Category Archives: SaaS

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.

Marketing Targeting: It’s Not Just Where You Fish, It’s What You Put on the Hook

Back in the day I was taught that marketers do three things, memorized via the acronym STP:  segment, target, position.

  • Divide the audience into different segments.  For example, dividing consumers by demographics or dividing businesses by size or industry.
  • Select the segments that the company wishes to target for its marketing.  For example, choosing small and medium businesses (SMB) as your target segment.
  • Position the product in the mind of the consumer, ideally in a unique way, providing differentiation and/or benefit [1].  For example, positioning your offering for the SMB segment as easy to deploy and inexpensive to own.

I’ve always thought of targeting as the answer to the question, “what list do I want to buy?”  Do I want buy a list of marketing directors at SMBs or a list of chief data officers (CDOs) at Fortune 1000 companies?

The list-buying metaphor extends nicely to events (what shows do these people attend), PR (what publications do they read), AR (to which influencers do they listen), some forms of digital advertising (e.g., LinkedIn where you have considerable targeting control), if not Google (where you don’t [2]).

For many people, that’s where the targeting discussion ends.  When most people think of targeting they think of where on the lake they want to fish.

While an angler would never forget this, marketers too often miss that what you put on the hook matters, too.  Fishing in the same part of the lake, an angler might put on crayfish for largemouth bass, worms for rainbow trout, or stinkbait for catfish.

It’s not just about who you’re speaking to; it’s about what you tell them — the bait, if you will, that you put on the hook.

Perhaps this is too metaphorical, so let’s take an example — imagine we sell financial planning and budgeting software to businesses and our target segment is small businesses between $0M to $50M in revenue.  Via some marketing channels we can communicate only to people in this segment, but through a lot of other important channels (e.g., Google Ads, SEO, content marketing), we cannot.  So we need to rely not only on our targeting, but our message, to control who we bring into the lead funnel.

Consider these two messages:

  • Plan faster and more efficiently with OurTool
  • End the misery and mistakes of planning on Excel

The first message pitches a generic benefit of a planning system and is likely to attract many different types of fish.  The second message specifically addresses the pains of planning on Excel.  Who plans on Excel?  Well, smaller businesses primarily [3].  So the message itself helps us filter for the kind of companies we want to attract.

Now, let’s pretend we’re targeting large enterprises, instead.  Consider these two messages.

  • End the misery and mistakes of planning on Excel
  • Integrate your sales and financial planning

The first message, as discussed above, is going to catch a lot of small fish.  The second message is about a problem that only larger organizations face — small companies are just trying to get a budget done, whereas larger ones are trying to get a more holistic view.  The second message far better attracts the enterprise target that you want.  As would, for example, a message about the pain and expense of budgeting on Hyperion.

I’ll close in noting that marketers who measure themselves by the number of fish they catch [4] — as opposed to the conversion of those fish into customers — will often resist the more focused message because you won’t set attendance records with the more selective bait.  So, as you perform your targeting, always remember three things:

  1. It’s about where you put the boat
  2. It’s also about the bait you put on the hook
  3. It’s not about the number of fish you catch, but the number of the right fish that you catch.

# # #

Notes

[1] The decision to emphasize differentiation or benefit is covered in The Two Archetypal Marketing Messages:  “Bags Fly Free” and “Soup is Good Food.”

[2] In a B2B sense, at least.

[3] Amazingly, a lot of large and very large businesses also plan on Excel, but let’s not confuse the exception for the rule or the point of the example — different messages attract different buyers.

[4] Either literally by putting KPIs on high-funnel metrics such as MQLs or, more subtly and more dangerously, by getting too much inner joy from high-funnel metrics (“look how many people came to our webinar!”)

Should Your SDRs Look for Projects or Pain?

There’s a common debate out there, it goes something like this:

“Our sales development representatives (SDRs) need to look for pain: finding business owners with a problem and the ability to get budget to go fix it.”

Versus:

“No, our SDRs need to look for projects: finding budgeted projects where our software is needed, and ideally an evaluation in the midst of being set up.”

Who’s right?

