, $100K/year ARR contract would be $200K in bookings.  TCV is total contract value which is typically the total (subscription) value of the contract, e.g., a 3-year deal with an ARR stream of $100K, $200K, $300K would have a $600K, regardless of when the cash payments occurred.  New ARR is new ARR from either new customers (often called New Logo ARR) or existing customers (often called Upsell ARR).  Net New ARR is new ARR minus churn ARR, e.g., if a regional manager starts with $10,000K in their region, adds $2,000K in new ARR and churns $500K, then net new ARR is $1,500K.  Committed ARR (as defined by Bessemer who defined the term) is “contracted, but not yet live ARR, plus live ARR netted against known projected ARR churn” (e.g., if a regional manager starts with $10,000K in their region, has signed contracts that start within an acceptable time period of $2,000K, takes $200K of expected churn in the period, and knows of $500K of new projected churn upcoming, then their ending committed is ARR is $11,500K.  (Why not $11,300K?  Because the $200K of expected churn was presumably already in the starting figure.)  Terminal ARR the ARR in the last year of the contract, e.g., say a contract has an ARR stream of $100K, $200K, $300K, the terminal ARR is $300K [1A].  Contracted ARR is for companies that have hybrid models (e.g., annual subscription plus usage fee) and includes only the contractually committed recurring revenues and not usage fees.

[1A] Note that it’s not yet clear to me how far Bessemer goes out with “contracted” ARR in their committed ARR definition, but I’m currently guessing they don’t mean three years.  Watch this space as I get clarification from them on this issue.

[2] In the sense of land-and-expand.

[3] On the assumptions that bookings is being used as a proxy for cash, which I recommend, but which is not always the case.

[4] e.g., non-recurring engineering; a bad thing to be focused on.

[5] Although if they all do so in different timeframes it becomes less meaningful.  Also unless the company has a track record of actually achieving the contractually committed growth figures, it becomes less credible.

[6] Which it never actually is in my experience, but it is a matter of degree.

[7] Though your investors will definitely like some of these choices better than others.

 

Why I’m Advising Bluecore

I first read The One to One Future by Don Peppers and Martha Rogers in 1997, four years after it was published.  As a marketer, the book made a big impression on me.  It was revolutionary stuff:  we should make the paradigm shift from mass marketing to individualized marketing.

When the book was published in 1993, newspaper ads were $75B/year, TV around $60B, the web browser was a mere three years old, and there were 623 total sites on the web.  There was effectively no web advertising market.  It was nine years before the Minority Report popularized a future vision of one-to-one advertising.  It was six years before Paco Underhill published Why We Buy revealing insights gleaned by manually tracking shoppers to understand in-store behavior [1].

Look at the subtitle: “Building Relationships One Customer at a Time.” You could use that in a webinar today.  The One to One Future was not just ahead of its time; it was so far ahead of its time that it could have equally been categorized under either “marketing” or “science fiction.”

Why?

  • It turns out, as with science fiction, that it’s easier to envision something than to build it. Remember, “they promised us flying cars and we got 140 characters.” [2]
  • Building individualized marketing systems required layers and layers of underpinnings that were simply not in place. You can’t do good personalization without a clean, real-time, 360-degree view of your customer.  Clean means a big effort into data quality and data profiling and typically either master data management or a customer data platform [3].  Real-time means real-time data integration [4].  360-degrees means pulling relevant data from virtually all of your systems.  Self-driving cars don’t work on cow paths.  Building those layers of requisite infrastructure has taken decades.
  • Marketing’s focus on the perfect offer was flawed. Say I found an offer with an 90% chance that you’d respond affirmatively.  Perfect, right?  But it was for a product that was out of stock.  The perfect offer has to be for the right product, in the customer’s preferred size or color, and available to sell.  We can’t just find the set called {great offers}.  We needed to intersect it with the set called {in stock and need to sell}.  This made a hard problem harder by pulling inventory and the supply chain into the equation.
  • Marketers got trapped in a vicious downward cycle of communications. Email click rates have nearly been cut in half over the past decade.  Marketing’s solution?  Send more emails to make up the difference.  Email vendors, who typically price by the email, were only too happy to accommodate.  That, however, is a short-term mentality.  More bad email with lower open and click rates isn’t the solution.  The same holds for ads and promotions.  Marketing needs to get out of this race to the bottom.  We need to focus on quality, not quantity.  And pay vendors for performance delivered, not communications sent, while we’re at it.
  • Finally, the retail industry needed to shift mentality from store-first to digital-first. Roots, as they say, run deep and retailers have long, deep roots in physical stores.  Bricks-and-mortar supposedly changed to clicks-and-mortar, but really, it was mortar-and-clicks the whole time.  The industry never really changed to digital-first from store-first.  Until Covid-19, that is.  While this meme, popularized in Forbes, was intended for many industries, it could have been custom made for retail [5].

