The Quantity Delusion: Why RevOps Scaled Math While Sellers (and Buyers) Collapsed

over engineered & misunderstood

RevOps is another idea that has acquired almost religious status – everyone violently agrees with it, not withstanding that very few can give you a coherent definition (accepting that RevOps means different things to different people and different companies!)

RevOps even works. But here’s the catch - only for companies with thousands of reps and millions of clean data points. The rest of the market tried to copy them and ended up with pretty dashboards built on unreliable CRM fields, flawed attribution, and false forecasting. It wasn’t RevOps that failed, it’s the fantasy that RevOps scales downward. It doesn’t. We’re mistaking enterprise infrastructure for universal truth, and if you step back from the dashboards long enough to breathe, one thing becomes painfully obvious:

Modern B2B didn’t get better — it just got louder.

The great RevOps promise was elegant on paper: unified revenue motion, data-driven clarity, predictable pipeline, scalability. But like most management fashions exported from MBA’s and Management Guru thinking it accidentally evolved into a different religion:

Quantity = Safety. Instrumentation = Intelligence. Dashboards = Truth.

Most companies just don’t have the data volume, CRM hygiene, or behavioral consistency to make RevOps math mean anything beyond “look, colored bars going up.”

The Myth of Predictable Revenue (for 99% of companies)

Here’s the quiet thing no one wants to say too loud:

The reason RevOps works (sort of) at Salesforce or AWS is because they have millions of interactions, thousands of reps, structured quoting, and a decade of historical patterns. Big companies like this can influence (nee dictate) how prospects and customers communicate and interact with them. For example it’s why a sales method like MEDDICC works for them - but very few others!

The rest of the market has:

• less sellers, but with more diverse personalities

• spotty CRM notes entered at 4:53 p.m. on a Friday

• one messy ICP

• three broken lead scoring rules

• and a pipeline full of optimism masquerading as truth

  
  

You can’t turn that into a predictive model no matter how many dashboards you stack. Predictive models that support solid conclusions need statistical validity and discipline. RevOps multiplied the wrong variable: inputs without comprehension.

We scaled activity, not relevance; automation, not preparation. The spreadsheets improved. The outcomes haven’t, in fact in many cases they’ve worsened.

The Collapse of the Numbers-First Mindset

We reduced sales and marketing (GTM) to an engineering problem, dismissing the fact that we’re dealing with human beings here. You know those things that are predictably unpredictable! We asked sellers to behave like algorithmic throughput instead of humans who influence humans.

The results are everywhere:

• response rates at 1% and falling

• conversion rates eroding quarter over quarter

• CAC rising faster than revenue growth

• inflated pipelines no one believes

• forecasts rewritten three times before the board meeting

• SDR burnout at crisis levels

• buyers increasingly allergic to outreach

The irony is tragic: We engineered precision around the one thing we never fixed: capability.

Dashboards measured. Cadences fired. Sequences sequenced – and a Partridge in a Pear Tree And meanwhile? Actual buying conversations got worse, not better.

When Math Becomes a Crutch

RevOps was meant to help leadership see the truth. It accidentally gave them a shield to avoid it.

• “The dashboard says it’s fine”

• “The funnel says marketing is performing”

• “The attribution model says the spend is justified”

But numbers describe conditions — not meaning. Pipeline volume is not pipeline intent. Stage progression is not understanding. Forecast confidence is not buyer confidence.

The machine became the focus, not the mission. Which is why effectiveness tools — the ones centered on:

• practice

• readiness

• qualification

• message clarity

• behavioral context

• strategic relevance

…are no longer “nice to have.”

They are the counterculture movement. Not against tech — but against empty math.

Quality vs Quantity: The Budget Reckoning

We have reached a moment where companies can’t have both:

• the giant RevOps tech spine, and

• the investment in actual seller capability

Budgets are finite. Leadership has to pick a philosophy, not just a platform.

Do you optimize volume, or do you improve the people doing the selling?

One says: “If we just send more, dial more, log more — growth will follow.”

