Monthly Archives: January 2021

Kellblog 2021 Predictions

Posted on January 30, 2021 | 5 comments

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, more.  HIT.
  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 HIT.  DB-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.

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Posted in BI, GTM, Management, Metrics, Planning, Predictions, SaaS, Silicon Valley, Startups

An Epitaph for Intrapreneurship

Posted on January 26, 2021 | 5 comments

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.

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Posted in Enterprise, Entrepreneurship, Leadership, Management, Silicon Valley, Startups, Strategy

The Holy Grail of Enterprise Sales: Is a Repeatable Sales Process Enough?

Posted on January 14, 2021 | Leave a comment

(This is the third in a three-part restructuring and build-out of a previous post.  See note [1] for details.)

In the first two posts in this series, we first defined a repeatable sales process and then discussed how to prove that your sales process is repeatable.

All that was just the warm-up for the big idea in this series:  is repeatability enough?

The other day I was re-reading my favorite book on data governance (and yes I have one), Non-Invasive Data Governance by Bob Seiner.  Reading it reminded me of the Capability Maturity Model, from Carnegie Mellon’s Software Engineering Institute.

Here’s the picture that triggered my thinking:

Did you see it?  Look again.

Repeatable is level two in a five-level model.  Here we are in sales and marketing striving to achieve what our engineering counterparts would call 40% of the way there.  Doesn’t that explain a lot?

To think about what we should strive for, I’m going to switch models, to CMMI, which later replaced CMM.   While it lacks a level called “repeatable” – which is what got me thinking about the whole topic in the first place – I think it’s nevertheless a better model for thinking about sales [2].

Here’s a picture of CMMI:

I’d say that most of what I defined as a repeatable sales process fits into the CMMI model as level 3, defined.  What’s above that?

  • Level 4, quantitively managed. While most salesforces are great about quantitative measurement of the result – tracking and potentially segmenting metrics like quota performance, average sales price, expansion rates, win rates – fewer actually track and measure the sales process [3].  For example, time spent at each stage, activity monitoring by stage, conversion by stage, and leakage reason by stage.  Better yet, why just track these variables when you can act on them?  For example, put rules in place to take squatted opportunities from reps and give them to someone else [4], or create excess stage-aging reports that will be reviewed in management meetings.
  • Level 5, optimizing. The idea here is that once the process is defined and managed (not just tracked) quantitatively, then we should be in a mode where we are constantly improving the process.  To me, this means both analytics on the existing process as well as qualitative feedback and debate about how to make it better.  That is, we are not only in continual improvement mode when it comes to sales execution, but also when it comes to sale process.  We want to constantly strive to execute the process as best we can and also strive to improve the process.  This, in my estimation, is both a matter of culture and focus.  You need a culture that process- and process-improvement-oriented.  You need to take the time – as it’s often very hard to do in sales – to focus not just on results, but on the process and how to constantly improve it.

To answer my own question:  is repeatability enough?  No, it’s not.  It’s a great first step in the industrialization of your sales process, but it quickly then becomes the platform on which you start quantitative management and optimization.

So the new question should be not “is your sales process repeatable?” but “is it optimizing?”  And never “optimized,” because you’re never done.

# # #

Notes

[1] I have a bad habit, which I’ve been slowly overcoming, to accidently put real meat on one topic into an aside of a post on a different one.  After reading the original post, I realized that I’d buried the definition of a repeatable sales model and the tests for having one into a post that was really about applying CMMI to the sales model.  Ergo, as my penance, as a service to future readers, and to help my SEO, I am decomposing that post into three parts and elaborating on it during the restructuring process.

[2] The nuance is that in CMM you could have a process that was repeatable without being (formally) defined.  CMMI gets rid of this notion which, for whatever it’s worth, I think is pretty real in sales.  That is, without any formal definition, certain motions get repeated informally and through word of mouth.

[3] With the notable exception of average sales cycle length, which just about everyone tracks – but this just looks at the whole process, end to end.  (And some folks start it late, e.g., from-demo as opposed to from-acceptance.)

