AI is finally a game-changing business tool. A year ago, ChatGPT was still blabbing incoherently. Now tools like Claude are making early ChatGPT feel like a toy. AI now sits at the center of real business intelligence and processes. But if your company’s fundamentals are broken, AI can’t save you. AI might be the most powerful tool we’ve ever had, but it isn’t a strategy.

Imagine building a complete indirect channel analysis—performance over the trailing 12 months, detailed financials, and an improvement plan—in two hours while multitasking. Or analyzing your entire funnel, from impressions to cash—with both an executive readout and a full tactical drill-down—in under 30 minutes. Or building an entire pricing management platform in 48 hours. Not a prototype—a working platform more robust than some of the $100K “enterprise solutions” currently being sold with a straight face. We’ve achieved all of these. It’s not incremental improvement, it’s a step change.

Yes, business acumen is still required to challenge outputs and refine conclusions. If you’re not getting at least five “good catch” per chat your analysis is still living in fantasy land. But that’s exactly the point. You’re no longer wasting time building slides or fixing spreadsheets—you’re spending your energy actually thinking. AI handles the mechanics; you handle the judgment.

On the flip side, watch out for the snake oil. I’ve yet to find a so-called “specialized AI tool” that outperforms general-purpose generative AI. HubSpot has been particularly underwhelming. They require significant effort to set up, solve narrow problems, and still manage to make plenty of mistakes. No surprises, if you can’t claim being an “AI company” good luck with your valuation. The result? A flood of half-baked solutions masquerading as innovation.

Save yourself the time and money. Start with generative AI before exploring specialized tools. Many of these companies won’t be around in 12–24 months anyway.

Why Can’t AI Save You?

Let’s say your COGS is 10% higher than your competitors. If you’re an early AI adopter, maybe you squeeze out a 10% from your SG&A. Great—you’ve bought yourself some time. But any halfway competent company will catch up. And then you’ll be right back where you started: structurally uncompetitive but with better charts.

Remember Adam Neumann and WeWork? A so called tech company—more specifically, a “physical social network.” Past the smoke and mirrors, their cost per square foot (adjusted for tenant turnover) exceeded what they could charge. No amount of technology can fix that. If you’ve read From EBITDA to Exit, you’ll remember the “sleazy friend” Adjusted EBITDA. WeWork had one better, “Community Adjusted EBITDA”— a fictional profitability metric in a future where locations required no corporate overhead, no marketing, and no SG&A. Even with today’s AI WeWork would have imploded, of course with fancier dashboards.

Post-Chapter 11, WeWork has achieved positive EBITDA by shedding unproductive locations and enforcing strict cost control. They fixed their fundamentals. Now—and only now—can AI actually help: improving customer targeting, identifying higher-value segments, extending lease durations, or lowering acquisition costs. AI is a force multiplier—not a bailout plan. So the playbook is simple:

1. Fix your fundamentals: competitive product, clear go-to-market, and a healthy cost structure.

2. Implement generative AI—yes, I recommend Claude. Everything else still feels like a toy in comparison.

AI accelerates what already exists. Healthy Business? AI will make you faster, sharper, and scalable. Broken Business? AI will rip the band-aid. As Adam Sandler put it in the Romano Tours SNL sketch: if you’re sad back home, we can’t fix that—you’ll still be your same sad self while vacationing in Italy.

Depending on your background, you might think in terms of cash generation (private businesses) or earnings (public markets). In PE, the lingua franca is EBITDA. And this is where the fun begins—because EBITDA has a sleazy sibling: adjusted EBITDA.

In this world, you can show “healthy” EBITDA while quietly bleeding profitability and cash. Yes, there are rules, but what you’re really selling is a narrative—one investors may or may not choose to believe. If they do, congratulations: your adjusted EBITDA becomes the foundation for your valuation, typically a multiple of EBITDA. And now, the Greek tragedy: that multiple is driven by belief in your industry’s fundamentals and your company’s ability to drive future quality revenues.

So where does GTM actually matter?

On the surface, it’s simple: are you generating enough revenue to cover COGS and SG&A? But the GTM’s real impact is on the EBITDA multiple—because the multiple reflects the quality and durability of earnings.

Here’s the issue: in most diligence data rooms, you get pristine financials and a handful of sales metrics—pipeline, retention, contracts. If it’s SaaS, you’ll also get the acronym Olympics: ARR, NRR, CAC, LTV… plus a few real crowd favorites for laughs:

• S.P.A.M. (Sales Pipeline Acceleration Multiplier)

• F.O.M.O. (Forecast Optimization & Monetization Output)

• B.L.T. (Bookings Leverage Thermostat)

• S.A.L.T. (Sales-Adjusted Lifetime Trajectory)

All delivered with a straight face, ideally across 47 slides.

What you almost never get is a clear explanation of how GTM decisions shape the quality of revenue. And that omission? That’s why post-acquisition sales integrations go sideways.

So what is “the narrative”?

