Appearance on the Metrics That Measure Up Podcast

“Measure or measure not.  There is no try.”

— My response to being called the Yoda of SaaS metrics.

Just a quick post to highlight my recent appearance on the Metrics That Measure Up podcast, hosted by Ray Rike, founder and CEO of RevOps^2, a firm focused on SaaS metrics and benchmarking.

Ray’s a great guy, passionate about metrics, unafraid of diving into the details, and the producer of a great metrics-focused podcast that has featured many quality guests including Bryon Deeter, Tom Reilly, David Appel, Elay Cohen, Mark Petruzzi / Paul Melchiorre, Sally Duby, Amy Volas, and M.R. Rangaswami.

In the episode, Ray and I discuss:

  • Top SaaS metrics — e.g., annual recurring revenue (ARR), ARR growth, net dollar retention (NDR), net promoter score (NPS), employee NPS, and customer acquisition cost (CAC) ratio
  • How metrics vary with scale
  • Avoiding survivor bias, both in calculating churn rates and in comparisons to public comparison benchmarks (comps) [1]
  • How different metrics impact the enterprise value to revenue (EV/R) multiple — and a quick place to examine those correlations (i.e., the Meritech comps microsite).
  • Win rates and milestone vs. cohort analysis
  • Segmenting metrics, such as CAC and LTV/CAC, and looking at sales CAC vs. marketing CAC.
  • Blind adherence to metrics and benchmarks
  • Consumption-based pricing (aka, usage-based pricing)
  • Career advice for would-be founders

If you enjoy this episode I’m sure you’ll enjoy Ray’s whole podcast, which you can find here.

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Notes

[1] Perhaps more availability bias (or, as Ray calls it, selection bias) than survivor bias, but either way, a bias to understand.

Navel Gazing, Market Research, and the Hypothesis File

Ask most startups about their go-to-market (GTM) these days and they’ll give you lots of numbers.  Funnel metrics.  MQLs, SQLs, demos, and associated funnel conversion rates.  Seen over time, cut by segment.  Win/loss rates and close rates as well, similarly sliced.  Maybe an ABM scorecard, if applicable.

Or maybe more financial metrics like customer acquisition cost (CAC) ratio, lifetime value (LTV) or net dollar retention (NDR) rate.  Maybe a Rule of 40 score to show how they’re balancing growth and profitability.

And then you’ll have a growth strategy conversation and you’ll hear things like:

  • People don’t know who we are
  • But the people who know us love us
  • We’re just not seeing enough deals
  • Actually, we are seeing enough deals, but we’re not making the short list enough
  • Or, we’re making the short list enough, but not winning enough.

And there are always reasons offered:

  • We’re not showing enough value
  • We’re not speaking to the economic buyer
  • We’re a vitamin, not a pain killer
  • We’re not aligned with their business priorities
  • People don’t know you can solve problem X with our solution
  • Prospects can’t see any differentiation among the offerings; we all sound the same [3]
  • They don’t see us as a leader
  • They don’t know they need one
  • They know they need one but need to finish higher priorities first

It’s an odd situation.  We are literally drowning in funnel data, but when it comes to actually understanding what’s happening, we know almost nothing.  Every one of the above explanatory assertions are assumptions.   They’re aggregated anecdotes [4].  The CRM system can tell us a lot about what happens to prospects once they’re in our funnel, but

  1. We’re navel gazing.  We’re only looking at that portion of the market we engaged with.  It’s humbling to take those assertions and mentally preface them with:  “In that slice of the market who found us and engaged with us, we see XYZ.”  We’re assuming our slice is representative.  If you’re a early-stage or mid-stage startup, there’s no reason to assume that.  It’s probably not.
  2. Quantitative funnel analysis is far better at telling you what happened than why it happened.  If only 8% of our stage 2 opportunities close within 6 quarters, well, that’s a fact [5].  But companies don’t even attempt to address most of the above explanatory assertions in their CRM, and even those times when they do (e.g., reason codes for lost deals), the data is, in my experience, usually junk [6].  And even on the rare occasion when it’s not junk, it’s still the salesrep’s opinion as to what happened and the salesrep is not exactly an unbiased observer [7].

What’s the fix here?  We need to go old school.  Let’s complement that wonderful data we have from the CRM with custom market research, that costs maybe $30K to $50K, and that we run maybe 1-2x/year and ideally right before our strategic planning process starts [8].  Better yet, as we go about our business, every time someone says something that sounds like a fact but is really an assumption, let’s put it into a “hypothesis file” that becomes a list of a questions that we want answered headed into our strategic and growth planning.