So where does Bluecore fit in?

  • Bluecore is a multi-channel personalization platform. They’re building what marketers in the past dreamed of, but couldn’t build, because the infrastructure wasn’t there.  Now it can be built, and they’re building it.
  • Bluecore is an AI/ML company focused on retail analytics and personalization. I’ve blogged before that AI/ML is best applied to specific problems and not general ones, and this is a great example.  They are a closed-loop, retailed-focused application that gets smarter every day and with each new customer.  If you believed in the increasing returns of marketing leadership in technology markets before AI/ML [6], you should believe in them twice as much after.
  • Bluecore’s personalization understands both customer and product – and intersects them. Across a catalog of more than 250M products and SKUs, Bluecore can match customers and products at a 1-1 level.  It automates what would have been the work of a team of in-house data scientists.
  • Bluecore is paid for performance, not volume. They back up their performance claims with a pricing model based not on volume but on success.  This is a great example of superior technology enabling disruptive business model innovation.

Why am I advising Bluecore?  Three reasons:

  • As a true, blue marketer this stuff genuinely interests me. I love working with marketing companies on marketing problems.
  • It’s always about the team. I’ve loved working with Fayez Mohamood (founder/CEO) and Sherene Hilal (SVP of Marketing).  As a bonus, former Salesforce teammate Scott Beechuk is an investor and on the board.  I like working with people who like working with me and appreciate my inimitable (I inadvertently almost typed inimical) style when it comes to feedback.
  • The momentum and market opportunity. Bluecore’s a highly successful company, having raised over $100M in VC with top-tier investors, and they are pursuing transformational change in a $4T market.  The last 100 years in retail were all about stores, the next 100 will be about retailers meeting customers wherever they are.  And that’s what Bluecore does.

# # #

Notes
[1] And why, to this day, you can find still baskets strewn throughout many retail shops as opposed to only at the entrance.  His work was kind of a manual predecessor to systems like RetailNext, whose founder I got to know through mutual investments in a prior life from StarVest.

[2]  Peter Thiel at Yale.

[3] Which weren’t to be invented for about 20 years

[4] The data warehouse was invented in 1992, with the publication of Bill Inmon’s Building the Data Warehouse.   Ralph Kimball would invent the star schema 4 years after that.

[5] Apologies to frequent readers for using this meme again – but I just love it!

[6] Tech buyers, and particularly IT buyers, tend to face high opportunity costs and high switching costs and are ergo generally risk averse.  This drives increasing returns for early market leaders.  Think:  no one ever got fired for buying IBM.

Thoughts on Hiring Your First VP of Sales

There’s some great content out there on the subject of hiring your first VP of sales at a startup, so in this post I’m going to do some quick thoughts on the subject in an effort to complement the existing corpus.

In other words, this is not your classic TLDR Kelloggian essay, but some quick tips.

