Kellblog Predictions for 2024

Posted on January 2, 2024 | 12 comments

Well, it’s that time of year again, time for my annual predictions post, now in its tenth incarnation.  As per my custom, let’s review my 2023 predictions before presenting those for 2024.  Please remember that I do these for fun and fun alone.  See my FAQ for my terms, disclosures, disclaimers, et cetera.

2023 Predictions Review

1. The great pendulum of Silicon Valley swings back.  Hit.  I think Silicon Valley is driven by a master pendulum that in turn drives numerous sub-pendulums — and they all swung back in 2023.  Valuations came down, structure regrettably came back, cashflow trumped growth, founder friendliness decreased, diligence generally flopped back from FOMO to FOFU, and companies again started to treat employees as, well, people they are paying to do work. 

2. The barbarians at the gate are back.  Partial hit.  They’re there, but not quite buying with the frenzy I’d anticipated.  The problem with buyer’s markets is that sellers can often wait — and it seems many have.  PE software acquisitions were at roughly pre-pandemic levels in the first three quarters of 2023, though still well below 2021 and 2022 highs. Notable deals on the year include Silver Lake buying Qualtrics ($12.5B) and SoftwareAG ($2.4B), Thoma Bravo buying Coupa ($8B), Clear Lake and Insight buying Alteryx ($4.4B), Vista buying Duck Creek ($2.6B), Francisco Partners buying Sumo Logic ($1.7B), and Symphony buying Momentive ($1.5B).  Expect more of this activity in 2024.

3. Retain is the new add.  Hit.  Customer retention came into sharp focus in 2023 and with it a new, balanced view relying on both NRR and GRR as key retention metrics.  As I said last year, “while this bodes well for the customer success (CS) discipline, it does not automatically bode well for the customer success department.” Some found themselves blown up (aka Slootmanned), often in hasty lose/lose transactions leaving customers dissatisfied with reduced attention levels and sales unhappy with additional work without additional resource or pay.  Blowing up customer success to save money is myopic.  Re-organizing it, or simply re-chartering it, with a more business-aligned mission is the key to success.  New technology (e.g., Hook) will help.  Jason Lemkin predicts a slow reboot of the customer success function in 2024. 

4. The Crux becomes the strategy book of the year.  Partial hit.  Two things went wrong here.  First, I was manifesting this prediction – I wanted it to be the strategy book of the year.  Second, I was late to the party.  I bought my copy in December, 2022 so to me it was a brand-new book, but it had been released seven months earlier and had already won recognition from the FT, Forbes, and The Globe & Mail.  Sales-wise, I don’t have access to great stats, but I can see its best ranking on Amazon is in Business Systems and Planning where it currently ranks 121st.  It should be in the top ten with Good to Great, Blue Ocean Strategy, Thinking in Bets, The Art of War, and its older sibling Good Strategy, Bad Strategy.  Popularity be damned, I think The Crux is a great book, better than its predecessor which does a great job tearing apart the garbage that passes for strategy, but a worse job of saying what to do about it.

5. The professionals take over for Musk.  Hit.  I almost downgraded this to a partial hit because “take over” may not properly describe what has happened with Linda Yaccarino.  But she is nevertheless the CEO, if perhaps in name only.  (And yes, I’m still reluctant to call Twitter X.)  The question today is not how long Musk lasts, but how long Yaccarino lasts.  Having withstood so much already, I think it’s unlikely that she’s gone in 2024, but I won’t waste a prediction on it this year.

6. The bloom comes off the consumption pricing rose.  Hit.  I’ve always felt the famous Warren Buffet quote applies to consumption-based pricing:  “when the tide goes out you can see who’s swimming naked.”  I’m scoring this a hit not because I think usage-based pricing (UBP) – as it’s also known — is bad, but because I felt it was overhyped and often pushed too hard on companies by investors chasing stratospheric (or Snowflake-spheric) net revenue retention rates (NRR).  In reality, UBP has both pros and cons and is better applied to some products than others.  While UBP companies were hit harder, as this slightly confusing slide from Iconiq demonstrates [1], they nevertheless grew faster than their subscription counterparts in 2023.  Consumption models are here to stay, but hopefully the industry can take a more balanced, rational view on them.

7. The rise of unified ops.  Partial hit.  I think organizations increasingly realize that stovepiped ops functions generate inconsistency, conflict, and excess cost.  Though here again, I was manifesting because I believe in unifying all go-to-market ops – e.g., salesops, servicesops, successops, and marketingops — into a single ops function.  Some companies call that unified function revops, others use revops to mean only the unification of sales and successops.  The big rock is to bring marketing into the unified team.  While it’s impossible to know the revops job description from the name alone, a phrase search for “sales operations” versus “revenue operations” on LinkedIn jobs reveals 3x more listings for salesops than revops.  We still have a long way to go, but I’m confident slowly and steadily these functions will integrate over time.  Every time a unified revops team is created an angel gets its wings. 

8. Data notebooks as the data app platform.  Hit.  This prediction is in large part a proxy for “Hex will prosper,” because I’m a big believer in their vision to create a collaborative analytics platform [2].  In a difficult fundraising environment they raised $28M from not just anyone but Sequoia, using my all-time favorite fundraising strategy —  not looking for money.   As of the round, they’d grown the business 4x over the prior year.  Per LinkedIn, headcount is up 240% over the past two years.  They continue to rapidly innovate on product.  They support a wide variety of use-cases that go well beyond data apps.  They’ve also expanded the personas they support.  And, for the marketers out there, they’re the first data-oriented company since Splunk to have a distinctive voice in their marketing (e.g., the Hex 3.0 launch subtitle, “one arbitrary version number for Hex, one giant leap for data people.”)  If you want to understand why I’m so excited about this company (and see concrete examples of what some of these data buzzwords mean), watch their latest product launch video.

9. Meetings somehow survive.  Hit.  I’m so glad the idiocy of companies are for builders, not managers was brief.  Yes, companies need to focus on continuous productivity improvement.  Yes, companies need to remain vigilant against unproductive meetings, particularly standing ones.  And yes, we can always do better.  But to suggest discarding the collaboration baby with the unproductive bathwater was always absurd.  If you want better meetings, read Death By Meeting.  But meetings were, and are, here to stay.

10. Silicon Valley thrives again in 2024.  TBD.  In a desire to end last year’s list on a positive note, I realize that I inadvertently included a 2024 prediction in my 2023 list.  Thus, the score on this prediction remains to be decided.  Despite a rough 2023, or more aptly, in part because of it, I remain optimistic that the Silicon Valley business environment will improve in 2024. 

Kellblog Predictions for 2024

1. Election Dejection. No matter your political leanings, the 2024 presidential election will be divisive, distracting, and quite probably depressing.  It will test our institutions, challenge supreme court legitimacy, and drown voters in higher-calling rhetoric about saving the country or saving democracy, as the case may be.  There will likely be a constitutional crisis or two along the way, for good measure.