[4] Where squatting means accepting an opportunity but not working on it, either at all or sufficiently to keep it moving.

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Posted in Enterprise, Sales, Sales Model, Startups

The Holy Grail of Enterprise Sales: Proving a Repeatable Sales Process

Posted on January 14, 2021 | 1 comment

(This is the second in a three-part restructuring and build-out of a previous post.  See note [1] for details.)

In the prior post we introduced repeatable sales process as the Holy Grail of enterprise software sales and, unlike some who toss the term around rather casually, we defined a repeatable sales process as meaning you have six things:

  1. Standard hiring profile
  2. Standard onboarding program
  3. Standard support ratios
  4. Standard patch
  5. Standard kit
  6. Standard sales methodology

The point of this, of course, is to demonstrate that given these six standard elements you can consistently deliver a desirable, standard result.

The surprisingly elusive question is then, how to measure that?

  • Making plan?  This should be a necessary but not sufficient condition for proving repeatability.  As we’ll see below, you can make plan in healthy as well as unhealthy ways (e.g., off a small number of reps, off disproportionate expansion and weak new logo sales).
  • Realizing some percentage of your sales capacity?  I love this — and it’s quite useful if you’ve just lost or cut a big chunk of your salesforce and are ergo in the midst of a ramp reset — but it doesn’t prove repeatability because you can achieve it in both good and bad ways [2].
  • Having 80% of your salesreps at 100%+ of quota?  While I think percent of reps hitting quota is the right way to look at things, I think 80% at 100% is the wrong bar.

Why is defaulting to 80% of reps at 100%+ of quota the wrong bar?

  • The attainment percentage should vary as function of business model: with a velocity model, monthly quotas, and a $25K ARR average sales price (ASP), it’s a lot more applicable than with an enterprise model, annual quotas, and a $300K ASP.
  • 80% at 100%+ means you beat plan even if no one overperforms [3] – and that hopefully rarely happens.
  • There is a difference between annual and quarterly performance, so while 80% at 100% might be reasonable in some cases on an annual basis, on a quarterly basis it might be more like 50% — see the spreadsheet below for an example.
  • The reality of enterprise software is that performance is way more volatile than you might like it to be when you’re sitting in the board room
  • When we’re looking at overall productivity we might look at the entire salesforce, but when we’re looking at repeatability we should look at recently hired cohorts. Does 80% of your third-year reps at quota tell you as much about repeatability – and the presumed performance of new hires – as 80% of your first-year reps cohort?

Long story short, in enterprise software, I’d say 80% of salesreps at 80% of quota is healthy, providing the company is making plan.  I’d look at the most recent one-year and two-year cohorts more than the overall salesforce.  Most importantly, to limit survivor bias, I’d look at the attrition rate on each cohort and hope for nothing more than 20%/year.  What good is 80% at 80% of quota if 50% of the salesreps flamed out in the first year?  Tools like my salesrep ramp chart help with this analysis.

Just to make the point visceral, I’ll finish by showing a spreadsheet with a concrete example of what it looks like to make plan in a healthy vs. unhealthy way, and demonstrate that setting the bar at 80% of reps at 100% of quota is generally not realistic (particularly in a world of over-assignment).

If you look at the analysis near the bottom, you see the healthy company lands at 105% of plan, with 80% of reps at 80%+ of quota, and with only 40% of reps at 100%+ of quota.  The unhealthy company produces the same sales — landing the company at 105% of plan — but due to a more skewed distribution of performance gets there with only 47% of reps at 80%+ and only a mere 20% at 100%+.

In our final post in this series, we’ll ask the question:  is repeatability enough?

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Notes

[1] I have a bad habit, which I’ve been slowly overcoming, to accidently put real meat on one topic into an aside of a post on a different one.  After reading the original post, I realized that I’d buried the definition of a repeatable sales model and the tests for having one into a post that was really about applying CMMI to the sales model.  Ergo, as my penance, as a service to future readers, and to help my SEO, I am decomposing that post into three parts and elaborating on it during the restructuring process.

[2] Unless you’ve had either late hiring or unexpected attrition, 80% of your notional sales capacity should roughly be your operating plan targets.  So this is point is normally subtly equivalent to the prior one.