  • Hire them first.  That is, before hiring any salesreps.  The first VP of Sales should be your first salesrep.  Hire someone who wants to walk (and even discover) the path before leading others.  Hire someone who enjoys the fight.
  • Hire them hopelessly early.  Don’t wait for product availability.  Don’t wait until you’ve hired 3-4 reps and they need a manager.  Don’t wait until you have a bookings plan that needs hitting. Hire them as early as possible.
  • Glue yourselves together for 6-12 months.  You want to spend 6-12 months as Frick and Frack.  Why?  Most founders can sell their idea and their software.  The real question is:  can anyone else?  By gluing yourselves together you will transfer a huge amount of critical knowledge to the sales VP.  That, or you’ll drive each other crazy and discover you can’t work together.  Either way, it’s good to succeed or fail fast.  And the goal is total alignment.  [1]
  • Hire them before the VP of marketing.  I know some very smart people who disagree with me on this question, but as a three-time enterprise software CMO (and two-time CEO) I take no shame in saying that marketing is a support function.  We’re here to help.  Hire us after hiring sales.  Let the VP of Sales have a big vote in choosing who supports them [2].
  • Hire someone who is a first-line manager today.  Their title might be district manager or regional vice president, but you want someone close to the action, but who also is experienced in building and managing a team.  Why?  Because you want them to be successful as your first salesrep for 6-12 months and then build up a team that they can manage.  In a perfect world, they’d have prior experience managing up to 10 reps, but even 4-6 will do [3].  You want to avoid like the plague a big-company, second- or third-line manager who, while undoubtedly carrying a large number, likely spends more time in spreadsheets and internal reviews than in customer meetings.

# # #

Notes
[1] Hat tip to Bhavin Shah for this idea.

[2] A wise VP of Marketing often won’t join before of the VP of Sales anyway.

[3] On the theory that someone’s forward potential is not limited to their prior experience.  Someone who’s successfully managed 4-6 reps can likely manage 10-12 with one extra first-line manager.  Managing 36 through a full layer of first-line managers is a different story.  That’s not to say they can’t do it, but it is a different job.  In any case, the thing to absolutely avoid is the RVP who can only manage through a layer of managers and views the sales trenches as a distant and potentially unpleasant memory.

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.
  • 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.
  • 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.
  • 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.)

Kellblog 2021 Predictions

I admit that I’ve been more than a little slow to put out this post, but at least I’ve missed the late December (and early January) predictions rush.  2020 was the kind of year that would make anyone in the predictions business more than a little gun shy.  I certainly didn’t have “global pandemic” on my 2020 bingo card and, even if I somehow did, I would never have coupled that with “booming stock market” and median SaaS price/revenue multiples in the 15x range.

That said, I’m back on the proverbial horse, so let’s dig in with a review of our 2020 predictions.  Remember my disclaimers, terms of use, and that this exercise is done in the spirit of fun and as a way to tee-up discussion of interesting trends, and nothing more.

2020 Predictions Review

Here a review of my 2020 predictions along with a self-graded and for this year, pretty charitable, hit/miss score.