To stay in my wheelhouse of Silicon Valley, communications, and to a lesser extent, media, I think three things will happen:

  • The media will make a dog’s breakfast of coverage.  Alternative facts.  Improper framing.  Narrative fallacy.  Bothsideism and false equivalence.  And many others.  Worst of all, due to a lazy preoccupation with oddsmaking, the media will abdicate a key duty in its coverage, wasting the coming months endlessly handicapping the outcome.  Instead of this horse-race journalism, the media should do what NYU professor Jay Rosen advises:  focus on not the odds, but the stakes in its coverage.
  • The election will test the once-veiled political neutrality of Silicon Valley.  For years, Silicon Valley was a place of quiet liberalism among workers and veiled libertarianism among overlords.  The attitude towards Washington was leave us alone and let us work [3].  In the past two decades, that’s changed with lobbying dollars up about 10x, more VCs and celebrity CEOs openly expressing political views, and the rise of podcasts with strong political leanings.  A16Z’s American Dynamism initiative has strong political overtones and is surprisingly nationalistic for an international firm [4].  Politics are coming out of the closet in Silicon Valley, for better or worse.
  • There will be a lot of infantile rhetoric.  The rise of social media dropped the level of our discourse, with many politicians only too happy to follow suit.  Today’s vile norms (e.g., name-calling) were unacceptable only 10 or 20 years ago.  This debasement will continue during the 2024 election cycle.  I refer readers to Graham’s hierarchy of disagreement as a framework for characterizing the quality of debate and to encourage everyone to climb, not descend, this ladder.

The ray of good news is that while the election will almost certainly be a mess, most Americans are exhausted by today’s politics and polarization.  Eventually, this should percolate into votes and candidates, and ultimately result in a government focused on consensus and compromise [5].  One hopes so, at least.

2. A Slow Bounceback in Startup Land. There’s blood in the Silicon Valley water.  3,200 startups failed in 2023.  Unicorns are turning into zombies.  The predicted mass extinction event appears to be upon us.  Those who can raise money face dilutive downrounds.  Even among healthier unicorns, there’s a large backlog of over-valued private companies trying to grow into a contemporary valuation before running out of cash.

On the financing side, VC funding was down to pre-pandemic levels.  OpenView surprised the industry with an abrupt shutdown in new investing.  Some predict that 25% of VC partners will exit the business in the next few years.  Silicon Valley Bank failed. 

So, what will happen in startup land in 2024?

  • We will start to turn the corner.  ARR growth stalled.  Valuation multiples were hammered.  But green shoots are emerging.  I think the worst of it is over, particularly for those companies that responded quickly to the downturn by increasing focus, reducing burn, and increasing runway.
  • This will happen more quickly on the startup side.  Net new ARR growth rates are already rebounding.  David Sacks is calling an end to the software recession of 2022 and 2023.  Gartner predicts software spend will grow 14% in 2024.  Things will recover, but they won’t snap back. 
  • And it will happen more slowly on the venture side.  Everything happens more slowly on the venture side [6].  While public markets can turn on a dime, venture funds are decade-long, illiquid, limited partnerships where prices are reset more quarterly than daily [7].  This creates a damping effect whereby dramatic change needs time to percolate through the system [8]. 

3. The Year of Efficient Growth. If 2023 ended up the year of hunkering down, then 2024 will be the year of efficient growth.  For the first time, an overall productivity measure, ARR/FTE, has crawled its way into the top 5 SaaS metrics [9].  See chart below for how it varies with scale [10].

The rule of 40 (R40) is back with a vengeance.  R40-compliant companies currently command a 61% EV/R multiple premium over their non-compliant counterparts [11].  In a two-factor regression, the relative importance of growth to profitability in predicting EV/R multiples is currently around 2.0 [12] – so growth and profit both matter, but growth still matters more. Because of that, and because Bessemer believes that the relative impact should change as a function of scale, they have introduced a new metric, the rule of X, which is a variably growth-weighted rule of 40 [13].  Don’t read the article with the understanding that there will be no math.  There’s plenty of it.

The ultimate sales pitch for the rule of X is its superior explanatory power of the EV/R multiple, as depicted in the chart below [14]. 

While I have several concerns about this proposed metric [15], the point is that Bessemer, a thought leader in SaaS metrics who to my knowledge defined and/or were early evangelists of CAC, CPP, and CCS, is spending time and energy on a growth/profit balance metric.  That’s the point.  GAAC is dead.  Long live balanced growth and profit. 

In 2024, expect emphasis on the usual go-to-market (GTM) efficiency metrics like CAC, CPP, and LTV/CAC, continued emphasis on both net and gross retention rates (NRR and GRR), new emphasis on overall productivity (ARR/FTE) and balanced growth measures (R40), and of course strong attention to cash burn efficiency (burn multiple).

4. AI Climbs the Hype Cycle. In 2023, artificial intelligence peaked on Gartner’s hype cycle. It garnered significant attention, particularly in sectors like healthcare, finance, and entertainment, promising personalized solutions and immersive experiences. However, amid this excitement, there was a growing awareness of AI’s challenges, including ethics and regulations. This marked a crucial juncture for AI, transitioning from hype to practical use, demanding responsible implementation.

Perhaps you noticed the change in voice — the prior paragraph was written by ChatGPT.  While I think I’m still winning my John Henry battle with generative AI, I know my lead won’t last forever.  Writers fighting ChatGPT are like mathematicians fighting calculators. 

Last year was an amazing year for AI, one that both inspired and frightened us.  While 40% of humans don’t pass the Turing test, ChatGPT can now pass as human about 40% of the time.  Marc Andreessen, in his role as public intellectual, declared that AI will save the world (presumably after software has finished eating it).  Some see Andreessen’s manifesto as visionary, others as self-serving, but it’s well worth reading as is this Stratechery interview. For extra fun, watch the techies debate on Hacker News. 

Should we lean into AI as the e/acc movement believes, or should we pull back to avoid turning humanity into collateral damage from an AI all-consumed with making paperclips? 

If you don’t have time for Marc’s philosophy, I recommend Ben Evans’ wonderful, more down-to-earth deck on AI.  It hits all the key issues with a nice balance of insights, examples, and just enough Meeker-style trends data [16].

In 2024, I think AI will continue to blow our socks off as we climb to peak hype.  Vendors will propose a wide variety of use-cases, some of which will stick while others will not.  Some features will become companies and some products will become features [17].  What’s a technology consumer to do?  Allocate time to experiment with a broad range of AI features and products.  I expect many AI solutions to go from magical advantage to table stakes almost overnight. 

In 2024, AI will continue to pose interesting questions in four areas:

  • Philosophical.  The semantics of predicting vs. reasoning.  See this amazing interview with Jensen Huang and Ilya Sutskever, in particular the part where Ilya presents his detective novel analogy.  Goosebumps. 
  • Practical.  Are you getting quality answers that you can trust or generating botshit?  Do generated answers include hallucinations, as a hapless lawyer discovered, or math challenges, as highlighted by Stephen Wolfram? 
  • Legal.  Copyright and fair use questions reminiscent of Internet 1.0.  Will OpenAI have their Napster moment?  Read the New York Times complaint.  While not yet at the forefront of debate, my friend Anshu Sharma often highlights important privacy concerns as well.
  • Pricing.  Much as SaaS moved the industry from perpetual to subscription (and then consumption) pricing, will AI move the industry to value- or results-based pricing? [18]

5. AI-Driven GTM Efficiency. We are experiencing a Cambrian explosion of enterprise AI tools.  Here’s a part of Sequoia’s map to them.  And these are just the leaders.

In marketing, you can find tools for anything including media relations, image editing, presentations, storytelling, personalization, SEO, content, copywriting, leadgen, intent, engagement, automation, cross-channel messaging, nurture, outbound, social, advertising, and even analytics [19]. 

In sales, you can find maps like this:

These things are everywhere.  And we’ve not even discussed customer success, customer support (e.g., chatbots), or professional services.

My prediction is that this Cambrian explosion will continue into 2024 and by the end of the year things will start to sort out.  What does that mean?

  • If you’re a vendor, you’re playing musical chairs and you should go all-out to ensure you have a seat when the music stops (i.e., the market starts to organize)
  • If you’re a customer, you should allocate real time to play with and explore these tools.  Don’t be too busy fighting battles with swords to talk to the machine gun salesperson.
  • If you’re a GTM executive, you should understand that your investors expect real productivity gains from these tools.