The other says: “If we teach people to be relevant, credible, and prepared — we’ll convert more and growth will follow.”

They sound related. They are not.

One is diagnostic. The other is intervention.

RevOps describes the storm. Readiness builds the roof.

“But RevOps is working — look how many companies use it!”

Everyone says this. Sure — and almost all of them are missing targets, burning out teams, drowning in automation residue, and waiting until they can’t maintain the lie and they lose their job. It’s not “if” it’s “when” and that’s no way to live!

Mass adoption ≠ mass effectiveness. Six Sigma was everywhere too — until it wasn’t. Lean, TQM, Agile… all had a moment of ubiquity before quietly evolving into something no one would admit was different.

No movement with enterprise adoption ever dies. It just rebrands.

Ideas That Didn’t Die, They Just Mutated

Original Hype	What It Quietly Became
Six Sigma (manufacturing-precision religion)	Continuous Improvement (minus the militant belts & jargon)
TQM (Total Quality Management)	ISO / QA frameworks (same soul, fewer posters)
Agile (post-Waterfall revolution)	Scaled Agile / Product Ops (i.e. Agile, but corporately house-trained)

RevOps will follow the same arc. There will be no mass apology, no LinkedIn confessional.

It will just morph into:

• “Revenue Effectiveness”

• “Go-to-Market Intelligence”

• “Unified Buyer Operations”

• or whatever consultancies decide sounds plausible next.

The math will stay. The slogan will change. The human element will (finally) have to return.

Where We Go from Here

The coming era is simple:

Better sellers, not bigger pipelines.

Sales that sounds like humans, not automation residue. Marketing that communicates meaning, not mail merges.AI that augments capability, not just generates dashboards. Technology that improves the act of selling, not just the act of logging. Quantity will always be easier to measure. Quality will always be harder to deliver. But when the noise reaches saturation —when conversion rates hit statistical irrelevance, when RevOps can no longer inflate reality, the market will remember what actually moves deals:

Humans who know how to sell. To think. To prepare. To matter.

The future isn’t anti-tech. It’s anti-thoughtless tech.

• Not less AI. Smarter AI.

• Not less data. More meaningful (and well considered) data.

• Not fewer dashboards. Fewer illusions.

  
  

The correction is underway. Quietly. Inevitably. And when it lands, effectiveness won’t be the counterculture anymore —it will be the center.

QUALITY vs QUANTITY: The New Battleground in B2B Sales & Marketing

Twenty ('ish) years ago B2B sales and marketing began a journey that has  trapped it inside a numbers-first machine. Pipelines swelled, dashboards multiplied, and companies celebrated “activity” as if volume alone produced revenue. The playbook became: blast more emails, run more sequences, generate more MQLs, make more calls, hire more SDRs, stand up more RevOps dashboards, and assume that scale equaled success.

But is the market changing? Will  companies wake up to the uncomfortable truth that the entire quantity-driven model is a fraud? Response rates are collapsing, CAC is rising, data quality isn’t just deteriorating but is at an all-time low, and sales conversations have never been weaker, more scripted, or more instantly forgettable. Do leaders know this, and if they do, do they care to admit it? Their dashboards reflect it, even if those dashboards can’t explain why it’s happening. Will quality vs. quantity become the new strategic battleground.

The Quantity Era: How We Got Here

The obsession with volume didn’t happen by accident — it was engineered. Everyone became enamored with technology Cinderella stories of vast fortunes and fame. Many said “we want that”.  In order to grow fast and furiously, technology (specifically SaaS) business models demanded predictability and scale, so companies built go-to-market engines believing “everything” could be measured (not withstanding that everything “can’t” be measured), automated, and repeated. CRMs grew bloated with fields that were outdated the moment they were entered. Marketing automation platforms turned personalization into a mail merge. Sales engagement tools encouraged reps to send 500 messages they didn’t understand rather than five that mattered.