  1. Ongoing social unrest. No explanation necessary.  HIT.
  2. A desire for re-unification. We’ll score that one a whopping, if optimistic, MISS.  Hopefully it becomes real in 2021.
  3. Climate change becomes new moonshot. Swing and a MISS.  I still believe that we will collectively rally behind slowing climate change but feel like I was early on this prediction, particularly because we got distracted with, shall we say, more urgent priorities.  (Chamath, a little help here please.)
  4. The strategic chief data officer (CDO). CDO’s are indeed becoming more strategic and they are increasingly worried about playing not only defense but also offense with data, so much so that the title is increasingly morphing into chief data & analytics officer (CDAO).  HIT.
  5. The ongoing rise of devops. In an era where we (vendors) increasingly run our own software, running it is increasingly as important as building it.  Sometimes, moreHIT.
  6. Database proliferation slows. While the text of this prediction talks about consolidation in the DBMS market, happily the prediction itself speaks of proliferation slowing and that inconsistency gives me enough wiggle room to declare HITDB-Engines ranking shows approximately the same number of DBMSs today (335) as one year ago (334).  While proliferation seems to be slowing, the list is most definitely not shrinking.
  7. A new, data-layer approach to data loss prevention. This prediction was inspired by meeting Cyral founder Manav Mital (I think first in 2018) after having a shared experience at Aster Data.  I loved Manav’s vision for securing the set of cloud-based data services that we can collectively call the “data cloud.”  In 2020, Cyral raised an $11M series A, led by Redpoint and I announced that I was advising them in March.  It’s going well.  HIT.
  8. AI/ML success in focused applications. The keyword here was focus.  There’s sometimes a tendency in tech to confuse technologies with categories.  To me, AI/ML is very much the former; powerful stuff to build into now-smart applications that were formerly only automation.  While data scientists may want an AI/ML workbench, there is no one enterprise AI/ML application – more a series of applications focused on specific problems, whether that be C3.AI in a public market context or Symphony.AI in private equity one.  HIT.
  9. Series A remains hard. Well, “hard” is an interesting term.  The point of the prediction was the Series A is the new chokepoint – i.e., founders can be misled by easily raising $1-2M in seed, or nowadays even pre-seed money, and then be in for a shock when it comes time to raise an A.  My general almost-oxymoronic sense is that money is available in ever-growing, bigger-than-ever bundles, but such bundles are harder to come by.  There’s some “it factor” whereby if you have “it” then you can (and should) raise tons of money at great valuations, whereas, despite the flood of money out there, if you don’t have “it,” then tapping into that flood can be hard to impossible.  Numbers wise, the average Series A was up 16% in size over 2019 at around $15M, but early-stage venture investment was down 11% over 2019.  Since I’m being charitable today, HIT.
  10. Autonomy CEO extradited. I mentioned this because proposed extraditions of tech billionaires are, well, rare and because I’ve kept an eye on Autonomy and Mike Lynch, ever since I competed with them back in the day at MarkLogic.  Turns out Lynch did not get extradited in 2020, so MISS, but the good news (from a predictions viewpoint) is that his extradition hearing is currently slated for next month so it’s at least possible that it happens in 2021.  Here’s Lynch’s website (now seemingly somewhat out of date) to hear his side of this story.

So, with that charitable scoring, I’m 7 and 3 on the year.  We do this for fun anyway, not the score.

 Kellblog’s Ten Prediction for 2021

1. US divisiveness decreases but unity remains elusive. Leadership matters. With a President now focused on unifying America, divisiveness will decrease.  Unity will be difficult as some will argue that “moving on” will best promote healing while others argue that healing is not possible without first holding those to account accountable.  If nothing else, the past four years have provided a clear demonstration of the power of propaganda, the perils of journalistic bothsidesism, and the power of “big tech” platforms that, if unchecked, can effectively be used for long-tail aggregation towards propagandist and conspiratorial ends.

The big tech argument leads to one of two paths: (1) they are private companies that can do what they want with their terms of service and face market consequences for such, or (2) they are monopolies (and/or, more tenuously, the Internet is a public resource) that must be regulated along the lines of the FCC Fairness Doctrine of 1949, but with a modern twist that speaks not only to the content itself but to the algorithms for amplifying and propagating it.

2. COVID-19 goes to brushfire mode. After raging like a uncontained wildfire in 2020, COVID should move to brushfire mode in 2021, slowing down in the spring and perhaps reaching pre-COVID “normal” in the fall, according to these predictions in UCSF Magazine. New variants are a wildcard and scientists are still trying to determine the extent to which existing vaccines slow or stop the B117 and 501.V2 variants.

According to this McKinsey report, the “transition towards normalcy is likely during the second quarter in the US,” though, depending on a number of factors, it’s possible that, “there may be a smaller fall wave of disease in third to fourth quarter 2021.”  In my estimation, the wildfire gets contained in 2Q21, with brush fires popping up with decreasing frequency throughout the year.

