Category Archives: Management

Structuring the Organization and Duties of Product Marketing and Competitive Analysis

I sometimes get asked about how to structure an enterprise software marketing organization and the relative roles of product marketing vs. competitive analysis.  In this post, I’ll share my (somewhat contrarian) thoughts on this topic.

My first job in marketing, which served as my bridge from a technical to a sales-and-marketing career, was as a competitive analyst.  Specifically, I was the dedicated Sybase competitive analyst at Ingres in the late 1980s, in a corporate job, but working out of the New York City sales office.  Because, at the time, Sybase was a strong new entrant with a beachhead strategy in financial services, this was rough equivalent of working for the Wehrmacht on Omaha Beach on D-Day.  I learned not only by watching Sybase’s market invasion, but more importantly by watching how the local reps [1] and corporate [2] responded to it.

I’m a huge believer in competitive analysis, which probably started when I first heard this quote watching Patton as an adolescent:

“Rommel, you magnificent bastard, I read your book!” [3]

My other formative experience came from watching yet another movie, Wall Street, where antagonist Gordon Gekko refers to Sun Tzu’s The Art of War.

While Gekko doesn’t use my favorite quote for these purposes [4], his reference to the book was very much in vogue at the time, and probably why I first read it.  My favorite quote from The Art of War is this one:

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

Regular readers know I believe the mission of marketing is to make sales easier.  So the question becomes:  in enterprise software, how do we structure product marketing and competitive in the best way to do just that?

First, let’s review some common mistakes:

  • Not specializing competitive, instead declaring that each product marketing manager (PMM) will cover their respective competitors.  Too much scope, too little focus.
  • Understaffing competitive.  Even in organizations where competitive exists as its own team, it’s not uncommon to see a ratio of 5-10 PMMs per competitive analyst in terms of staffing.  This is too unbalanced.
  • Chartering competitive as strategic.  While I often euphemize the competitive team as “strategic marketing” or “market intelligence,” that’s not supposed to actually change their mission into some think tank.  They exist to help sales win deals.  Don’t let your competitive team get so lofty that they view deal support as pedestrian.
  • Putting competitive under product marketing.  This both blurs the focus and, more importantly, eliminates a healthy tension [5].  If your messaging doesn’t work in the field, the CMO should want to hear about it early (e.g., in their own staff meeting) and have a chance to fix it before it escalates to the corporate QBR and a potential sales attack on marketing in front of the CEO.
  • Putting competitive in the field.  This happens when marketing abdicates responsibility for producing sales-ready competitive materials and someone else picks up the ball, usually the sales productivity team, but sometimes field marketing [6].   This disconnects corporate product marketing from the realities of the field, which is not healthy.

Now, let’s tell you how I think structuring these departments.

  • Product marketing exists to build messaging and content [7] that describe the features and benefits of the product [8].  The job is to articulate.  They are experts in products.
  • Competitive analysis exists to research competitors, devise plays, and build tools to help sales win deals.  The job is to win.  They are experts in the competitors.

As long as we’re in movie quote mode, here’s one of my favorite quotes from James Mason’s character in The Verdict [9]:

I’d prepared a case and old man White said to me, “How did you do?” And, uh, I said, “Did my best.” And he said, “You’re not paid to do your best. You’re paid to win.”

While he was speaking to about lawyers, he might as well have been speaking to competitive:  you’re paid to win.

That’s why I believe competitive needs to be holistic and play-oriented.  Simply put, take everything you know about a competitor  — e.g., products, leadership, history, tactics — and devise plays that will help you win against them.  Then train sales on how to run those plays and supp0rt them in so doing.