Much of this was justified as “data-driven decision making.” Academic thinking, drilled into all those MBAs. But most companies lacked the one thing data-driven models genuinely require: enough real data. A handful of partial CRM records and scattered email touches are not enough to predict buyer intent, deal probability, or message effectiveness. Yet the dashboards kept getting prettier, and the addiction to activity kept getting stronger.

The quantity engine created motion, but not momentum. And now, in a market where buyers have endless information, limited attention, and shrinking patience, motion without meaning is a dead end.

Is there a Quality Revival: Why Will Effectiveness Is Become the Only Advantage That Matters

The market is shifting toward a new truth: companies don’t need more touches, more sequences, or more dashboards — they need better ones. They need sellers who show up prepared, marketers who understand relevance, and customer-facing teams who can think, adapt, and create insight in real time.

This is why a new class of AI applications is emerging — not the volume machines of the last decade, but tools built around effectiveness. These are AI systems that improve human performance rather than replace judgment; systems that help sellers practice, rehearse, strategize, and tailor their approach; systems that help marketers craft relevance, not just scale outputs.

They don’t add more noise, they reduce it. They don’t automate spam - they elevate quality. And they don’t depend on perfect CRM data, because they operate on thinking, preparation, and context — things humans control.

In other words, they finally address the real bottleneck: the quality of the conversations that shape the pipeline, not the quantity of the activities logged inside it.

Why Quality Is Becoming a Competitive Advantage

Three big forces are accelerating this shift.

• First, every market is saturated with content, outreach, and automation. Buyers aren’t responding to more — they’re filtering it out, and worse ignoring it (and AI can help with that as well!). The companies breaking through are the ones demonstrating relevance and insight, not volume.

• Second any companies outside the biggest of the big don’t have the data density required to make RevOps forecasting and analytics meaningful. If a company has 10 reps and 40 live deals, no algorithm on earth can extract reliable patterns. Statistical validity demands a critical mass of data, and 95% of companies have neither the quantity or quality of that data. You can prettify the dashboard, but you can't conjure signal where none exists.

  
  

• Third, AI has reached the point where it can meaningfully improve human performance — helping salespeople think through strategy, helping marketers sharpen messaging, helping reps rehearse and internalize better engagements. Quality can now be engineered, supported, and scaled. Not by replacing humans, but by making them dramatically better.

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The advantage is no longer the size of a tech stack. It’s the quality of the customer-facing moments that tech enables, and that’s driven by insight, relevance, perspective, context, creativity, clarity and courage. It’s no longer all about “cadence.”

The End of the Activity Arms Race

The old model (RevOps if we might be so bold) recognizes teams for how much they produce - how many emails, how many calls, how many sequences, how many dashboards, how many “touchpoints”. People watered down lead definitions so they could show more success. In fact Marc Benioff’s greatest early achievement was the use of the word “lead” instead of “contact” which instantly propelled the value of his system. It wasn’t contact management; it became lead management.

None of the RevOps categories have predictive power anymore (they never did). Not on their own. They may describe movement inside the machine, but they don’t tell you whether that movement is intelligent, persuasive, or connected to any real human buying intent.

The next era of B2B isn’t an arms race of activity. It’s a craft race — a race toward sharper thinking, more relevant messaging, stronger conversations, and better understanding of the customer’s world. They (who’s “they?”) used to describe sales and marketing as “arts, and crafts.” It was something of an “insult.” Science (engineers) built the products. But driven by greed and vanity, sales and marketing wanted to (and did) become engineering problems – science. Science is “cleverer” than “arts and crafts” and who doesn’t want to be “cleverer?”

Where AI Fits Into the Shift

The irony is that AI — the technology most people assumed would create more automation and more volume — can be the catalyst for quality.

Modern AI tools can:

• take in context and generate strategic insights

• help reps plan more intelligently and prepare faster

• simulate conversations and coach reps through better talk tracks

• personalize content at a level no template ever could

• surface client-specific insights that previously required hours of research

• help teams understand buyers at a depth that no “sequence” ever delivered

  
  

This isn’t the AI of “send 10,000 more emails.” It’s the AI of “send five better ones.” It’s the AI of “show up smarter, run a better meeting, create a better conversation.”