(Bear in mind, I went to the same school of armchair epidemiology as Dougall Merton, famous for his quote about spelling epidemiologist:  “there are three i’s in there and I swear they’re moving all the time.”)

3. The new normal isn’t. Do you think we’ll ever go into the office sick again? Heck, do you think we’ll ever go into the office again, period?  Will there even be an office?  (Did they renew that lease?)  Will shaking hands be an ongoing ritual? Or, in France, la bise?  How about those redeyes to close that big deal?  Will there still be 12-legged sales calls?  Live conferences?  Company kickoffs?  Live three-day quarterly business reviews (QBRs)?  Business dinners?  And, by the way, do you think everyone – finally – understands the importance of digital transformation?

I won’t do detailed predictions on each of these questions, and I have as much Zoom fatigue as the next person, but I think it’s important to realize the question is not “when we are we going back to the pre-COVID way of doing things?” and instead “what is the new way of doing things that we should move towards?”   COVID has challenged our assumptions and taught us a lot about how we do business. Those lessons will not be forgotten simply because they can be.

4.We start to value resilience, not just efficiency. For the past several decades we have worshipped efficiency in operations: just-in-time manufacturing, inventory reduction, real-time value chains, and heavy automation.  That efficiency often came at a cost in terms of resilience and flexibility and as this Bain report discusses, nowhere was that felt more than in supply chain.  From hand sanitizer to furniture to freezers to barbells – let alone toilet paper and N95 masks — we saw a huge number of businesses that couldn’t deal with demand spikes, forcing stock-outs for consumers, gray markets on eBay, and countless opportunities lost.  It’s as if we forget the lessons of the beer game developed by MIT.  The lesson:  efficiency can have a cost in terms of resilience and agility and I believe,  in an increasingly uncertain world, that businesses will seek both.

5. Work from home (WFH) sticks. Of the many changes COVID drove in the workplace, distributed organizations and WFH are the biggest. I was used to remote work for individual creative positions such as writer or software developer.  And tools from Slack to Zoom were already helping us with collaboration.  But some things were previously unimaginable to me, e.g., hiring someone who you’d never met in the flesh, running a purely digital user conference, or doing a QBR which I’d been trained (by the school of hard knocks) was a big, long, three-day meeting with a grueling agenda, with drinks and dinners thereafter.  I’d note that we were collectively smart enough to avoid paving cow paths, instead reinventing such meetings with the same goals, but radically different agendas that reflected the new constraints.  And we – or at least I in this case – learned that such reinvention was not only possible but, in many ways, produced a better, tighter meeting.

Such reinvention will be good for business in what’s now called The Future of Work software category such as my friends at boutique Future-of-Work-focused VCs like Acadian Ventures — who have even created a Bessemer-like Future of Work Global Index to track the performance of public companies in this space.

6. Tech flight happens, but with a positive effect. Much has been written about the flight from Silicon Valley because of the cost of living, California’s business-unfriendly policies, the mismanagement of San Francisco, and COVID. Many people now realize that if they can work from home, then why not do so from Park City, Atlanta, Raleigh, Madison, or Bend?  Better yet, why not work from home in a place with no state income taxes at all — like Las Vegas, Austin, or Miami?

Remember, at the end of the OB (original bubble), B2C meant “back to Cleveland” – though, at the time, the implication was that your job didn’t go with you.  This time it does.

The good news for those who leave:

  • Home affordability, for those who want the classic American dream (which now has a median price of $2.5M in Palo Alto).
  • Lower cost of living. I’ve had dinners in Myrtle Beach that cost less than breakfasts at the Rosewood.
  • Burgeoning tech scenes, so you don’t have go cold turkey from full immersion in the Bay Area. You can “step down,” into a burgeoning scene in a place like Miami, where Founder’s Fund partner Keith Rabois, joined by mayor Francis Suarez, is leading a crusade to turn Miami into the next hot tech hub.

But there also good news for those who stay:  house prices should flatten, commutes should improve, things will get a little bit less crazy — and you’ll get to keep the diversity of great employment options that leavers may find lacking.