In terms of gains, this slide from Battery argues that an AI-enabled sales team with 75 people can support the same number of sellers (and drive the same quota) as a traditional 110-person team.  Are you ready for this board conversation?  You should be.

6. Beyond Search. The traditional search business is in trouble.  For decades, information retrieval people have pleaded for “answers, not links.” While Google has made progress over the years at providing answers (e.g., featured snippets, PAA) [20], generative AI clearly delivers the answers that many have sought for so long.

Search today is in roughly the same mess that it was in the pre-PageRank days of Yahoo and AltaVista.  Bombed out.  Gamed out.  Loaded with clickbait.  Over advertised.  It’s just increasingly hard to find what you’re looking for.  And that’s before the coming, widespread creation of more AI-generated, SEO-driven content.  More cruft to jam up the system. 

Well, Clayton Christensen to the rescue.  We are watching the cycle of disruptive innovation play out.  As Google continues to cater to its existing customers and is increasingly run by extractors as opposed to innovators, they create the opportunity for disruption.  Now, since Google is a very smart company, they’re not flat-footed in response and are very much trying to disrupt themselves.  But, regardless of which vendors win, I expect generative AI’s answers to largely replace traditional search’s lists-of-links going forward.

This will have a huge impact on SEO.  For example, the question will no longer be “are you above the fold?” but instead, “are you in the answer or not?”  Consider this example, where I asked ChatGPT to make a short-list of conversation intelligence tools to evaluate.

You’re either on that list or you’re not [21].  There is no next page — no consolation prize if you will.  Perhaps that’s not really a change because few people clicked on subsequent pages anyway.  But I think the stakes are going up in an increasingly winner-take-all race — where most of us currently lack the requisite knowledge and skills to even compete.  I’m not talking about how to use ChatGPT for traditional SEO and generate more cruft.  I’m talking about optimizing your content for inclusion in ChatGPT results.  SEO is dead.  Long live ChatGPTO.

For decades, information retrieval expert Stephen Arnold has written a blog called Beyond Search. In 2024, we’re finally going to get there.

7. From RAGs to Riches. Consider this now famous chat with Chevy of Watsonville.

That feeling when your chatbot is overqualified for the job.

General-purpose, large language models (LLMs) can suffer from three weaknesses:

  • Broad scope, in many applications far broader than is necessary or desirable.
  • Inability to inform them with specialized knowledgebases and/or supplemental information after the model has been trained.
  • No sourcing, making hallucination detection more difficult and limiting their use in environments that require sources.

A relatively new technology, introduced in 2020, called retrieval-augmented generation (RAG) solves these problems.  This article provides a great technical overview of RAG.  IBM Research also wrote a great high-level overview, including two nice analogies: 

“It’s the difference between an open-book and a closed-book exam,” Lastras said. “In a RAG system, you are asking the model to respond to a question by browsing through the content in a book, as opposed to trying to remember facts from memory.”

And

“Think of the model as an overeager junior employee that blurts out an answer before checking the facts,” said Lastras. “Experience teaches us to stop and say when we don’t know something. But LLMs need to be explicitly trained to recognize questions they can’t answer.”

From what I understand of RAG, I like it because it’s a practical approach for eliminating problems with LLMs that adds enterprise features like use of existing knowledgebases and references to sources. 

In 2024, I think we’ll be hearing a lot more about RAG.  Salesforce has added it to Einstein.  Glean has raised over $150M from Sequoia and others to reinvent enterprise search using RAG.  Cohere has raised over $400M from Index and others to build conversational apps with RAG.  Many more will follow.

8. Outbound Finds Its Proper Place. Debates about outbound heat up faster than honey in a microwave oven.  Particularly when companies (often quite prematurely) think they have picked all the low-hanging inbound fruit, outbound becomes a religious issue, fast.  Here are some of the reasons I’ve heard for this:

  • The great hope.  It must succeed because other methods are topping out or failing (and execution quality couldn’t possibly be the reason).
  • It worked before.  Five years ago at my last company, even if it was in a different situation, with a different strategy, in a different time.
  • I was brought here to make it work.  It’s why the CEO hired me.  I know how to build it.
  • Sales wants control of its own destiny.  Even if it’s inefficient, I don’t want to be so dependent on marketing.
  • I need outbound SDRs to groom into sellers.  They’re my funnel for filling AE headcount.
  • I want a club to beat sellers.  When sellers complain about lack of leads, I need to be able to say:  “So what have you done to help yourself?”

The last point is true only in cases where sellers are required to generate a certain amount of their own pipeline, which, with the exception of account-based marketing (ABM) models, I don’t think they should do.  Remember the quote:  “sellers are like airplanes, they only make money when they’re in the air.”

Recently, I’ve heard more and more CEOs abandon this religious belief in outbound.  That’s good.  Standalone outbound [22] is a low-conversion rate activity.  Stalk someone.  Twist their arm to agree to a meeting.  See if they show up.  (Often, they don’t, so repeat the stalking process.)  Try to convince them they need to buy in your category and then to buy from  you.  See what happens. 

If you were a seller, which would you prefer?

  • The stalked, arm-twisted lead above, or
  • Someone who found us through an organic search, downloaded a white paper, attended a weekly demo session, rated it highly, and asked to speak to a seller

Conversion rates usually reflect this [23].  Partner- and inbound-generated leads often convert at double or triple the rate of outbound.  I expect standalone outbound effectiveness to only get worse because of the AI-driven tools arms race.  Every SDR will be sending AI-generated, personalized email sequences.  And that’s not to mention the new Gmail anti-spam rules that go into effect in February.

What’s the glaring exception here?  ABM, done properly.  When a company targets a small number of accounts, focuses sellers on penetrating them, and aligns both marketing and outbound SDRs as part of the effort.  In effect, the whole company stalks the customer, not just an SDR.  Does this work?  Yes, absolutely.  What’s the catch?  That’s simple:

Is the juice worth the squeeze?

ABM is a lot of work.  You shouldn’t bother trying it to win a $10K or even a $50K deal.  But when you can do $100K to $500K+ deals and have a few strong references in a vertical to which your company has strategically committed, that is when you should do ABM. 

Outbound isn’t Santa Claus.  It’s just a nice old man with whiskers.  In 2024, I think many companies will figure that out.   

9. The Reprise of Repricing. Compressed valuation multiples and reduced growth mean lower stock prices.  That’s no surprise.  However, this creates real problems with equity-based compensation, greatly lowering or entirely eliminating its value.  Let’s look at two common equity-based compensation methods.

  • RSUs which are typically granted in terms of value.  For example, if you’re granted $400K worth of RSUs over 4 years when the stock is $50, you get 8,000 shares over 4 years or 500 shares/quarter.  If the stock falls to $20, you’re now vesting $10K per quarter instead of $25K.  That’s a big compensation hit.
  • Stock options which are the right to buy shares at a fixed price, typically the stock’s value on the day the option is granted.  For example, you are granted 8,000 shares over 4 years when the stock price is $50.  If the stock falls to $20, your option is “underwater,” meaning it’s basically worthless because the market price is well below your strike price [24].  That’s an even bigger compensation hit [25].

Now, let’s imagine that we at GoodCo have a similar competitor across the street called NiceCo, and that NiceCo’s stock has suffered similarly.  I can stay at GoodCo and vest equity compensation at a reduced or zero rate, or I can quit, cross the street to work at NiceCo, and get a new grant.