If you adopt this mindset you end up with an organization where:

  • Product marketing and competitive are separate functions, both reporting directly to the CMO
  • Product marketing is product-oriented, focused on articulation of features and benefits
  • Competitive is competitor-oriented, focused on using all available information to create plays that win deals and support sales in executing them
  • Product marketing staffing is driven by the number of products you’re covering
  • Competitive staffing is driven by the number of competitors you’re covering (and at what depth level or tier).
  • You end up with a ratio of more like 3:1 than 10:1 when it comes to the relative staffing of product marketing and competitive

You think of these organizations as a matrix:

# # #

Notes

[1]  In the case of the reps, their response was to walk away from financial services deals because they knew they were likely to lose.  This, of course, had the effect of making it easier for Sybase to enter the market.  The smart reps went to Westchester and Long Island and sold in other verticals.  The dumb ones battled Sybase on Wall Street, lost deals, missed mortgage payments, broke marriages, and got fired — all for doing what the c0mpany strategically should have wanted them to do:  to slow down the invasion.   A classic case of micro and macro non-alignment of interests.

[2] The corporate response was to blame sales management.  Rather than seeing the situation as a strategic problem where an enemy was breaking through lines with an integrated strategy (e.g., partners), they chose to see it as an operational or execution problem.  Think:  we’re hiring bad reps in NYC and losing a lot deals — fire the sales manager and get some new talent in there.

[3]  Good Strategy, Bad Strategy tells the presumably more common inverse tale, where during the Gulf War in 1991 General Schwarzkopf was widely credited with a left-hook strategy described as “surprise,” “secret,” and “brilliant,” that was clearly published in the US Army Field Manual 100-5 saying the following, complete with an illustration of a left hook.

Envelopment avoids the enemy’s front, where its forces are most protected and his fires most easily concentrated. Instead, while fixing the defender’s attention forward by supporting or diversionary attacks, the attacker maneuvers his main effort around or over the enemy’s defenses to strike at his flanks and rear.

[4] Gekko refers to:  “Every battle is won before it’s ever fought.”

[5] Organization design is all about creating and managing healthy tensions.  Such tensions are a key reason why I like marketing reporting to the CEO (and not sales), customer success reporting to the CEO (and not the CRO/sales), and engineering reporting to the CEO (and not product), for a few examples.

[6] At one point, way back, Oracle had a huge market intelligence organization, but housed within Americas Marketing, a field marketing organization.

[7] Content being collateral (e.g., web content, white papers, e-books), presentations (internal and external), and demonstrations — all built around communicating the key messages in their messaging blueprint.

[8] Often, but not always, with a primary emphasis on differentiation.

[9] It’s not lost on me that the character was morally bankrupt and was implicitly saying to win at any and all costs.  But I nevertheless still love the quote.  (And yes, win within normal legal and societal constraints!  But win.)

Product Power Breakfast with Chris McLaughlin on Big/Small, US/Euro, and Marketing/Product

This week’s episode of the SaaS Product Power Breakfast is Thursday, June 10th, at 8am Pacific and we welcome a special friend and unique guest, Chris McLaughlin, currently CMO at France-based powerhouse LumApps, a collaboration and communications platform backed by top European investors including Idinvest and Goldman Sachs.

I got to know Chris by working together in his prior gig as joint CMO and CPO at Nuxeo, a France-based content services platform that had a great exit earlier this year to Thoma Bravo / Hyland Software, and where I sat on the board of directors for the past 4 years.

Chris has a unique background because of its dualities, working:

  • As a senior executive for both US-based and European-based companies.
  • At both growth startups and large megavendors (e.g., EMC/Documentum, IBM/FileNet)
  • In leadership roles on both the Product and the Marketing side.

In this week’s episode we — and the audience — will ask Chris many questions, including:

  • How to get product and marketing working together, especially when they aren’t under a common boss.
  • How European startups should organize their go-to-market functions to enter and grow in the US market
  • The role of both the product and marketing leaders in startups with either a technical founder or business founder
  • When is the right time to hire your first CPO and/or CMO
  • How to align product, marketing, and sales around a strategy — and dealing with the normal challenges in focusing that strategy

See you there, Thursday 6/10 at 8 am Pacific — and bring a friend.

As always, the room will be recorded and posted.  We think of the show as a podcast recorded in front of a live, studio audience.

A Ten-Point Sales Management Framework for Enterprise SaaS Startups

In this post, I’ll present what I view as the minimum sales management framework for an enterprise SaaS startup — i.e., the basics you should have covered as you seek to build and scale your sales organization [1].