Having grown up in the New York City suburbs, been educated on Michael Porter, and worked both inside and outside of the industry hub in Silicon Valley, I feel like the answer here is kind of obvious:  yes, there will be flight from the high cost hub, but the brain of system will remain in the hub.  So it went with New York and financial services, it will go with Silicon Valley and tech.  Yes, it will disperse.  Yes, certainly, lower cost and/or more staffy functions will be moved out (to the benefit of both employers and employees).  Yes, secondary hubs will emerge, particularly around great universities.  But most of the VCs, the capital, the entrepreneurs, the executive staff, will still orbit around Silicon Valley for a long time.

7. Tech bubble relents. As an investor, I try to never bet against bubbles via shorts or puts because “being right long term” is too often a synonym for “being dead short term.” Seeing manias isn’t hard, but timing them is nearly impossible.  Sometimes change is structural – e.g., you can easily convince me that if perpetual-license-based software companies were worth 3-5x revenues that SaaS companies, due to their recurring nature, should be worth twice that.  The nature of the business changed, so why shouldn’t the multiple change with it?

Sometimes, it’s actually true that This Time is Different.   However, a lot of the time it’s not.  In this market, I smell tulips.  But I started smelling them over six months ago, and BVP Emerging Cloud Index is up over 30% in the meantime.  See my prior point about the difficultly of timing.

But I also believe in reversion to the mean.  See this chart by Jamin Ball, author of Clouded Judgement, that shows the median SaaS enterprise value (EV) to revenue ratio for the past six years.  The median has more than tripled, from around 5x to around 18x.  (And when I grew up 18x looked more like a price/earnings ratio than a price/revenue ratio.)

What accounts for this multiple expansion?  In my opinion, these are several of the factors:

  • Some is structural: recurring businesses are worth more than non-recurring businesses so that should expand software multiples, as discussed above.
  • Some is the quality of companies: in the past few years some truly exceptional businesses have gone public (e.g., Zoom).  If you argue that those high-quality businesses deserve higher multiples, having more of them in the basket will pull up the median.  (And the IPO bar is as high as it’s ever been.)
  • Some is future expectations, and the argument that the market for these companies is far bigger than we used to think. SaaS and product-led growth (PLG) are not only better operating models, but they actually increase TAM in the category.
  • Some is a hot market: multiples expand in frothy markets and/or bubbles.

My issue:  if you assume structure, quality, and expectations should rationally cause SaaS multiples to double (to 10), we are still trading at 80% above that level.  Ergo, there is 44% downside to an adjusted median-reversion of 10.  Who knows what’s going to happen and with what timing but, to quote Newton, what goes up (usually) must come down.  I’m not being bear-ish; just mean reversion-ish.

(Remember, this is spitballing.  I am not a financial advisor and don’t give financial advice.  See disclaimers and terms of use.)

8. Net dollar retention (NDR) becomes the top SaaS metric, driving companies towards consumption-based pricing and expansion-oriented contracts. While “it’s the annuity, stupid” has always been the core valuation driver for SaaS businesses, in recent years we’ve realized that there’s only one thing better than a stream of equal payments – a stream of increasing payments.  Hence NDR has been replacing churn and CAC as the headline SaaS metric on the logic of, “who cares how much it cost (CAC) and who cares how much leaks out (churn) if the overall bucket level is increasing 20% anyway?”  While that’s not bad shorthand for an investor, good operators should still watch CAC and gross churn carefully to understand the dynamics of the underlying business.