  • For RSUs, I might get a new grant of 20,000 shares and vest at my original $25K/quarter rate.  And feel like there’s upside because the stock may appreciate from there.
  • For options, I might get a new 20,000 share grant at a strike price of $20/share, a no-brainer compared to my existing grant of far fewer shares at a far higher price [26] [27].

How can GoodCo retain its employees in this situation?  The short answer — barring soft factors like superior management, culture, and perks — is they can’t.  This is a major problem and left unsolved, GoodCo will lose a lot of employees to NiceCo [28].

Enter repricing.  While I won’t get into the details, the basic idea for stock options is that in return for some modest consideration (e.g., a reduction in share count), the company will reset the strike price on the options in the example above from $50 to $20.  While the concept is simple, the rules are different for public and private companies and, unsurprisingly, public companies are more restricted in what they can do.

For RSUs, it’s slightly different.  Technically speaking you don’t need to reprice anything.  The company can simply grant more RSUs to make up the difference in reduced value.  Or, it seems they can run a sort of repricing where they, e.g., redo the initial grant math to produce  a new higher vest rate, but in exchange for a vesting reset. 

After that long introduction, my prediction is simple.  In 2024, repricing will be back.  If your company has a greatly reduced valuation and is not talking about repricing or its equivalents, then you might want to ask them.  I’d advise some patience because these things can take time.  And bear in mind these rules often vary a lot by country.

As always with financial and career matters, make your own decisions, consult your own advisors, and ensure you understand Kellblog terms and disclaimers.  You can also read a book like Consider Your Options for more information. 

10. Peak Podcasting. For years, podcasts have been on the rise, with the pandemic driving a massive peak in podcast creation.  One of the better-kept B2B marketing secrets was that starting a CEO podcast could serve as a structured way to help CEOs, particularly introverted ones, get out there and meet new, important people.  Creating a CEO podcast was the ultimate three-fer, improving:

  • Communications, driving company messages and positioning the founder/CEO as a thought leader.
  • Customer relationships, gaining access to and/or reinforcing relationships with next-level executive contacts as invited guests.
  • Partner relationships, interviewing fellow CEOs, greasing the skids for many kinds of partnerships, potentially including the one that eventually sells the company.

If you like the sound of that and haven’t started one yet, I still think it’s a good idea.  But start fast.  It takes a long time to build an audience and I think in 2024 we will hit peak CEO podcast, for the simple reason that the word is getting out.  My feeling is largely intuition-driven – podcast advertising forecasts still paint quite a rosy picture – but I think the software market will tire of B2B CEO podcasts over the next few years.  If you don’t believe me, ask the podcast police. 

@squilliam_peters

Poor guy doesn’t even have a cohost #comedy #sketch #podcast #sketchcomedy

♬ original sound – Will Peters

If you want to create a podcast, make sure everyone understands why you’re doing it, get buy-in for a long-term, high-frequency commitment [29], and start now.  That should keep the podcast police away.

Thank you for reading to the end, and I wish everyone a happy and healthy 2024.

# # #

Notes

[1] The two bars on the right make the point – they compare top-quartile growth rates of UBP and subscription companies.  This slide doesn’t do the world’s best job of making this point, but I’ve seen it in other studies as well.

[2] Note that while I’m an angel investor in Hex, I do not work closely or actively with the company so my conclusions about their progress are based entirely on external observation. 

[3] With the major exceptions of government-funded research projects (e.g., DARPA) and, usually as companies gain in scale, the embrace of government as a customer.

[4] Though I suspect they’d argue it’s a US-focused practice more than a firmwide initiative, but that hasn’t been 100% clear to me in reading about it.

[5] Which I believe was the subtitle of my American Government textbook back in college.

[6] Even OpenView’s “abrupt” shutdown was not an overnight closing of the doors; it was a cessation in new investment.  As the firm noted, it will continue to exist and support existing investments until the existing funds reach their eventual conclusions – which can be years, even for growth investors.

[7] While new investments and valuations can turn on a dime, the rest of the business is focused on the long-term task of building companies and delivering TVPI, DPI, and IRR over a decade or so.

[8] IMHO, this damping is a good thing because it damps out irrationality as well.  You can’t see a bank run on a venture fund, because investors generally don’t have the right to demand their money back. 

[9] Iconiq New Era of Efficient Growth, slide 16.

[10] OpenView 2023 SaaS Benchmarks, slide 41.

[11] Battery State of the OpenCloud, slide 8.

[12] Bessemer State of the Cloud, slides 14-16.  This shows nicely the growth at all costs era (6.0x), the trough after the peak (0.8x) and return to normal (2.0x).  While growth is still twice as important as profit in predicting valuation, the balance still matters.

[13] SEG’s growth-weighted rule of 40 is double the 2x growth-weighted-average of growth and profit (i.e., it’s like a weighted average that isn’t averaged because they don’t take the last step and divide by 2). SEG does this to stay consistent with R40 which is the sum of profit and growth, not the average. In the rule of X, the relative weight (i.e., the “multiplier”) varies – over time and across stage.  This makes the metric more complex, less comparable across stage and time, and produces a wider ranges of outcomes. For example, a company whose (growth, profit) is (100%, 50%) scores 150 on R40 and scores 950 on RX when the multipler is 9x, 130 when the multiplier is 0.8x, and 280 when the multiplier is 2.3x.

[14] Frankly, this argument strikes me as circular.  If you’re getting the weight multipler from a regression of the current market, it seems obvious that you’d expect a higher R^2 compared to any fixed weighting of growth and profit, including the default weight of one in the rule of 40.

[15] My concerns:  bad name (if rule of 40 abbreviates to R40, this abbreviates to RX), hard to interpret scores, incomparability across stages and time, and seeming circular logic (see prior note).  Their ultimate point is correct:  growth matters more and blind adherence to an unweighted rule of 40 may take you to the wrong place.  But this metric needs some more work.

[16] Meeker was legendary for drowning the audience in nevertheless interesting data in her annual tech trends reports.  As my dearly departed father might have said, “there’s enough here to gag a maggot.”

[17] Ben Evans covers these ideas, starting on slide 39 of his deck.

[18] The argument in favor is that AI will create a lot of value, vendors want to capture that value, and vendors are certain enough that they’re willing to take the downside risk to get the upside.  The argument against is that value creates an upper bound on pricing, but the lower bound is determined by the price of alternatives.  At Host Analytics, I could replace a Hyperion system that cost $500K/year with a SaaS app that cost $50K.  That’s a lot of value to tap.  But if Adaptive Insights were willing to do the same deal at $25K, then the price of alternatives, not value, became the focus of the conversation and differentiation the focus of the sales cycle.

[19] Please note that none of these references are endorsements, I don’t know many of the companies, and I’m sure many would be unhappy with my chosen one-word label.  The point is to show the breadth and depth of the market.

[20] Front-running content producers in the process – e.g., featured snippets provide answers that leverage content producers’ content while eliminating and/or reducing traffic to their sites.

[21] This example also shows the problems with ChatGPT’s cut-off date, e.g., it doesn’t seem to know that Chorus is now part of Zoominfo.

[22] By standalone outbound, I mean outbound not done as part of a bigger ABM program.

[23] Unless they’ve been gamed to over-credit outbound as is sometimes the case when a company has “outbound fever.”

[24] Technically, even an underwater option has value because of its time value and the chance the stock price may rise above the strike price at some point in the future during the life of the option.  In my example, it needs to go up by 150% before the option has any intrinsic value. 

[25] Though these days an increasing number of tech workers are jaded with stock options, may value them at zero, and see them as pure upside – e.g., lottery tickets on top of their cash compensation.   In that case, there is no “hit” per se to compensation, because they were expecting zero value anyway.

