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 CRMs grew bloated with fields that were outdated the moment they were entered.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.

AI and the Lighthouse: Why Some Navigate Further — and Others Run Aground

Rocks, monsters and storms - Can AI be the Beacon of the Friendly Lighthouse?

In modern life and business, we’re all convinced that we’re all captains (or leaders). Ever since Madonna asked us all to “strike a pose,” we’ve been conditioned over the last 30 years to picture ourselves steady at the helm — confident hands on the wheel, charting the course. We’ve got it all figured out and cannot admit to any doubt. Then along comes AI: maybe a lighthouse in the fog?

AI shines a light on everything — the rocks, the routes, and the shortcuts. The recent MIT research, Economist article and observations on “dirty data”, serve to shine their own lights on existing (and sometimes perpetual) problems – illusions of competence, status quo bias,  and old baggage (which as U2 point out – “we can’t leave behind.”). These have inherently little to do with AI, but suddenly, the sea feels different. The question is: does the light of AI make us better navigators… or just more aware of how lost we really are?

Two Views of the Same Horizon

When it comes to AI’s impact on performance, two competing schools of thought have emerged.

One view, championed by Ethan Mollick, author of Co-Intelligence: Living and Working with AI, argues that AI acts as a great equalizer, a democratizer of knowledge - the light that helps the average navigator find their bearings. Mollick argues that AI gives the biggest boost to mid/lower-level performers, helping them think and problem-solve with more structure and insight. In his words (not mine), “it turns poor performers into good performers.”

The other view, recently published in The Wall Street Journal by Matthew Call of Texas A&M, suggests that the opposite is true — that AI widens the gap between top performers and everyone else. A bit like the way the “asset appreciating” economy of the last 15 years has favored those who already have/had assets to appreciate, opening up a bigger gap between the “haves and the have nots.” Call’s research finds that high performers are the ones who adapt first, learn faster, and use their expertise to ask more nuanced questions — seeing the increasingly important details that others miss.

So which is it? Is AI the lighthouse that helps everyone steer straight — or a beam that blinds the unprepared?

When Confidence Becomes Complacency

The answer, as it turns out, depends less on who’s holding the wheel, and more on what you want the wheel to do! . How so?

Top performers — the experienced captains — tend to embrace AI with curiosity and intent. They experiment, and use the light to read the waves ahead. They know when to trust their instruments and when to trust their instincts. They might also suffer from the complacent reaction of “I already knew that.”

Meanwhile, many others — often just as capable but less sure — either wait for “official guidance” or assume the light will do the steering for them. In other words, they confuse illumination with navigation.

This is where the modern workplace illusion creeps in: most people think they’re already captains. We’ve been conditioned to believe we’re all high performers. But AI doesn’t reward self-belief — it rewards skill, adaptability, and humility. It doesn’t care who you think you are. It cares how you think about using it.

The Lighthouse Exposes the Rocks — It Doesn’t Move Them

AI reveals more than it replaces. In sales and marketing, for example, AI can cut through the bias and drudgery of account planning. It gathers data, surfaces insights, and highlights connections that human teams miss. Tools like Shadow act as specialized co-pilots, giving teams greater “intellectual horsepower” by quickly doing some heavy lifting on research, analysis and suggesting obscure but powerful approaches.

But that same light can also expose weaknesses. AI doesn’t fix a poor strategy — it just makes it more visible. It doesn’t stop a team from being biased — it simply shows the bias in higher resolution and draws our attention to it. And if people overestimate their own capability, they’ll still end up sailing toward the rocks, they’ll just do it faster. The equalizer and the divider, as it turns out, are the same beam of light.

How Leaders Keep the Fleet Afloat

So how do you make sure your people don’t get dazzled — or shipwrecked — by the glow of AI? Call’s research offers three good navigational principles:

• Create safe harbors for experimentation. Give teams time and space to explore AI tools without fear of getting it wrong. You can’t learn to navigate by staring at a manual, as sailors know, you have to get wet.

• Share the maps. Capture and circulate effective learnings so one person’s discovery becomes everyone’s advantage.

• Redefine what “good” looks like. Update evaluation systems so AI-assisted work is recognized fairly, not dismissed as “cheating” or credited only to the usual stars.

  
  

In short, AI is less of a compass, and more of an environmental shift — one that requires new habits, new metrics, and a new humility about what it means to be “good” at your job.

Conclusion: The Light Shows What Was Always There

In this regard AI is no different from other technology waves. It’s not THAT you use it, it’s HOW you use. It’s application over availability.

For some, that’s liberating — the light helps them see paths they never knew existed. For others, it’s unsettling — the fog was hiding more than just the shore.

The truth is, AI isn’t inherently an equalizer or a divider. It’s a mirror held up to how your organization learns, shares, and adapts. Lighthouses don’t steer ships. People do. And AI won’t futureproof or propel your organization in and of itself. By applying it courageously and thoughtfully, you will.