This is driving two changes in SaaS business, the first more obvious than the second:

  • Consumption-based pricing. As was passed down to me by the software elders, “always hook pricing to something that goes up.”  In the days of Moore’s Law, that was MIPS.  In the early days of SaaS, that was users (e.g., at Salesforce, number of salespeople).  Today, that’s consumption pricing a la Twilio or Snowflake.   The only catch in a pure consumption-based model is that consumption better go up, but smart salespeople can build in floors to protect against usage downturns.
  • Built-in expansion. SaaS companies who have historically executed with annual, fixed-fee contracts are increasingly building expansion into the initial contract.  After all, if NDR is becoming a headline metric and what gets measured gets managed, then it shouldn’t be surprising that companies are increasingly signing multi-year contracts of size 100 in year 1, 120 in year 2, and 140 in year 3.  (They need to be careful that usage rights are expanding accordingly, otherwise the auditors will flatten it back out to 120/year.)  Measuring this is a new challenge.  While it should get captured in remaining performance obligation (RPO), so do a lot of other things, so I’d personally break it out.  One company I work with calls it “pre-sold expansion,” which is tracked in aggregate and broken out as a line item in the annual budget.

See my SaaStr 2020 talk, Churn is Dead, Long Live Net Dollar Retention, for more information on NDR and a primer on other SaaS metrics.  Video here.

9. Data intelligence happens. I spent a lot of time with Alation in 2020, interim gigging as CMO for a few quarters. During that time, I not only had a lot of fun and worked with great customers and teammates, I also learned a lot about the evolving market space.

I’d been historically wary of all things metadata; my joke back in the day was that “meta-data presented the opportunity to make meta-money.”  In the old days just getting the data was the problem — you didn’t have 10 sources to choose from, who cared where it came from or what happened to it along the way, and what rules (and there weren’t many back then) applied to it.  Those days are no more.

I also confess I’ve always found the space confusing.  Think:

Wait, does “MDM” stand for master data management or metadata management, and how does that relate to data lineage and data integration?  Is master data management domain-specific or infrastructure, is it real-time or post hoc?  What is data privacy again?  Data quality?  Data profiling?  Data stewardship?  Data preparation, and didn’t ETL already do that?  And when did ETL become ELT?  What’s data ops?  And if that’s not all confusing enough, why do I hear like 5 different definitions of data governance and how does that relate to compliance and privacy?”

To quote Edward R. Murrow, “anyone who isn’t confused really doesn’t understand the situation.”

After angel investing in data catalog pioneer Alation in 2013, joining their board in 2016, and joining the board of master data management leader Profisee in 2019, I was determined to finally understand the space.  In so doing, I’ve come to the conclusion that the vision of what IDC calls data intelligence is going happen.

Conceptually, you can think of DI as the necessary underpinning for both business intelligence (BI) and artificial intelligence (AI).  In fact, AI increases the need for DI.  Why?  Because BI is human-operated.  An analyst using a reporting or visualization tool who sees bad or anomalous data is likely going to notice.  An algorithm won’t.  As we used to say with BI, “garbage in, garbage out.”  That’s true with AI as well, even more so.  Worse yet, AI also suffers from “bias in, bias out” but that’s a different conversation.

I think data intelligence will increasingly coalesce around platforms to bring some needed order to the space.  I think data catalogs, while originally designed for search and discovery, serve as excellent user-first platforms for bringing together a wide variety of data intelligence use cases including data search and discovery, data literacy, and data governance.  I look forward to watching Alation pursue, with a hat tip to Marshall McLuhan, their strategy of “the catalog is the platform.”

Independent of that transformation, I look forward to seeing Profisee continue to drive their multi-domain master data management strategy that ultimately results in cleaner upstream data in the first place for both operational and analytical systems.

It should be a great year for data.

10. Rebirth of Planning and Enterprise Performance Management (EPM). 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 that I know (and for transparency my relationship with them, if any.)

Planning is literally being reborn before our eyes, in most cases using modern infrastructure, product-led growth strategies, stronger end-user focus and design-orientation, and often with a functional, vertical, or departmental twist.  2021 will be a great year for this space as these companies grow and put down roots.  (Also, see the follow-up post I did on this prediction.)

Well, that’s it for this year’s list.  Thanks for reading this far and have a healthy, safe, and Rule-of-40-compliant 2021.