[26] If the company derives option grants from value, they’d say:  we’ll grant you $400K worth of value, so at $20/share, that’s 20,000 shares.  Even if they don’t work this way and simply offer to match the number of shares, the job-switcher is still offered a far better deal — 8,000 shares at a $20 strike price, as opposed to $50.

[27] Note that other factors come into play here, including the fact that grant sizes tend to decrease over time.  For example, if you’ve been at GoodCo for four years with an initial grant of 8,000 shares, the going rate for your job might have dropped to 2,000 shares.  Thus, crossing the street to NiceCo might result in a grant at a lower strike price, but with a much smaller number of shares.  I think this is somewhat less true of RSUs (because they feel more a part of annual compensation as opposed to gravy on top), but I’d need to think more to be sure.

[28] That said, in this example, they can presumably hire NiceCo employees in the same situation.  That aside, neither company benefits from the mass rotation of employees.

[29] Because that’s what it takes to climb the charts.  And some advertising spend doesn’t hurt either.

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The Top 7 Marketing Metrics for a QBR or Board Meeting

Posted on December 20, 2023 | 8 comments

The other day an old friend, a highly accomplished marketing executive, asked me a simple question: if you only had five metrics to summarize marketing performance for a quarterly business review (QBR) or board meeting, what would you pick?

In this post, I’ll share my answer to that question. (Hint: I cheated and used seven.) 

I made my list from scratch. In order to avoid any anchor bias, I refused to even look at the draft list she sent me before coming up with my answer. 

I kinda cheated a second time because I grouped each metric under a heading. I like to remind people of marketing’s priorities, hopefully demonstrating alignment in the process. And, if marketing is not aligned, taking a grouped approach provides a clear opportunity for someone to speak up. Note that unlike some Kellblog posts, I won’t talk much here about formatting the metrics [1]. Instead, I’m going to focus on the metrics themselves. What should we measure?

If I were to present these, I’d preface this by saying, “Good morning, great to see everyone, and as a reminder, here’s what we do here in marketing. In priority order, we …”

We Make Pipeline That Closes

1. Marketing-sourced pipeline generation. I prefer measuring pipeline generation using opportunity count, not dollars, both because it’s more visceral and, particularly when there is a broad range of opportunity values [2], it can be impossible to know the value of an opportunity without getting fairly far into the sales cycle [3]. (And don’t worry, we’ll cover dollars below in metric three where it matters even more.) This metric is about count. Think: we agreed that marketing needed to generate 110 stage-two opportunities [4] during the quarter and we generated 120.

2. Marketing-sourced pipeline conversion. Because we understand that the point is not just to generate pipeline (which is really only a leading indicator), but to generate sales, we measure the conversion rate of marketing-generated pipeline. The trick is that this is an inherently lagged measure and the longer your sales cycle, the longer your lag. To make this concrete, the table below demonstrates an idea I call time-based close rates. If you generate 120 opportunities this quarter, while 23% of them may close in the fullness of time, 2% close this quarter, 4% next quarter, 10% the quarter after that, and so on.

Because sales lives quarter-to-quarter [5] and will die waiting for the fullness of time, we must factor this progression into our planning. We must also account for it in our metrics and the only good solution I know is to use trailing twelve month (TTM) conversion rates [6] [7]. Note that the CMO is stuck on the horns of a dilemma: either face criticism for using a lagging but highly sales-aligned indicator or face criticism for using a leading indicator that might not result in sales [8]. I’ll take the former in this case, particularly because so many other marketing KPIs are only leading indicators of sales. 

We care about pipeline that closes, not just pipeline that gets created or advances to demo stage, but pipeline that closes. I show that caring with this metric.

We Tee Up Sales for Success Each Quarter

3. Day-one pipeline coverage. This ties to my idea that the CMO should be the quarterback of the pipeline. Not just the marketing-sourced pipeline, but the whole pipeline. Most companies have four sources of pipeline with specific targets for each. For example, 60% from marketing, 20% from partners, 10% from outbound SDRs, and 10% from sales. The way most organizations are structured, the only person who owns all four sources is the CEO. Thus, insanely, in most organizations there is no natural owner for the overall pipeline other than the CEO. Because the CMO should always have the CRO’s back, because the CEO should delegate this important responsibility even if there is no natural owner, and because marketing is usually the majority pipeline contributor, I believe that the CMO should be the official owner of the overall pipeline. 

This means it’s the CMO’s job to ensure that sales starts every quarter with aggregate 3.0x pipeline coverage and, as a key part of that, to forecast next-quarter starting pipeline. That forecasting process should start early enough that you can still do something about forecasted problems, e.g., no later than one full quarter in advance. ”Doing something” might mean asking for more demandgen dollars or asking one of the other pipeline source owners (e.g., partners) to step up to higher targets. Worst case, it means escalating the forecasted and as-yet unresolved problem to the CEO.

The metric here is simple. The philosophy behind it is not [9].

We Generate Pipeline Efficiently

4. Demandgen cost per opportunity. Because we understand that SaaS companies effectively buy customers and that most SaaS companies require more than one year to recoup their investment in customer acquisition, we continually seek to reduce our demandgen cost per opportunity [10]. I pick demandgen cost per opportunity rather than overall marketing cost per opportunity because I want to put emphasis on the incremental (aka variable) cost of generating opportunities. If I want to measure overall marketing efficiency, I can use the marketing contribution to the CAC ratio. Here I want to focus on demandgen cost because it excludes fixed marketing costs that aren’t linked to generating opportunities (e.g., CMO salary, PR firm retainer) and because the primary business question I want to answer is: how much will it cost to generate 50 more of them? To do that, I don’t need to hire a second CMO or increase the PR retainer. 

If you take this approach, someone will eventually criticize you saying, “you’re deliberately understating the total cost of marketing-generated opportunities by including only demandgen costs,” to which you will reply: ”No, I am correctly stating the demandgen cost of opportunities because that’s the business question I’m trying to answer, and if you want to talk about overall marketing efficiency we can look at the CAC ratio and marketing’s contribution to it, including the sales/marketing expensive ratio.” [11]

We Get the Word Out

5. Awareness. Important as it is, demand generation is not the only thing we do here in marketing. We’re also responsible for getting the word out, making sure potential customers have heard of the company, have a positive opinion of us, and would consider us if and when they go shopping for a solution [12]. Towards that end, we run a number of programs to drive awareness/opinion/consideration in the market including public relations, brand advertising, and content marketing. Demandgen itself generates awareness as a by-product. 

To get an aggregate measure of these activities, we run a quarterly survey of buyers that measures:

  • Unaided awareness. Name vendors that come to mind in the XYZ space.
  • Aided awareness. Have you ever heard of vendor? [13]
  • Positive opinion. Do you have a positive opinion of vendor?
  • Consideration. Would you consider purchasing vendor?

While we’re happy to share the full report with anyone interested, in the QBR meeting we present aided awareness for ourselves and our top competitors.

6. Organic web traffic. The other way we measure general awareness is through organic web traffic, specifically how many unpaid visitors we get per month on the website. Are people finding us on the Internet and visiting our site? This is a coarse measure, but it allows us to keep an eye on how we are doing over time and relative to our competition [14]. 

We Care What Sales Thinks

7. Internal marketing CSAT. We view sales as our internal customer and our overall mission as to make sales easier. Towards that end, we run an internal customer satisfaction (CSAT) survey of sales each quarter and report back sales’ overall CSAT rating with marketing at the QBR. In order to inform our OKRs, we ask about many things (e.g., priorities, challenges) in this survey and the full report is available for anyone who attends the QBR.