This is the bridge from quantity to quality — and maybe it’s already reshaping the way leading teams operate.

The New Battleground

If the last twenty years has been defined by scale, the next will be defined by effectiveness. If yesterday’s edge was motion, tomorrow’s is mastery. If companies competed on how many people they touched, they will now compete on the quality of the impact they have.

The quantity era was built by technology that amplified activity. We threw more technology at these challenges, and all we succeeded in scaling were our sales and marketing crimes and misdemeanors.  The quality era will be built by technology that recognizes we're dealing with people (and all our behavioral vagaries) and that amplifies insight, preparation, and human ability.

The companies that thrive will be the ones who stop chasing bigger numbers and start building better sellers, better marketers, better conversations, and better experiences.

Quality isn’t a nice-to-have. It’s the only advantage left that actually moves people. And people in this space haven’t been moved in a while.

Better Tech, Worse Data: Why B2B Marketing Databases Have Fallen Apart

Ok – so something of a rant here. I’ve been in data “hell” for a few weeks – ever since we contracted with Adapt, purchased some new data from them and then found that despite their validation claims, their data polluted our marketing data to the point we got flagged by our email provider – fabulous. Having dived into this, what have I found? Nothing good. We appear to have systemic poor standards and corruption in (what I see) as the entire data market. This would explain the rise of multi-source tools like Clay? Got any opinions on this? Let me know and in the meantime , read on.

We live in an age where the tools for B2B marketing and sales have never been sharper—AI-powered enrichment, intent-data platforms, predictive scoring engines, slick CRMs. You’d think this combo of tech prowess and data ambition would deliver richer, more accurate company and contact datasets than ever. Instead… something broke. And it’s not the tech. It’s the data.

The paradox of progress

Marketers today roll out campaign stacks that would have been sci-fi five years ago. But when you actually dig into the lists feeding those tools, the fundamentals are crumbling. Job titles are outdated. Companies have inflated or deflated headcounts. Contacts bounce or never existed. And worst of all, the vendors promising “95% accuracy” are still basing segmentation on the same shaky foundations.

Here are the facts:

• Nearly half of marketing data used for decision-making is incomplete, inaccurate or out-of-date.

• Industry benchmarks suggest B2B contact data decays by around 2.1% per month, or roughly 25% a year in many cases.

• A typical marketer believes 25% or more of their database is inaccurate; one survey found 60% don’t trust the health of their data.

So while the tools got smarter, the underlying records didn’t—and in many cases, they got worse.

Root cause #1: Self-reported firmographics (and the incentives to fib)

A lot of the trouble starts with the simplest assumptions: that the company size, industry, revenue and headcount numbers you use to filter accounts are actual facts. In reality, they’re often marketing narratives. Think of it:

• A company rounding up its headcount to look more credible.

• An executive maintaining a LinkedIn profile even after they’ve left.

• A directory scraping numbers without checking if those figures still hold.

When you use filters like “200–1,000 employees, $50–250M revenue, North America”, you assume those numbers are accurate. But many are based on self-reported or loosely gathered data. That means you’re already segmenting on guesswork. Add hybrid/remote workforce changes and more frequent restructures, and the error margin grows.

Root cause #2: Cheap offshore enrichment (lazy at scale)

Data vendors have had their own cost-pressure problem. To deliver millions of records cheaply, many rely on low-cost offshore teams tasked with scraping, verifying and populating fields. Under time pressure and volume quota, the temptation to shortcut looms large: guessing seniority, copying job titles verbatim, catching phone numbers that aren’t checked, submitting email addresses with minimal verification.

At small volumes, you might catch the errors. But at enterprise scale? You're drowning in junk. Thousands of contacts get flushed into your campaigns. Many bounce, some were never relevant, and your sales team wastes hours chasing ghosts.