As once was once taught to me, the answer to every marketing question is “it depends” and the genius is knowing “on what.”  This question is no exception.  The answer is:  it depends.  And on:

  • Whether you’re in a hot or cold market.
  • Whether your SDR is working an inbound or outbound motion

I first encountered this problem decades ago rolling out Solution Selling (from which sprung the more modern Customer-Centric Selling).  Solution Selling was both visionary and controversial.  Visionary in that it forced sales to get beyond selling product (i.e., selling features, feeds, and speeds) instead focusing on the benefits of what the product did for the customer.  Controversial in that it uprooted traditional sales thinking — finding an existing evaluation was bad, argued Bosworth, because it meant that someone else had already created the customer’s vision for a solution and thus the buying agenda would be biased in their favor.

While I think Bosworth made an interesting point about the potential for wired evaluation processes and requests for proposal (RFPs), I never took him literally.  Then I met what I could only describe as “fundamentalist solution seller” in working on the rollout.

“OK, we we’re working on lead scoring, and here’s what we’re going to do:  10 points for target industry, 10 points for VP title or above, 10 points for business pain, -10 points for existing evaluation, and -10 points for assigned budget.”

Wut?

I’d read the book so I knew what Bosworth said, but, but he was just making a point, right?  We weren’t actually going to bury existing evaluations in the lead pile, were we?  All because the customer knew they wanted to buy in our category and had the audacity to start an evaluation process and assign budget before talking to us?

That would be like living in the Upside Down.  We couldn’t possibly be serious?  Such is the depth of religion often associated with the rollout of a new sales methodology.

Then I remembered the subtitle of the book (which everyone seems to forget).

“Creating buyers in difficult selling markets.”  This was not a book written for sellers in Geoffrey Moore’s tornado, it was book for written for those in difficult markets, tough markets, markets without a lot of prospects, i.e., cold markets.  In a cold market, no one’s out shopping so you have no choice but find potential buyers in latent pain, inform them a solution exists, and try to sell it to them.

Example:  baldness remedies.  Sure, I’d rather not be bald, but I’m not out shopping for solutions because I don’t think they exist.  This is what solution sellers call latent pain.  Thus, if you’re going to sell me a baldness remedy, you’re going need to find me, get my attention, remind me that I don’t like being bald, then — and this is really hard part — convince me that you have a solution that isn’t snake oil.  Such is life in cold markets.  Go look for pain because if you look for buyers you aren’t going to find many.

However, in hot markets there are plenty of buyers, the market has already convinced buyers they need to buy a product, so the question sellers should focus on is not “why buy one” but instead, “why buy mine.”

I’m always amazed that people don’t first do this high-level situation assessment before deciding on sales and marketing messaging, process, and methodology.  I know it’s not always black & white, so the real question is:  to what extent are our buyers already shopping vs. need to be informed about potential benefits before considering buying?  But it’s hard to devise any strategy without having an answer to it.

So, back to SDRs.

Let’s quickly talk about motion.  While SDR teams may be structured in many ways (e.g., inbound, outbound, hybrid), regardless of team structure there are two fundamentally different SDR motions.

  • Inbound.  Following-up with people who have “raised their hand” and shown interest in the company and its offerings.  Inbound is largely a filtering and qualification exercise.
  • Outbound.  Targeting accounts (and people within them) to try and mutate them into someone interested in the company and its offerings.  In other words, stalking:  we’re your destiny (i.e., you need to be our customer) and you just haven’t figured it out, yet.

In hot markets, you can probably fully feed your salesforce with inbound.  That said, many would argue that, particularly as you scale, you need to be more strategic and start picking your customers by complementing inbound with a combination of named-account selling, account-based marketing, and outbound SDR motion.

In cold markets, the proverbial phone never rings.  You have no choice but to target buyers with power, target pains, and convince them your company can solve them.

Peak hype-cycle markets can be confusing because there’s plenty of inbound interest, but few inbound buyers (i.e., lots of tire-kickers) — so they’re actually cold markets disguised as hot ones.

Let’s finally answer the question:

  • SDRs in hot markets should look for projects.
  • SDRs in cold markets should look for pain.
  • SDRs in hot markets at companies complementing inbound with target-account selling should look for pain.