I’ll conclude with a slide that summarizes this post.

# # #

Notes

[1] There has been some great content produced about this and in great detail of late — e.g., the Iconiq Go-To-Market Reporting Guide. While I’ve not yet reviewed it with a fine-tooth comb (because it’s both long and brand new), it looks quite good on my initial skim.

[2] Thus you end up using a placeholder value for new oppties which is effectively a proxy for counting them. If you create new oppties at zero value in such situations, I don’t pollute the pipeline with lots of proxy-valued oppties and, if I want to, I can always create “implied pipeline” by substituting 0 with the overall ASP or segment ASP. It’s impossible to do the reverse, because if your proxy value is $50K, you won’t know if a $50K oppty is a real value or a proxy value. 

[3] The other problem is that opportunity value is not single-valued but changes over time. So if you want to do pipeline metrics on value then you immediately beg the question: when? When the oppty was created? When it was 90 days old? When it hit stage 3? The world is much simpler if you just deal with counts for pipeline generation targets.

[4] Aka, sales-accepted opportunities. Generally in the sense that two keys have turned: an SDR thought it was an opportunity and passed it to sales, and a quota-carrying seller concurred.

[5] A favorite quote: ”I want salespeople who live in 90-day increments.”

[6] The simpler approach is to look at the TTM close rate of the year-ago cohort of new opportunities. The more complex approach is to look at the TTM close rate of all oppties generating in the past year, effectively stacking and sliding the progression (close rate vector) above. 

[7] At the 1Q24 QBR in January, I would say we generated 120 oppties in 1Q23 and 24 of them closed during 2023, for a 20% TTM close rate. (Unbeknownst to me at the time, 3 more will eventually close per the last column but that hasn’t happened yet. I could mention as an aside that 3 more are in the forecast for this quarter if I wanted to, assuming that none have close dates beyond that.)

[8] Such as stage 4 oppties or, while I don’t like demo as sales stage, oppties that reached demo. These are further down the pipeline than stage 2 oppties, but they are nevertheless still leading indicators and not sales. Because getting to these intermediate stages happens faster, the conversion rates are less lagged, but they are alas still leading indicators. I’ve talked to many CEOs who hooked everything to demo as a key stage, only to find that they were doing lots of demos, but not making many sales.

[9] People indoctrinated with a silo mentality may find it illogical or impossible to be accountable for something they don’t fully control. Think: how can I own the overall pipeline when I’m only responsible for generating 60% of it? I challenge such people to change their thinking to: I have two jobs. One is to generate 60% of the pipeline. The other is to make sure sales is teed up for success every quarter. I do that by forecasting starting pipeline coverage, leading a small team of leaders to decide what to do when we’re forecasting below target, and when needed escalate the problem early to the CEO.

[10] And the other way we try to reduce customer acquisition cost per dollar of ARR is to provide programs, tools, and training that increases the s2-to-close rate. We need to think of this as reducing demandgen cost per opportunity while holding quality constant (or improving it) where quality is measured by the s2-to-close rate.

[11] For die-hards, I’m often guilty of conflating incremental (i.e., marginal cost) with fixed vs. variable cost. The CMOs salary is a fixed cost. Demandgen is a variable cost in that it varies with volume. Total demandgen spend / total oppties generated = average cost per opportunity which is the actual calculation I’m encouraging. A true marginal cost would be the incremental cost of generating 1 more oppty, e.g., the cost of getting enough clicks to generate enough leads to generate a single opportunity. Here I think the average cost works fine and the real improvement is excluding the fixed costs that blur up the incremental cost of getting 10 or 50 more. But I’m sloppy in my language sometimes.

[12] And trying to accelerate that shopping trip is another thing we do in marketing, but the specific focus here.

[13] For most early- and growth-stage startups, <vendor> and <product> are synonymous. For bigger companies, you need to separate them. It’s not: have you heard of Salesforce? It’s: in the CX space, have you heard of Salesforce Experience Cloud?

[14] There is a nuance here but I do think companies should track this for both themselves and their competitors. The nuance is that for your own site, you can “know” how much traffic you get, but for the competition you can only “guess,” using tools like Ahrefs or SimilarWeb. The trick is when their guess for you is off, there can be a tendency to dismiss the competitor data as well. That’s a mistake. Present your own data for you over time (that you “know”) and then, when doing competitive analysis, compare the “guesses” using only the guess data, basically hoping for compensating and consistent errors in the process.

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Lessons from Playing with a Simple Quota Attainment Model

Posted on December 14, 2023 | Leave a comment

Quota attainment can get confusing quickly. It’s simple concept, but:

  • Do you mean percent of reps at 100% of their quota or above some lower bar? Board members bark rules like, “we should shoot for 80% of our reps at 100% of quota,” almost hearing the unspoken words, “because that’s what we did back in the day at GreatCo.” How much of that is nostalgia I’m not sure, but here in the real world, I almost never see 80% at 100% [1]. In fact, getting to 80% at 80% is actually quite an achievement. 
  • Do you mean on a quarterly or annual basis? The telltale of a dilettante is when asked they reply: ”uh, well, we need 80% at 100% on a quarterly basis.” In my enterprise B2B world, that literally never happens. Getting to 80% at 100% on an annual basis is nearly impossible. On a quarterly basis, it’s absolutely impossible. See this post, and the spreadsheet at the end of it, to demonstrate this point quite tangibly [2].
  • Do you mean productivity or quota? Quota is the target we assign to reps. Productivity is what we expect them to actually sell. Many people build a quota model and then subtract a cushion to get productivity. I prefer to build a productivity model [3] and then uplift to quota. Note that both the thickness and layer-by-layer allocation of that cushion is an important sales culture issue. But, back to our main point: when you say 80% or 100%, my question is: of what? [4]
  • Do you mean rep-by-rep achievement or overall realization of assigned quota? Most people mean rep-by-rep. But it’s also quite interesting to view realization as professional services teams do. If we model a consultant to bill 2,000 hours per year at a list price of $200/hour, they should bill $400,000 per year. If, due to beach time, discounts, and rework, they end up billing $300,000, we would say their realization is 75%. We realized only 75% of their theoretical billings. By analogy, we can say that if we assign $10M in street-level quota [5] and sell $7.5M that we realized 75% of our assigned quota.

In short, there’s enough potential for confusion here that I recommend three things:

  • Track percent of reps below 50%, above 80%, and above 100% of quota [6]. 
  • Track realization of overall, street-level quota.
  • Make the conversation concrete by building and playing with a simple quota attainment model [7].

I have embedded such a model below, and you can download it here.

Let’s quickly have some fun looking at the scenarios I created:

  • Scenario 1 realizes 100% of assigned quota and does that with three stars and one superstar. 20% of reps are less than 50% of quota [8]. Fun, but rarely happens.
  • Scenario 2 is what I view as realistic for a high-performing company. We made plan. We’ve got 80% of reps at 80%. Only 20% are below 50%. 30% at 100%, which might be a tad light for most sales managers. (That’s why I started making it green at 40%.)
  • Scenario 3 explores highly uneven achievement. We’re still making plan, but half of total sales are coming from one rep. 50% are below 50%. I lived a less dramatic version of this scenario and it’s unpleasant. The board will ignore that you’re making plan and complain endlessly about the unhealthy distribution of achievement [9].
  • Scenario 4 will never happen in real life but it shows you what happens when everyone is at 80%. The good news is you’re 80% at 80% and 0% below 50%. The bad news is you’re 0% at 100%. Some people wish for this, but I’m not sure they actually understand what they’re wishing for.
  • Scenario 5 is a more realistic version of scenario 3. Again we make plan and this time we get 30% at 100%. But save for one person at 90%, everyone else is in various degrees of trouble. 50% are below 50%. Half the salesforce is thinking of quitting.
  • Scenario 6 is an utter disaster, sadly not uncommon in early-stage startups where quotas are mis-set. Either we have hired 10 bad reps or we are setting quotas too high. Change the quota to $600K and watch everything change [10]. 
  • Scenario 7 demonstrates that you can’t actually have 80% at 100% without overperforming. Only rep 7 is over quota (and by only $200K) and the other $600K comes from the contributions of the stragglers. 