After all, market research can tell us:

  • If people are aware of us, but perhaps don’t pick us for the long list because they have a negative opinion of us
  • How many deals are happening per quarter and what percent of those deals we are in
  • Who the economic buyer is and ergo if we are speaking to them
  • What the economic buyer’s priorities are and if we are aligning to them
  • When features are most important to customers shopping in the category
  • What problems-to-be-solved (or use-cases) they associate with the category
  • Perceived differences among offerings in the category
  • Satisfaction with various offerings with the category
  • If and when they intend to purchase in the category
  • And much more

Net — I think companies should:

  • Keep instilling rigor and discipline around their pipeline and funnel
  • Complement that information with custom market research, run maybe 1-2x/year
  • Drive that research from a list of questions, captured as they appear in real time and prompted by observing that many of these assertions are hypotheses, not facts — and that we can and should test them with market research.

 

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Notes

[1] As many people use “demo” as a sales process stage.  Not one I’m particularly fond of [2], I might add, but I do see a lot of companies using demo as an intermediate checkpoint between sales-accepted opportunity and closed deal — e.g., “our demo-to-close rate is X%”

[2] I’m not fond of using demo as a stage for two reasons:  it’s vendor-out, not customer-in and it assumes demo (or worse yet, a labor-intensive custom demo) is what’s required as proof for the customer when many alternatives may be what they want — e.g., a deep dive, customer references, etc.  The stage, looking outside-in, is typically where the customer is trying to answer either (a) can this solve my problem or (b) of those that can solve my problem is this the one I want to use?

[3] This is likely true, by the way.  In most markets, the products effectively all look the same to the buyer!  Marketing tries to accentuate differentiation and sales tries to make that accentuated differentiation relevant to the problem at hand, but my guess is more often than not product differentiation is the explanation for the selection, but not the actual driver — which might rather be things like safety / mistake aversion, desire to work with a particular vendor / relationship, word of mouth recommendations, belief that success is more likely with vendor X than vendor Y even if vendor X may (perhaps, for now) have an inferior product)

[4] As the saying goes, the plural of anecdote is not data.

[5] And a potentially meaningless one if you don’t have good discipline around stages and pipeline.

[6] I don’t want to be defeatist here, but most startups barely have their act together on defining and enforcing / scrubbing basics like stages and close dates.  Few have well thought-out reason codes.

[7] If one is the loneliest number, salespersonship is the loneliest loss reason code.

[8] The biggest overlooked secret in making market research relevant to your organization — by acting on it — is strategically timing its arrival.  For example, win/loss reports that arrive just in time for a QBR are way more relevant than those that arrive off-operational-cycle.

A Ten-Point Sales Management Framework for Enterprise SaaS Startups

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Examples:

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

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

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

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

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

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

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

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

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

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

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

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

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

ABM is Not B2B and Other Rants on Account-Based Marketing

The other day I read a book  on account-based marketing (ABM), entitled ABM is B2B, and I must say I disliked it.  Capital D disliked.

My Thoughts on the Book:  ABM is B2B
Why?  It struck me as the kind of buzzword-laden, hype-filled, superficial-case-study-driven, strawman-arguing, compound-adjective-using [1] marketing book that seems to deliberately complexify marketing, perhaps in an attempt – as marketers sometimes do – to perpetuate the idea that marketing is a dark art best left to wizards and gurus and basically everyone else should, well, GTFO and leave us alone as we blaze the trail to ABM in the name of alignment.

A marketing book written in marketing copy style.  For marketers.   Sentence fragments.  Big claims.  Lots of benefits.  Testimonials (-ish).  Few features.  Only after finishing the book, did it really sink in that the book was a sequel [2].  Perhaps that was the problem.  I’ll never know.

I must confess the book irked me before I could open the cover.  Let me get this off my chest:  ABM is not B2B.  ABM means account-based marketing.  B2B means business-to-business [3].  They’re not the same.  QED.  Thanks, I’m here all week.

Perhaps “ABM is B2B” is an attempt to generate a pithy metaphor like “the medium is the message” (McLuhan),  “chaos is a friend of mine“ (Dylan), or “advertising is the rattling of a stick inside a swill bucket” (Orwell).  But it doesn’t even work as a metaphor.  It’s like saying “fruit are apples.”  No.  Apples are fruit.  Just as ABM is one type of B2B marketing.