Root cause #3: Dirty data circulating in an echo chamber

Think of the B2B data ecosystem as a big muddy pond. Even if one vendor tried to clean it up, the neighbors just pump the same water back through a different filter. Many vendors and enrichment platforms buy from each other, license the same public registries, and apply AI models trained on their own imperfect data. That means a wrong title or bad phone number doesn’t disappear — it gets recycled, re-labelled as “verified,” and sold as “premium.” Many practitioners report that all the big vendors feel like they’re pitching the same contacts under different branding.

What’s worse: your AI scorecard might be brilliant, but it's scoring data that’s fundamentally flawed. So yes—“smarter AI” makes faster decisions, but it doesn’t make the data any better.

The acceleration factors: Why it feels worse now

Several trends have made the decay problem worse:

• Job mobility is higher than ever. Emails and roles change frequently. Some analyses show business email bounce rates edging above 3.5% per month.

• Remote/hybrid work blurs the line between full-time employee, contractor, affiliate; the definition of “company headcount” is fuzzy.

• Budget and attention pressures mean companies invest in shiny campaign tech but leave cleaning and stewardship underfunded.

• AI hype drives more automation, which amplifies scale rather than accuracy. The faster you operate, the faster you mis-operate when the base data is broken.

• Everyone’s in denial, scared of losing their jobs, and pointing fingers at everyone and everything else for poor marketing yields.

  
  

Why this matters: Reach, relevance and reporting all take the hit

If you’re playing in the game of marketing-to-sales with bad data, the consequences ripple across everything:

• Reach suffers. Invalid emails, incorrect domains, wrong LinkedIn handles—you’re chasing shadows.

• Relevance suffers. You think you’re talking to an “IT Director at 500-employee SaaS firm,” but you’re actually reaching a consultant in a 50-person firm. Segmentation falls apart.

• Reporting suffers. Since your funnel inputs are dubious, forecasts, ROI, attribution—all become unreliable.

In other words: your big tech stack might deliver the dashboards, but the numbers they display are built on mud. Your sales team loses faith, marketing looks like it’s under-performing, and you spend more to get less.

A better approach: Fix the foundation instead of buying more tech

Here’s what you can do—and by “you,” I mean you, the sales/marketing/RevOps professional who’s sick of junk data.

1. Build smaller, fresher target lists

   Less is more. Focus on tighter, better-defined universes. Refresh them often. One small accurate list beats a million garbage records.

   
   

2. Treat third-party data as a hypothesis, not truth

   Use it as a starting point. Then layer on your own verification—behavioural signals, direct contact, validation calls, sales feedback.

   
   

3. Invest in data hygiene as a discipline

   Clean up bounce rates. De-duplicate records. Assign ownership—RevOps or a dedicated data steward.

   
   

4. Close the loop between sales and marketing

   If a contact is wrong, make the “mark bad data” action one click for sales. Feed it back to your data provider. Negotiate a deal with the vendor where you get replacement data, money back being as you're acting as their QA resource! Make accountability real.

   
   

5. Ask better questions of your vendors

   Don’t just ask “how many records do you have?” Ask “how do you source them?”, “how often do you verify?”, “how do you treat role changes, company dissolutions, remote shifts?” Maybe a waste of time - as we've said, the truth is in short supply generally these days, but specifically in this business it seems.

   
   

6. Avoid Offshore entanglements

   Hard to do these days, maybe impossible in this business, but if the price looks "too good to be true" it probably is!

Conclusion – It’s not a tech problem. It’s an honesty problem

The root issue in B2B marketing data isn’t that the tools are broken. It’s that we scaled the wrong behaviors: we assumed self-reported numbers were correct, we rewarded volume over accuracy, we recycled bad records and slapped “verified” labels on them. We automated the propagation of mediocrity.

If you want real improvement, it’s not about buying a new platform. It’s about changing your attitude toward data. Treat it as an asset. Treat cleaning as a process. Treat verification as non-negotiable. Until then, “data-driven marketing” is just a sophisticated way of being confidently wrong.

If you like this, I can pull together a slide deck too (you know you’ll want one) and a condensed version for a LinkedIn post.