If you find yourself in conversations about attainment and things start to get confusing, I’d whip out a model like this and start playing with scenarios. You can download this sheet here.

# # #

Notes

[1] Nomenclature: X% at Y% means X% of reps at or above Y% of their quota.

[2] The title is about “proving a repeatable sales process” because a common use of attainment statistics is to prove sales model repeatability.

[3] When done my way (i.e., based on productivity) every number in the model (except the one row with quota) is a realistic take on what we expect to sell. The alternative is to have every number uplifted by 20-30% and then need to do constant mental discounts. That’s too much work that’s too easily forgotten. Start your model on Earth.

[4] Some companies run two layers of cushion: quota to productivity (to account for the fact that 100% of quota is rarely realized) and then productivity to plan (to add extra cushion to increase the odds of hitting plan).

[5] The sum of the quotas for each of the reps, forgetting management layers and cushions, which can complicate things endlessly.

[6] “At” here means “at or above.” I wanted to make many sentences less wordy.

[7] People aren’t great at statistics and distributions and often screw up even simple mental math. For example, if you have a 20% cushion between quota and productivity and you say we need 80% of reps at 100% of plan, then you are also saying that the plan is to beat plan. While that might sound like a great locker room speech, it’s bad analytics. If 80% are at 100%, you hit plan. Any over-performance (and there always is) by the hundred-plus percenters takes you above plan, and any contributions from the 20% of reps below quota take you beyond that.

[8] My intent at picking 50% is both that it’s an unacceptable performance and, while you should model it out for your company, they are likely unprofitable to carry, depending on cost of sales and marketing support resources. Reps at 80% aren’t achieving plan but they are usually squarely profitable.

[9] And they’re not wrong to do so, but well, you did make plan.

[10] Orange cells are drivers/input cells that you can type in. One only hopes their OTE is $150K so it’s inline with a 4x+ quota/OTE ratio and that they don’t require heavy support resources. Then, resetting the quotas might just be the solution.

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The Oft-Maligned Operating Partner and the Use of Tension Questions in Market Research

Posted on November 26, 2023 | 1 comment

This post is going to get a little weird because it’s going to do two things at once.

  • Discuss an interesting, if dated, survey [1] I found on the sometimes tense relationship between CEOs and PE operating partners (and other senior advisors like executives in residence) [2].
  • Demonstrate how it makes great use of tension questions to make the report more interesting and reveal the drama in what could be an otherwise dry subject.

The former should interest executives of VC/PE-backed companies who want to work better with their advisors and, of course, to such advisors themselves. The latter should interest all marketers, but particularly those responsible for the periodic PR market studies [2] that many companies produce (e.g., Collibra’s Data Intelligence Index, Atomico’s State of European Tech, or Pigment’s Office of the CFO Report).

I love these studies because you can get not a two-fer, but a three-fer, in terms of benefits:

  • Thought leadership, via leading discussion of emerging ideas (e.g., ask CFOs about their AI strategy)
  • Increased market awareness, via promotion of the survey report
  • Stronger positioning. For example, Collibra’s index supports their migration from data governance (their historical roots) to a broader and more modern positioning in data intelligence.

And that’s not to mention the MQLs if you use your report as gated asset. Or any proprietary insights you gather from questions where you don’t publish the answers. Goosebumps. I love these things.

This report starts with some gem quotes that cut to the heart of the problem:

Most CEOs have little/no prior experience with this type of relationship. At the extreme, there can be mistrust, miscommunication, competitiveness, and misalignment — all of which distract from the value creation agenda.

Friction between CEOs and operating partners might be an unavoidable human condition. This relationship is unlike any other in the business world. It would be interesting to ask CEOs to draw where the operating partner fits within the context of their organization chart. We all know they are likely to draw the Board above them and all employees below them. But where would they draw the operating partner …As a sub-component of the Board above them? As a peer? As an independent advisor working for them?

But the real strength of this report is its use of tension questions, where you ask two groups about the same issue and then spotlight tensions between them. We don’t have to go far to find one:

They asked both CEOs and operating partners about operating partner NPS. They then compared CEO actuals with operating partner expectations and, bang, right at the outset, we have a gap you could drive a truck through. 39% of CEOs are detractors whereas operating partners expected only 3% detractors. Operating partners think they have a respectable NPS of 41, whereas CEOs report a dismal, actual NPS of -3.

Conclusion: operating partners have no idea what CEOs think of them. That’s a tension question at work.

But we’ve only just started the bus. Let’s back it up over the operating partners by looking at value added.

About 70% of operating partners think they add “significant value” through their work, while only 20% of CEOs do. Zero percent of operating partners think they add only “little value,” but nearly 30% of CEOs do. Brutal as this survey is, they forgot a category that might have made it worse: negative value-add. The minimum value-add from a helper isn’t zero. It’s negative. Some would-be help actually slows you down.

Note that these tension questions are not manipulative or loaded. They’re fair questions that simply shine a bright light on an actual tension. That’s what makes them great.

Now, let’s twist the knife by looking at the cost/benefit of operating partners.

Around half of CEOs think that operating partners don’t bring enough value to offset their perceived cost.  Ten times more operating partners than CEOs think that operating partners bring 10x+ their cost in value.

I’m ready to re-title this report: The Blissful Ignorance of Operating Partners.

They then move on to open-ended questions and verbatim responses. These are an important part of all surveys, but particularly so with tension surveys. We’ve identifed one or more massive gaps. Now, what do we want to do about them?

Here, they ask the CEOs:

What one suggestion would you make to operating partners/senior advisors to help them be more effective in creating value for your business?

And we get some great answers:

Focus on building a relationship of trust with the CEO, not dispensing advice being the deal partner’s operations spy.

Prioritize what actually creates value and listen to what management wants your help with.

They should be a resource to the CEO, not the board.

As someone who works as an advisor, I’d note that you don’t always get a choice in deciding whether the CEO or the board is your customer. For example, in my work with Balderton, I position myself as “a free service brought to you by Balderton Capital,” hopefully clearly communicating that while Balderton is paying me, I am working for you. That said, Balderton is paying me, so if you tell me you plan to destroy the company I may need to mention that to them.

These relationships can be inherently complex. They are simpler on the control-oriented PE side [4], where it’s not, “I work for one of the many investors,” but instead, “I work for the owners.” If you want to eliminate this complexity entirely, you can hire advisors directly, which many early-stage VC companies do [5]. That said, VC/PE advisors are often high caliber, fully booked, and expensive, so working through your investors may be the only way to access the ones you want.

Back to the survey, they then took some verbatims from companies with a good relationships between the CEO and the operating partner. Quotes:

Trust is essential. There are no “go arounds,” no undermining the leadership team.

Operating partners and the CEO talk candidly about the rules of engagement and communication protocols — including what not to do.

Every conversation is confidential and to be kept between the CEO and the operating partner (not shared with the Board or the management team).

Finally, they give the operating partners a chance to tell their side of the working-better-together story. Quotes:

Work collaboratively with the operating partners. [Theres is] zero payback in defending that you are already doing it right.