But when the authors are cofounders of an ABM company, I suppose every marketing problem looks like an ABM problem [4].  When your only tool’s a hammer, every problem looks like a nail.

I was also irked at the outset because — let’s give credit where credit’s due — ABM itself has been so effectively marketed at the C, VC, and board levels.  It reminds me of a cartoon from long ago where an executive is talking to their assistant:

“It’s clear that everyone needs a relational database.  Please go find out what a relational database is.”

Many of the CEOs I work with are in the same situation.  The board knows they need ABM.  They know they need ABM.  But no one’s quite sure what ABM is, and nobody wants to admit it. Hey, I’m a former billion-dollar company CMO and I’m not sure.  So I bought the book to help.  It didn’t.

Let’s have a taste:

And now, with new data, surveys, and customer stories we’ve witnessed, we can see what’s possible when we look at ABM as B2B. In turn, companies that are finding their way and jumping in with their own programs finally can plot where they are on their ABM journey. We call it the B2B Maturity Curve. The curve is simple, yet dynamic. Ask any company if they would prefer to be average or great with their marketing, and you know what they’d say. But they’re not all where they want to be yet. Within this curve is a roadmap outlining the movement from status quo to B2B 2.0 across every key component of ABM marketing, sales, and customer success, making it abundantly clear that most organizations haven’t reached full maturity.

Does that actually say anything?

Nevertheless, there are a lot concepts, quotes, and ideas that I like within the book.  A few examples:

  • The value of marketing is defined by sales.  I wouldn’t say it quite that way, but yes.
  • Some accounts deserve champagne, others sparkling water.  Yes.  As a matter of both company and go-to-market (GTM) strategy, we need to segment the market and then target certain segments [5].  Champagne, to me, is usually part of a long-term, slow nurture program.
  • Your silos should burn to the ground.  I don’t like the passive voice, but yes, sales, marketing, and customer success should all work together closely.  You should burn your silos down.
  • Counting leads for leads’ sake (a so-called vanity metric) is stupid.  Yes.  But only stupid marketers did it.  Strawman. [6].

The book is like a stew made with tasty ingredients that don’t come together into a dish.  Overall, I have three issues with the book:

  • ABM is not B2B. ABM is ABM, one type of B2B marketing appropriate in some situations as a function of company and sales strategy.  This blows the book up on the launchpad for me.
  • Marketing can’t be more aligned to ABM than sales. You’re either aligned to sales or you’re not.  If sales is all-in on ABM, great.  If sales is not, then marketing can’t be all-in — and still be aligned with sales.  If forced to choose among alignment targets, marketing should pick sales every time.  Not ABM idol worship.  Another showstopper.
  • It borders on extremism. The book sometimes preaches what I might call fundamentalist ABM [7] – e.g., MQLs are bad, you should get rid of them as a concept and never think about them again.  No, they aren’t.  When ignorant marketers celebrate MQL volume without caring about conversion, that’s bad.  But you don’t need ABM to fix that; you can do so in other ways.

Enough about the book.  Let’s talk about ABM.  Or should I say B2B?  (I’m so confused.)

My Thoughts on Account-Based Marketing
Here’s my favorite quote on ABM, from a CRO friend:

“If what you mean by ABM is that we should start picking our customers instead of them picking us, then I am in favor.”

Here’s what I often see going wrong with ABM in startups:

  • The board wants it because they want to be helpful, even if they’re not quite sure what it is.
  • The CEO wants it because, well, the board wants it and more focus sounds like a good idea.
  • The CRO doesn’t really want it (think:  don’t fence me in) but grew up in sales and is savvy enough to nod their head in all the right places during discussions.
  • The CMO wants it because the CEO does, it’s a cool marketing buzzword, and a good thing to have on the resume.

What gets implemented is a hybrid where every department does a little ABM.  Salesops whips up an ideal customer profile (ICP), usually not terribly mathematically [8], and a target account list that’s often way too long.  Marketing does some ABM-style programs, such as selective website customization, personalized direct mail, and ad retargeting.  SDRs perform target account research and account-focused outbound.  Sales assigns “focus accounts,” perhaps 10 to 30 per salesrep, so each rep has both a territory and a list of of focus accounts.