A clear, end-to-end understanding of what’s actually driving your KPIs—across GTM and beyond. And, as usual, reality is less flattering than the pitch deck.

Example #1: The 50% Churn “Business Model”

I once helped integrate a short-run injection molding company losing 50% of customers annually. The official narrative: it’s a transactional business, customers churn, many go out of business. Blame the externalities. The reality: quality issues and missed lead times. Customers weren’t churning—they were escaping.

That misunderstanding turned a 3x revenue deal into something that should’ve been closer to 1x. The cost of internal friction? Never priced in.

Example #2: The Founder Is the GTM Strategy

A ~$5M commodity business charging a 10–20% premium. Strong retention, stable margins—looked great. The assumption: “We’ll plug this into our salesforce and scale it right away.”

Missing piece: the narrative. The founder was the GTM engine. Customers weren’t loyal to the product—they were loyal to the person. Remove the founder, and “premium pricing” becomes “overpriced.” Not a bad business—but the GTM plan needs to accept this is a human moat, not a scalable one (yet).

Your narrative—and your ability to sell it— or to get it right if your buying directly impacts valuation. Getting it right (or wrong) can swing outcomes by millions.

There are three pillars that can help decipher your own or your target’s real narrative:

1. Profitability of your customer journey

How are customers acquired? How do they buy? What’s repeat vs. new?

After surviving the acronym Olympics and whatever showed up on slide 42, it turns out one of them is actually useful: LTV. You need to quantify your LTV for each path of the customer journey to understand what’s truly accretive—and what just quietly destroying value. And don’t aim for perfection; directional will do. This isn’t a physics experiment.

If behavior is shifting (e.g., e-commerce to sales-led), is that value creation or added friction? If you can’t answer that, you don’t have a narrative—you have a very confident guess.

2. What decisions are driving growth?

“Growth” is not an explanation—it’s an outcome. Repeating KPIs doesn’t make them causal. Yes, you should know if you’re spending more or getting more efficient, acquiring new customers or expanding existing ones. But that’s table stakes. The real question is: what specific strategic and tactical decisions unlocked that growth?

Was it a pricing change? A channel shift? A product improvement? A sales motion tweak? If you can’t clearly connect growth to deliberate actions, you don’t have a narrative or strategy—you have a good quarter.

3. Competitiveness of your cost structure

If you priced at market, would you still hit your EBITDA targets? If not, it’s not a pricing problem—it’s a cost problem. Any time you hear “we just need better value selling” or “if Apple can do it…”—you’re not in strategy, you’re in therapy.You can bamboozle a few customers. But without real, defensible value, margin erosion is inevitable. And if you have a cost problem—fix the cost. Otherwise, it’s like fixing a gangrened foot by cutting the hand.

Pricing is easy to change. Costs require actual work, which is exactly why people avoid it.

In conclusion…

Good GTM-driven P&L KPIs are table stakes. A strong understanding of the fundamentals—your narrative—is what gets priced in, and it can be worth millions on exit or acquisition. Because here’s the uncomfortable truth: only about 30–40% of M&A deals actually create value for the acquirer. The rest are… educational.

So yes, your numbers need to be solid—beautiful dashboards, pristine cohorts, a CAC payback chart that gets approving nods. Because nothing says “we’ve got this figured out” like a well-formatted spreadsheet. Unfortunately, spreadsheets don’t explain why the business works… or why it might suddenly not.

But don’t worry—if you don’t have a narrative, investors will happily create one for you. It’ll just be their version—more optimistic during the deal, far more “reflective” six months later. The ones who get paid don’t leave that interpretation up for debate—they make the story painfully, undeniably clear.

So, do you have a solid narrative?

Over the course of my career, I’ve built or rebuilt three different sales organizations — with varying levels of success and humility. But across all of them, one discipline has consistently improved performance: creating a structured, unapologetically intrusive space for weekly pipeline inspection.

It is, admittedly, uncomfortable. And like its medical namesake, it is preventative, not punitive. When done well, it shifts from awkward interrogation into heartfelt coaching. When done poorly, it’s just uncomfortable for no reason — which, unfortunately, many pipeline reviews are.

In “Sales: Art or Science”, we discussed the importance of discipline and recurring pipeline reviews. The Colonoscopy is the practical application — the weekly ritual that reinforces accountability, strengthens forecast accuracy, and drives meaningful CRM adoption.

Assuming, of course, that your CRM is now sludge free. Otherwise, go read “CRMs: The Colonoscopy of the GTM World” immediately. Yes, apparently when I think sales, I think colonoscopy.

What Is The Colonoscopy?

There’s a popular belief that it takes 21 days to change behavior. The number is debatable. The principle is not. Behavior changes through repetition, structure, and visibility.

The Colonoscopy is a structured, scripted, data-driven meeting designed to create those conditions. The agenda is standardized. The CRM view is consistent. Preparation is non-optional.

It is not a brainstorming session. It is not a motivational talk. It is operational hygiene. And accountability works because humans respond to visibility. If commitments aren’t written down and revisited publicly, they will feel urgent in the moment and optional a week later.