That we are there to help create equity value, which is in all our interests, and therefore a partnership approach is key — on both sides.

Trust us to have the discretion to keep certain conversations and information privileged.

As an advisor, I think the last point is key and if a CEO is concerned, an explicit conversation can usually help.

As a marketer, I loved this survey. It picked a great topic and executed against it well, with some awesome tension question and well-chosen verbatims. I can only guess why they didn’t run it annually and I personally wish they did — half the fun with this type of survey is watching how things change over time.

# # #

Notes

[1] This thing is not easy to find online. You can find some old references to it, but Blue Ridge Partners seems to have archived it off their website. Perhaps they felt it was outdated, or maybe they stirred the pot too hard. Since there is no copyright notice of any kind in the report, I’ve uploaded it here (highlighting mine), so you can see it.

[2] It’s dated (2018) but my hunch is the core issues haven’t changed that much.

[3] I don’t know what else to call them. They are definitionally market research, but they aren’t run with the primary intent of learning more about the market. They are typically run by PR/comms for purely marketing reasons. New insights can be a by-product, but they’re not the primary point.

[4] The side of PE where they buy a controlling stake of the company and which, I believe, is the primary focus of this report.

[5] Often done with equity compensation via a YC FAST advisor agreement to simplify the process.

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Some Marketers Use Data to End Conversations; Others Use Data to Start Them

Posted on November 21, 2023 | 3 comments

Be the second kind.

The other day I was meeting with an advisory client [1], talking with the CMO and a handful of go-to-market team members. We started to discuss marketing, topics like product positioning and the website. So I asked for some feedback.

I displayed the company’s website alongside a competitor’s and asked, “which do you like better? And why?”

I asked for feedback on product positioning, too. Since the company works in a somewhat ill-defined category, it can credibly position either as an XYZ or a PDQ. I noted that the competitor chose PDQ while we had chosen XYZ, and again asked for feedback.

I knew darn well that the positioning had been extensively debated at the e-team and board level. I also knew the marketing team was strongly quantitative and did a lot of testing and measurement. But I just wanted to hear what the people had to say.

Because there were potential power distance issues [2], I wanted to make everyone feel more comfortable. So I said, “I’m just looking for your opinions, there are no wrong answers.”

Turns out there were.

The CMO jumped in explaining why, despite their initial feedback, our website was better and how everything had been tested and that opinions didn’t matter, only conversion rates did.

The CMO continued, explaining why XYZ was superior to PDQ, that we’d A/B tested both, and XYZ outperformed PDQ on conversions. Opinions didn’t matter, only conversion rates did.

Just in case a dying ember of life still burned in the conversation, the CMO snuffed it out by explaining that the homepage itself didn’t matter — and therefore really wasn’t worth talking about — because most of our traffic didn’t arrive on the homepage, but on scores of landing pages customized to specific paid or organic search terms.

Silence followed.

While I certainly flubbed the pre-meeting sync-up [3], this is an example of how some marketers use data to end conversations — when I think they should use data to start them.

Ending Conversations with Data

Killing conversations with data is easy. Use the data you have (ideally, that the audience has never seen) to tell them they’re wrong. We’ve tested this. We have the data. Trust the science. You are wrong. Case closed.

Once in a while, you do need to end conversations with data. For example, at the end of a long decision-making process where you have reviewed the data, had numerous conversations about it, and need to make a final decision. That’s fine. I’m not saying to never use data to end conversations.

What I’m saying is don’t use data to stifle a conversation. To cut one short. Or to avoid one entirely. Why do some marketers do this? It certainly varies by case, but I think some of the key reasons are:

  • They forget that sales is the customer. If you view someone as your customer, you should want to listen to them any chance you get. Any time. About anything [4].
  • They want to keep control. While boxing out people is a great short-term strategy to maintain control, it’s a great long-term strategy to find yourself needing new employment.
  • They don’t want their apple cart upset. Particularly towards the end of a major project, marketers often close their ears to feedback because they get more focused on project completion than on project success. They become unveilers.
  • They get offended. Don’t you think we tested this? Don’t you think we looked at the competitor’s positioning? Basically, don’t you think we know how to do our job? I get it. But marketing is not a sport for the thin-skinned.
  • You hit a nerve. Maybe there’s baggage attached to the issue, they’re having a bad day, or they’re just tired of debate. These aren’t valid reasons to shut down conversations, but we’re all human. Marketers need to learn to manage these feelings. See note [5] for how I learned this.

I follow two principles that help me avoid these problems.

  • Always be curious. My curiosity about their opinions must trump any potential sting in their response. If forced to choose between ignorance and hurt feelings, I’ll take the hurt feelings every time.
  • Defensiveness kills communication. I know of no better way to stop all communication than to interrupt someone providing feedback with a defensive explanation. When you’re talking, you’re not listening.

Starting Conversations with Data

I like to start conversations with data. For example, on the XYZ vs. PDQ positioning question, you can run a few focus groups to discuss it [6]. You can do some market research, such as surveys [7]. You can add some keyword research. And a summary of how industry analysts and competitors position the space. Then you package that up into a short summary presentation [8] and run a series of internal meetings where you tee up discussions with the data — with both the groups you must meet (e.g., the exec staff) and with anyone willing to make the time and effort (e.g., town halls).

You’re not keeping the data under your cloak and using it as a secret weapon to silence opposition. You’re gathering the information you can afford to gather, packaging it up nicely, and having a series of open discussions about it.

That’s the way to start conversations with data. And people will love it when you do.

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Notes

[1] While my desire to tell a given story in Kellblog is sometimes triggered by a single event, in order to preserve anonymity, be able to speak more generally, and spice-up things, I quickly adapt and meld such stories with dozens of others I’ve experienced over the years. Readers sometimes tell me, “I think I was in that meeting” — and they might well have been — but please don’t be surprised if the tale I tell is not a precise recounting. My goal is not to precisely describe a single experience, but instead to take lessons from the sum-total of them.

[2] A senior advisor and a CMO have quite a bit more power in an organization than a CSM or a seller. Thus, communication transparency can suffer. How much it suffers is a function of both organizational and national culture.

[3] And I’ll own that. My bad. Improvisation, as they say, only looks easy. Good improvisation usually happens among people who play together often and have a shared understanding of the underlying context and structure. Here, I tried to improvise a feedback exercise with someone with whom I rarely play, and we ended up stepping all over each other.

[4] I say this because some people only want feedback when they’re ready for it. Think: this is not a good time in our product lifeycle or campaign development cycle. Or, I can only accept feedback right now through this channel. When it comes to customers and feedback, it should be: at any time in any place. How you action it may vary based on where you are in a lifecycle, but listen first and explain those constraints later.

[5] I once had the good fortune of starting a marketing job on the day of a QBR where I got to watch my predecessor present the marketing update to the sales leadership. The whole thing got defensive very fast, the marketer bobbing and weaving, ducking blows, and having a few deer-headlight moments. I still remember the meeting and thinking one thing to myself: I never want to be that person. (Or more specifically, since they were a fine person, I never want to be in that place, in that situation.)

[6] While you can spend a lot of money on this, you can also spend a little or even none. For example, calling a couple of Zoom meetings to discuss things with trusted customers and prospects.

[7] Again, you can spend $25K to $50K on a study or you can make your own survey and mail it out. I’m not saying the data you get will be scientifically valid, but that’s not the point here. We’re not trying to prove anything with the data or make a decision on it alone. We’re trying to bring data to the conversation so we can have a better one.

[8] Spending 10 to 15 minutes of a 60 to 90-minute session teeing up the discussion, and the rest of it asking a few well-crafted questions and listening to the answers.

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