 

Unlearning As You Scale: Presentation from a VC Portfolio CEO Summit

The good people of Costanoa Ventures invited me to speak at their summit where they gather portfolio company CEOs to participate in an impressive set of sessions related to building and scaling startups.  I was honored to be in the company of friends and respected colleagues like Nick Mehta and Rob Reid as presenters at the conference.

Costanoa asked me to speak about un-learning at this year’s un-summit and, as a (sometimes, some might say frequent) contrarian, I was only too happy to do so.  The slides from the presentation are below.  I focused on 4 topics:

  • The sensible application of the popular Silicon Valley adage, “the folks who got you here aren’t the ones who will get you to the next level,” and how to reconcile it with an older, even more popular adage:  “dance with who brung ya.”
  • Generalizing the next-level adage beyond people to systems, processes, and operational strategies.
  • Things to do and pitfalls to avoid in recruiting next-level executives, with a particular focus on avoiding very successful people caught in the lather/rinse/repeat trap.
  • Critically thinking whether you have been successful because of, in spite of, or independent of a list of your company’s practices, values, and deeply held beliefs

This slides are here and embedded below.

Thanks to Greg Sands, Martina Lauchengco, and Rachel Quon for inviting me and giving me such a great topic to work with.

Video of My SaaStr 2020 Presentation: Churn is Dead, Long Live Net Dollar Retention

Thanks to everyone who attended my SaaStr 2020 presentation and thanks to those who provided me with great feedback and questions on the content of the session.  The slides from the presentation are available here.  The purpose of this post is to share the video of the session, courtesy of the folks at SaaStr.  Enjoy!

 

Appearance on the CFO Bookshelf Podcast with Mark Gandy

Just a quick post to highlight a recent interview I did on the CFO Bookshelf podcast with Mark Gandy.  The podcast episode, entitled Dave Kellogg Address The Rule of 40, EPM, SaaS Metrics and More, reflects the fun and somewhat wandering romp we had through a bunch of interesting topics.

Among other things, we talked about:

  • Why marketing is a great perch from which to become a CEO
  • Some reasons CEOs might not want to blog (and the dangers of so doing)
  • A discussion of the EPM market today
  • A discussion of BI and visualization, particularly as it relates to EPM
  • The Rule of 40 and small businesses
  • Some of my favorite SaaS operating metrics
  • My thoughts on NPS (net promoter score)
  • Why I like driver-based modeling (and what it has in common with prime factorization)
  • Why I still believe in the “CFO as business partner” trope

You can find the episode here on the web, here on Apple Podcasts, and here on Google Podcasts.

Mark was a great host, and thanks for having me.

SaaStr 2020 Session Preview: Churn is Dead, Long Live Net Dollar Retention!

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Reunited with old friend Tracy Eiler on the speaker page

The SaaStr Annual conference was delayed this year, but Jason & crew know that the show must go on.  So this year’s event has been rechristened SaaStr Annual @ Home and is being held in virtual, online format on September 2nd and 3rd.  The team at SaaStr have assembled a strong, diverse line-up of speakers to provide what should be another simply amazing program.

The purpose of this post is to provide a teaser to entice you to attend my session, Churn is Dead, Long Live Net Dollar Retention Rate, bright and early on Wednesday, September 2nd at 8:00 AM.

“I eat SaaS metrics for breakfast,” he thinks.  Or at least, “with.”

In this session, we’ll cover:

  • Separating a SaaS business into its two component parts
  • What makes SaaS companies so interesting for PE buyers
  • The SaaS leaky bucket of ARR
  • SaaS unit economics 101:  CAC, LTV, LTV/CAC, and CAC payback period
  • The three, fairly lethal problems with churn rates
  • Why “ARR is a fact and churn is an opinion”
  • Cohort analysis basics and survivor bias
  • Net dollar retention (NDR) rate definition and benchmarks
  • Explanatory power of NDR vs. ARR growth and the Rule of 40 in determining valuation multiples
  • The NDR implications of Goodhart’s Law
  • Applying Goodhart’s Law to NDR
  • The next frontier:  remaining performance obligation (RPO)

While the topic might seem a little dry, the content is critically important to any SaaS executive, and I can assure you the presentation will be fast-paced, fun, and anything but dry.

I hope you can attend and I look forward to seeing you there.