What happens?  Everybody gets a little taste of ABM and not much changes.  Those focus accounts?  Well, if my territory is New Jersey plus 20 focus accounts, while my manager might bug me once in a while for account plans, if the territory is producing inbound leads and I’m hitting my numbers, well those focus accounts aren’t going to get much focus.

In fact, only when the territory isn’t producing leads will the focus accounts get focus.  How?  When the rep complains to the CRO in a forecast review about in-bound lead volume, the CRO gets to say:  “you’re on the hook for generating 20% of your pipeline so get on phone and bang away on those focus accounts.”  It’s a built-in protection system for the CRO who, per Kellogg’s first rule of sales management [9], knows they need one.

But are we really picking our customers?  When I ran MarkLogic (where we had only about 30 reps) we had one rep whose territory was one account (NSA).  On my first customer visit at Salesforce, I visited an account (Qualcomm) that was also the rep’s only account.  That’s focus.  New Jersey plus 20 “focus” accounts?  Not so much.  There’s tipping your hat to the ABM gods as a demonstration of political astuteness and then there’s actually picking your customers.

This is a high-class problem.  In good markets you can build to $10M, $50M, $100M or more in a purely horizontal way, riding the back of a new, general-interest category. In fact, if you’re riding your way to $500M right now on the back of hot category, there’s a strong argument you don’t need ABM yet and you might grow faster without it.  ABM is not a virtue unto itself.  It’s just another way to grow revenue.

In bad markets you can’t even get to $10M on the back of a hot category (because definitionally, there isn’t one).  This focuses you solving specific problems for customers, usually in specific industries.  ABM comes naturally in these situations as you are unknowingly already executing it as a company-level strategy.  ABM might tighten your focus (e.g., on key accounts within the vertical) and your execution (e.g., integrated cross-channel campaigns).

The second reason companies execute focus strategies early in their evolution is product completion.  If your enterprise software product is MVP-level, and you have four huge customers — a bank, a pharma, a megatech, and a government agency — you are likely to get figuratively drawn-and-quartered by your customers as each pulls you in a different product requirements direction.  A more strategic, chasm crossing approach would be to focus on one of those industries as a beachhead, accumulating customers with more homogenous requirements, and then expanding into adjacent markets via a bowling alley strategy.

At some point, though, most companies — even those who grew up in hot markets — decide that they can grow faster and do bigger deals with an account-focused strategy.  Usually this is done in conjunction with building a channel strategy and rolls out as something like:  we’re going to focus our enterprise direct sales force on accounts bigger than $2B and give the rest to channels [10].  As part of that, we’re going to focus each enterprise rep on somewhere between 5 and 30 accounts.  No territory plus focus accounts.  Just 5 to 30 accounts as your territory, period.

After focus, the thing I like best about ABM is its in-built reality check.  Traditional marketing funnel metrics (e.g.,. MQL to SAL conversion rates) are typically not cohort-based and, more importantly, assume a linear sales flow that simply isn’t the reality in enterprise software.  As I’ve often said in meetings:

People, we’re not selling toothbrushes here.  There’s no simple linear flow from ad-click to landing-page to trial to purchase [11].  These are complex transactions that happen over the course of months involving multiple constituents in different roles (e.g., user, business buyer, approver).  The full cycle is typically measured in quarters, engaged contacts in dozens, and touches by the score.

I love ABM because it constantly reminds you of that truth, to step back and look bigger picture at the engagement progress across target accounts and not just at MQLs and SALs.  That is, however, not to say that we shouldn’t look at MQLs and SALs; we just need to do so intelligently.  Moreover, at every ops review, we should have a slide [13] that looks back at recently closed deals and remind ourselves how much time, how many people, and many touches were involved in closing that deal.

I’ll summarize my views on ABM here:

  • Many startups get pushed to do ABM too early for the wrong reasons.  If you don’t feel the need to do ABM, I’d argue that you shouldn’t.
  • ABM is, however, a muscle that develops slowly so you should probably start your ABM program about 2 years before you think you’ll need  it.
  • The best way to start at ABM is not by asking everyone to do a little, but by asking a select few to do a lot.  Create a small dedicated strategic accounts team focused on the customers you want to pick and consisting of, e.g., 3 salesreps, 2 sales consultants, 2 SDRs, 2-3 CSMs, and one marketer.  Measure that team not by your regular functional, funnel metrics but first by ARR [13] and then by an ABM scorecard.  If that doesn’t work, fix it.  If it does work, expand it.