[3] Per the prior point, the typical over-assignment cushion is around 20%

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Posted in Enterprise, GTM, Marketing, Sales, Sales Model, Salesops, Startups

The Holy Grail of Enterprise Sales: Defining the Repeatable Sales Process

Posted on January 14, 2021 | 3 comments

(This is the first in a three-part restructuring and build-out of the prior post.  See note [1] for details.)

The number one question go-to-market question in any enterprise software startup is:  “do you have a repeatable sales process?” or, in more contemporary Silicon Valley patois, “do you have a repeatable sales motion?”

It’s one of the key milestones in startup evolution, which proceed roughly like:

  • Do you have a concept?
  • Do you have a working product?
  • Do you have any customer traction (e.g., $1M in ARR)?
  • Have you established product-market fit?
  • Do you have a repeatable sales process?

Now, when pressed to define “repeatable sales process,” I suspect many of those asking might reply along the same lines as the US Supreme Court in defining pornography:

“I shall not today attempt further to define the kinds of material I understand to be embraced… but I know it when I see it …”

That is, in my estimation, a lot of people throw the term around without defining it, so in the Kelloggian spirit of rigor, I thought I’d offer my definition:

A repeatable sales process means you have six things:

  1. Standard hiring profile
  2. Standard onboarding program
  3. Standard support ratios
  4. Standard patch
  5. Standard kit
  6. Standard sales methodology

All of which contribute to delivering a desirable, standard result.  Let’s take a deeper look at each:

  1. You hire salesreps with a standard hiring profile, including items such as years of experience, prior target employers or spaces, requisite skills, and personality assessments (e.g., DiSC, Hogan, CCAT).
  2. You give them a standard onboarding program, typically built by a dedicated director of sales productivity, using industry best practices, one to three weeks in length, and accompanied by ongoing clinics.
  3. You have standard support ratios (e.g., each rep gets 1/2 of a sales consultant, 1/3 of an SDR, and 1/6 of a sales manager).  As you grow, your sales model should also use ratios to staff more indirect forms of support such as alliances, salesops, and sales productivity.
  4. You have a standard patch (territory), and a method for creating one, where the rep can be successful.  This is typically a quantitative exercise done by salesops and ideally is accompanied by a patch-warming program [2] such that new reps don’t inherit cold patches.
  5. You have standard kit including tools such as collateral, presentations, demos, templates.  I strongly prefer fewer, better deliverables that reps actually know how to use to the more common deep piles of tools that make marketing feel productive, but that are misunderstood by sales and ineffective.
  6. You have a standard sales methodology that includes how you define and execute the sales process.  These include programs ranging from the boutique (e.g., Selling through Curiosity) to the mainstream (e.g., Force Management) to the classic (e.g., Customer-Centric Selling) and many more.  The purpose of these programs is two-fold:  to standardize language and process across the organization and to remind sales — in a technology feature-driven world — that customers buy products as solutions to problems, i.e., they buy 1/4″ holes, not 1/4″ bits.

And, most important, you can demonstrate that all of the above is delivering some desirable standard result, which will be the topic of the next post.

# # #

Notes

[1] I have a bad habit, which I’ve been slowly overcoming, to accidently put real meat on one topic into an aside of a post on a different one.  My favorite example:  it took me ~15 years to create a post on my marketing credo (marketing exists to make sales easier) despite mentioning it in passing in numerous posts.  After reading the prior post, I realized that I’d buried the definition of a repeatable sales model and the tests for having one into a post that was really about applying CMMI to the sales model.  Ergo, as my penance, as a service to future readers, and to help my SEO, I am decomposing that post into three parts and elaborating on it during the restructuring process.

[2] I think of patch-warming as field marketing for fallow patches.  Much as field marketing works to help existing reps in colder patches, why can’t we apply the same concepts to patches that will soon be occupied?  This is an important, yet often completely overlooked, aspect of reducing rep ramping time.

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Posted in Enterprise, Entrepreneurship, Marketing, Sales, Sales Model, Salesops, Startups