  1. Weekly sheet
  2. Pipeline management rules, with an optional stage matrix
  3. Forecasting rules
  4. Weekly forecast calls
  5. Thrice-quarterly pipeline scrubs
  6. Deal reviews
  7. Hiring profiles
  8. Onboarding program
  9. Quarterly metrics
  10. Gong

Weekly Sheet
A weekly sheet, such as the one used here, that allows you to track, communicate, and intelligently converse about the forecast and its evolution.  Note this is the sheet I’d use for the CEO’s weekly staff meeting.  The CRO will have their own, different one for the sales team’s weekly forecast call.

Pipeline Management Rules with Optional Stage Matrix
This is a 2-3 page document that defines a sales opportunity and the key fields associated with one, including:

  • Close date (e.g., natural vs. pulled-forward)
  • Value (e.g., socialized, placeholder, aspiration, upside)
  • Stage (e.g., solution fit, deep dive, demo, vendor of choice)
  • Forecast category (e.g., upside, forecast, commit)

Without these definitions in place and actively enforced, all the numbers in the weekly sheet are gobbledygook.  Some sales managers additionally create a one-page stage matrix that typically has the following rows:

  • Stage name (I like including numbers in stage names to accelerate conversations, e.g., s2+ pipeline or s4 conversion rate)
  • Definition
  • Mandatory actions (i.e., you can be fired for not doing these)
  • Recommended actions (i.e., to win deals we think you should be doing these)
  • Exit criteria

If your stage definitions are sufficiently simple and clear you may not need a stage matrix.  If you choose to create one, avoid these traps:  not enforcing mandatory actions (just downgrade them to recommended) and multiple and/or confusing exit criteria.  I’ve seen stage matrices where you could win the deal before completing all six of the stage-three exit criteria!

Forecasting Rules
A one-page document that defines how the company expects reps to forecast.  For example, I’d include:

  • Confidence level (i.e., the percent of the time you are expected to hit your forecast)
  • Cut rules (e.g., if you cut your forecast, cut it enough so the next move is up — aka, the always-be-upsloping rule.)
  • Timing rules (e.g., if you can forecast next-quarter deals in this quarter’s forecast)
  • Management rules (e.g., whether managers should bludgeon reps into increasing their forecast)

Weekly Forecast Calls
A weekly call with the salesreps to discuss their forecasts.  Much to my horror, I often need to remind sales managers that these calls should be focused on the numbers — because many salespeople seem to love to talk about everything but.

For accountability reasons, I like people saying things that are already in Salesforce and that I could theoretically just read myself.  Thus, I think these calls should sound like:

Manager:  Kelly, what are you calling for the quarter?
Kelly:  $450K
Manager:  What’s that composed of?
Kelly:  Three deals.  A at $150K, B at $200K, and C at $100K.
Manager:  Do you have any upside?
Kelly:  $150K.  I might be able to pull deal D forward.

I dislike storytelling on forecast calls (e.g., stories about what happened at the account last week).  If you want to focus on how to win a given deal, let’s do that in a deal review.  If we want to examine the state of a rep’s pipeline, let’s do that in a pipeline scrub.  On a forecast call, let’s forecast.

I cannot overstate the importance of separating these three types of meetings. Pipeline scrubs are about scrubbing, deal reviews are about winning, and forecast calls are about forecasting.  Blend them at your peril.

Thrice-Quarterly Pipeline Scrubs
A call focused solely on reviewing all the opportunities in the sales pipeline.  The focus should be on verification:

  • Are all the opportunities actually valid in accordance with our definition of a sales opportunity?
  • Are the four key fields (close date, value, stage, forecast category) properly and accurately completed?
  • All means all.  While we can put more focus on this-quarter and next-quarter pipeline, we need to review the entire thing to ensure that reps aren’t dumping losses in out-quarters or using fake oppties to squat on accountants.

I like when these calls are done in small groups (e.g., regions) with each rep taking their turn in the hot seat.  Too large a group wastes everyone’s time.  Too small forgoes a learning opportunity, where reps can learn by watching the scrubs of other reps.