In the end, traditional marketing is hanging a sign to attract customers; ABM is stalking customers. (Think:  you don’t know it, but we’re your destiny.)

ABM shouldn’t be conceptualized as account-based marketing.  It’s account-based everything.  Or, better put, account-based go-to-market.  There.  ABM should be ABGTM.

Someone should write a book on that.

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Notes

[1] The attempted humor here is to accuse the book of abusing compound adjectives while simultaneously abusing compound adjectives.  (Think: “I unequivocally deplore people who use highfalutin language.”)

[2] Despite many embedded references to their first book, which I disregarded as cross-sell attempts rather than saw as harbingers of possible sequel disease — where the authors have already said what they wanted to say and are effectively just saying it again, fancier, two-dot-oh-ier.

[3] The old term for B2B marketing was industrial marketing, to separate marketing to businesses from marketing to consumers.

[4] The authors Sangram Vajre and Eric Spett are the co-founders of Terminus, an ABM (their title tag says ABM, not B2B!) marketing software company that has raised over $120M in VC to date.

[5]  In ancient times we were taught the acronym STP:  segment, target, position.  I still like it.

[6] This is one of several strawman arguments in the book.  Long, long ago competent marketers stopped celebrating leads for leads’ sake.  (Apologies for not gender-neutralizing strawman, but I think strawperson doesn’t work.  Ideas appreciated.)

[7] The first time I saw methodology fundamentalism was with Solution Selling.  The book preached that actively evaluating prospects were likely agenda-biased by another vendor and ergo that finding upstream prospects (in pain, but before starting evaluation) produced better leads.  While I get the concept (and it’s an interesting one), I never took it literally — but our salesops people did.  In our initial implementation they actually scored prospects with active evaluations who met BANT criteria as the lowest quality leads!  (Think:  “oh, they’re evaluating, don’t call them back.  Already gone!”)  That’s methodology fundamentalism triumphing at the expense of common sense.

[8] For an early-stage company an ideal customer profile (ICP) must be aspirational.  For a growth-stage company it should be the result of a regression:  identify customers who look like our successful customers.  Which begs interesting questions (that I need to blog on later) about what “look like” and “successful” mean.

[9] Sales managers are some of the biggest hard-asses on the planet because they spend their careers managing salespeople, some of the most demanding employees on the planet.

[10] You might tier your sales structure here as well.  For example, $2B+ accounts to a field-based enterprise team, $1B to $2B to a hub-based, mid-market team, and <$1B to channels.

[11] You can view Quip’s 30 day no-questions-asked return policy as an in-built trial.

[12] Based on a detailed quarterly study of a handful of representative accounts, perhaps by segment.

[13] While it won’t happen immediately, let’s not forget the goal isn’t “to do ABM” for ABM’s sake, but to generate higher sales productivity.  If, over time, we don’t see that — well, why are we doing this again?

The Product Superpowers That Few Flex: Join Special Guest Brett Queener on the SaaS Product Power Breakfast

Please join Thomas Otter and me this Thursday, May 6th at 8:00 am Pacific for the SaaS Product Power Breakfast on Clubhouse with special guest Brett Queener, partner at Bonfire Ventures, former President & COO at SmartRecruiters, product-line general manager at both Salesforce.com and Siebel, and member of the board of directors at Aforza, Atrium, ClearedIn, Cube, Invoca, Lytics, Pendo, SmartRecruiters, and Spekit.

Our topic will be The Product Superpowers That Few Flex:  Intention and Conviction.

We aim to cover the following questions:

  • What was it like running product for Marc Benioff?  (Or, for that matter, Tom Siebel?)
  • What do you look for when evaluating products for seed-stage investments?
  • Cadence:  daily / monthly / quarterly releases — which is best and why?
  • What in your mind is a world-class product manager?
  • How is the role of product decisioning changing?
  • In a product-led growth (PLG) world, does product own growth?
  • What’s a feature and not a company?

With Brett, the action is sure to be cutting, frank, insightful, fast-paced — and funny.  Content warning:  when Brett and I get together, the errant F-bomb has been known to drop, so this may be our first R-rated episode.

Bring a friend — it should be a crackling session.  If you need a Clubhouse invite, ask.  And for those who can’t make it live, the SaaS Product Power Breakfast is now available in podcast form, so it will be recorded and you can always listen to it later.

See you there!