As a non-believer in alleged continuous scrubbing, I like doing these scrubs in weeks 2, 5, and 8 so the data presented to the executive staff is clean in weeks 3, 6, and 9.  See this threepart series for more.

Deal Reviews
As a huge fan of Selling Through Curiosity, I believe a salesperson’s job is to ask great questions that both reveal what’s happening in the account and lead the customer in our direction.  Accordingly, I believe that a sales manager’s job is to ask great questions that help salesreps win deals.  That is the role of deal review.

A deal review is a separate meeting from a pipeline scrub or a forecast call, and focused on one thing:  winning.  What do we need to learn or do to win a given deal?  As such,

  • It’s a typically a two-hour meeting
  • Run by sales management, but in a peer-to-peer format (meaning multiple reps attend and reps ask each other questions)
  • Where a handful of reps volunteer to present their deals and be questioned about them
  • And the focus is on asking reps (open-ended) questions that will help them win their deals

Examples:

  • What questions can you ask that will reveal more about the evaluation process?
  • Why do you think we are vendor of choice?
  • What are the top reasons the customer wouldn’t select us and how are we proactively addressing them?
  • How would we know if we were actually in first place in the evaluation process?

Hiring Profiles
A key part of building an enterprise SaaS company is proving the repeatability of your sales process.  While I have also written a threepost series on that topic, the TLDR summary is that proving repeatability begins with answering this question:

Can you hire a standard rep and onboard them in a standard way to reliably produce a standard result?

The first step is defining a hiring profile, a one-page document that outlines what we’re looking for when we hire new salesreps.  While I like this expressed in a specific form, the key points are that:

  • It’s specific and clear — so we can know when we’ve found one and can tell recruiters if they’re producing pears when we asked for apples.
  • There’s a big enough “TAM” so we can scale — e.g., if the ideal salesrep worked at some niche firm that only had 10 salespeople, then we’re going to have trouble scaling our organization.

Onboarding Program
The second key element of repeatability is onboarding.  Startups should invest early in building and refining a standard onboarding program that ideally includes:

  • Pre-work (e.g., a reading list, videos)
  • Class time (e.g., a 3-5 day live program with a mix of speakers)
  • Homework (e.g., exercises to reinforce learnings)
  • Assessment (e.g., a final exam, group exercise)
  • Mentoring (e.g., an assigned mentor for 3-6 months)
  • Reinforcement (e.g., quarterly update training)

In determining whether all this demonstrates a standard result, this chart can be helpful.

Quarterly Metrics
Like all functions, sales should participate in an estaff-level quarterly business review (QBR), presenting an update with a high-quality metrics section, presented in a consistent format.  Those metrics should typically include:

  • Performance by segment (e.g., region, market)
  • Average sales cycle (ASC) and average sales price (ASP) analysis
  • Pipeline conversion analysis, by segment
  • Next-quarter pipeline analysis, by segment
  • Customer expansion analysis
  • Win/loss analysis off the CRM system, often complemented by a separate quarterly third-party study of won and lost deals
  • Rep ramping and productivity-capacity analysis (e.g., RREs)

Gong
As someone who prides himself on never giving blanket advice: everybody should use Gong.

I think it’s an effective and surprisingly broad tool that helps companies in ways both tactical and strategic from note-taking to coaching to messaging to sales enablement to alerting to management to forecasting to generally just connecting the executive staff to what actually happens in the trenches — Gong is an amazing tool that I think can benefit literally every SaaS sales organization.

# # #

Notes
[1] This post assumes the existence of functioning upstream work and processes, including (a) an agreement about goals for percentage of pipeline from the four pipeline sources (marketing, SDR/out, sales/out, and partners), (b) a philosophically aligned marketing department, (c) good marketing planning, such as the use of an inverted funnel model, (d) good sales planning, such as the use of a bookings capacity model, and (e) proper pipeline management as discussed in this threepart series.

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 to 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.

An Epitaph for Intrapreneurship

About twenty years ago, before I ran two startups as CEO and served as product-line general manager, I went through an intrapreneurship phase, where I was convinced that big companies should try to act like startups.  It was a fairly popular concept at the time.

Heck, we even decided to try the idea at Business Objects, launching a new analytical applications division called Ithena, with a mission to build CRM analytical applications on top of our platform.  We made a lot of mistakes with Ithena, which was the beginning of the end of my infatuation with the concept:

  • We staffed it with the wrong people.  Instead of hiring experts in CRM, we staffed it largely with experts in BI platforms.  Applications businesses are first and foremost about domain expertise.
  • They built the wrong thing.  Lacking CRM knowledge, they invested in building platform extensions that would be useful if one day you wanted to build a CRM analytical app.  From a procrastination viewpoint, it felt like a middle school dance.  Later, in Ithena’s wreckage, I found one of the prouder moments of my marketing career  — when I simply repositioned the product to what it was (versus what we wanted it to be), sales took off.
  • We blew the model.  They were both too close and too far.  They were in the same building, staffed largely with former parent-company employees, and they kept stock options in both the parent the spin-out.  It didn’t end up a new, different company.  It ended up a cool kids area within the existing one.
  • We created channel conflict with ourselves.  Exacerbated by the the thinness of the app, customers had trouble telling the app from the platform.  We’d have platform salesreps saying “just build the app yourself” and apps salesreps saying that you couldn’t.
  • They didn’t act like entrepreneurs.  They ran the place like big-company, process-oriented people, not scrappy entrepreneurs fighting for food to get through the week.  Favorite example:  they had hired a full-time director of salesops before they had any customers.  Great from an MBO achievement perspective (“check”).  But a full-time employee without any orders to book or sales to analyze?  Say what you will, but that would never happen at a startup.

As somebody who started out pretty enthralled with intrapreneurship, I ended up pretty jaded on it.

I was talking to a vendor about these topics the other day, and all these memories came back.  So I did quick bit of Googling to find out what happened to that intrapreneurship wave.  The answer is not much.

Entrepreneurship crushes intrapreneurship in Google Trends.  Just for fun, I added SPACs to see their relatively popularity.

Here’s my brief epitaph for intrapreneurship.  It didn’t work because:

  • Intrapreneurs are basically entrepreneurs without commitment.  And commitment, that burn the ships attitude, is key part of willing a startup into success.
  • The entry barriers to entrepreneurship, particularly in technology, are low.  It’s not that hard (provided you can dodge Silicon Valley’s sexism, ageism, and other undesirable -isms) for someone in love with an idea to quit their job, raise capital, and start a company.
  • The intrapreneurial venture is unable to prioritize its needs over those of the parent.  “As long as you’re living in my house, you’ll do things my way,” might work for parenting (and it doesn’t) but it definitely does not work for startup businesses.
  • With entrepreneurship one “yes” enables an idea, with intrapreneurship, one “no” can kill it.  What’s more, the sheer inertia in moving a decision through the hierarchy could kill an idea or cause a missed opportunity.
  • In terms of the ability to attract talent and raise capital, entrepreneurship beats intrapreneurship hands down.  Particularly today, where the IPO class of 2020 raised a mean of $350M prior to going public.

As one friend put it, it’s easy with intrapreneurship to end up with all the downsides of both models.  Better to be “all in” and redefine the new initiative into your corporate self image, or “all out” and spin it out as an independent entity.

I’m all for general mangers (GMs) acting as mini-CEOs, running products as a portfolio of businesses.  But that job, and it’s a hard one, is simply not the same as what entrepreneurs do in creating new ventures.  It’s not even close.

The intrapreneur is dead, long live the GM.

Unlearning as you Scale: Recording of my Costanoa Ventures 2020 Summit Presentation

Last month I presented Unlearning As You Scale at Costanoa Ventures 2020 Costanoa CEO UnSummit.  In response to several requests for a live recording of the presentation, I sat down this weekend and recorded the following.

Key topics discussed:

  • How to properly apply the popular Silicon Valley adage, “the folks who got you here aren’t the ones to take you to the next level.”
  • How to generalize that adage to not only people, but systems, processes, and strategies.
  • If and when required, how to hire next-level executives while avoiding common pitfalls.
  • How to critically think about success with your team.

 

An audio-only version of the presentation is here:

 

My original post on the event is here.

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.