The Colonoscopy eliminates “optional.”

Pillars of an Effective Colonoscopy

1. Make It Regular, Consistent, and Efficient

At minimum: weekly.

The team may resist initially. That’s normal. Transparency tends to feel uncomfortable before it feels productive.

Your responsibility as a leader is to ensure the meeting delivers value quickly — clarity, coaching, obstacle removal. Otherwise your team will start scheduling their beach vacations during it.

Keep it fast-paced. Energy signals importance.

2. Salesperson-Led, Group Setting

If leadership runs the meeting, it becomes a report-out.

If salespeople run it, it becomes ownership.

Each seller:

• Pulls their standardized CRM report

• Explains top deals

• Identifies promising new opportunities

• Addresses stalled deals

• Outlines next steps

Five minutes per seller. Discipline matters. A consistent CRM view (typically Kanban) is essential. If it’s not in the system, it’s not in the forecast.

Calibrate inspection depth based on seniority. Junior sellers need guidance. Senior sellers need intelligent challenge and an opportunity to mentor. Otherwise they’ll decide this thing is beneath them.

3. Drive Clear Commitments

This is where the process becomes powerful.

On Monday:

• Each seller commits to a weekly number.

• They articulate which opportunities support that number.

• They define required actions and identify risks.

On Friday:

• They report actual attainment.

• They explain what closed, what slipped, and why.

Research consistently shows that written, specific goals increase performance — particularly when shared and revisited. Public commitment adds a layer of accountability that private intention does not.

A visible dashboard — even a simple whiteboard — that tracks weekly commitments and results reinforces consistency. Over time, patterns emerge. So do coaching opportunities.

Accountability is not about embarrassment. It is about clarity.

4. Keep It Fresh

Structure should remain constant. Incentives should evolve.

Introduce periodic SPIFFs, recognition moments, or stretch challenges. Recently, we offered a steak dinner for achieving a stretch target — two sellers achieved it. Another month, it was lunch for exceeding weekly commitments while staying on quota trajectory.

Small incentives, when paired with visible accountability, can drive disproportionate effort. Unexpected high-priced protein does wonders for motivation.

In Closing

The Colonoscopy is invasive, uncomfortable, and occasionally awkward. But discomfort in a controlled environment is far preferable to surprises at quarter-end.

When implemented correctly, it becomes a cornerstone of sales culture — reinforcing discipline, improving forecast accuracy, strengthening CRM hygiene, and developing sellers through timely coaching.

Preventative medicine is rarely glamorous. It is, however, effective.

And last but not least—realizing how easily percentages can be manipulated, and how disastrous the outcomes can be. Because many of us, though we pretend to be rational beings, fail to apply mathematical thinking to our daily lives.

The UK Pill Scare That Wasn’t

According to How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg, in 1995 the UK Committee on Safety of Medicines issued a public warning—widely amplified by the media— that third-generation oral contraceptive pills doubled the risk of life-threatening blood clots. (Yes, a 100% increase.)

As a result, many women stopped taking their contraceptives. A survey later found that 40% of unplanned pregnancies were among women who had stopped after the announcement. What the warning failed to mention? The risk went from 1 in every 7,000 women (0.01%) to 2 in every 7,000 (0.02%). Mathematically true—but catastrophically misleading.

From Birth Control to Pipeline Control

While misreading your win-rate might not cause a public health crisis, it can cause a financial one—personal, corporate, or (most likely) both. And the problem is far more widespread than you think. Even CRMs get it wrong.

Gold Medal for Bad Math: HubSpot

HubSpot’s default win-rate formula divides won business by the sum of won + lost business. Which makes me wonder: Has anyone at HubSpot ever actually worked in sales—or passed high-school math?

Here’s why that’s nonsense:

• Most salespeople never mark deals as closed-lost. (Hope never dies, right?)

• That means the formula ignores all open deals—usually the largest chunk of your pipeline.

• The result? A wildly inflated win-rate that looks great on dashboards and terrible in boardrooms.

Should you use HubSpot’s win-rate for planning or forecasting? Absolutely not. Maybe as a warning of how not to calculate win-rate. Perhaps that was their master plan all along. The baseball equivalent would be calculating a batting average by counting only hits and outs—while ignoring strikeouts. That fourth at-bat might not be as impressive as you think.

Unless you have a very short sales cycle and a team of monks disciplined enough to close every deal properly, that number is meaningless. Sure, maybe the math checks out—in the limit as t → ∞. I know, hilarious.

Silver Medal: Timeframe Confusion

The runner-up mistake? Using won business in a given month divided by total pipeline, without defining a timeframe. Sure, at least the denominator isn’t just won plus lost business. But win-rates without a clear timeframe are like horoscopes—vaguely comforting, empirically useless.

A close tie for silver:

When you calculate won business this month (regardless of creation date) divided by pipeline created this month. That also overestimates your win-rate—because you’re borrowing wins from previous months and comparing them against fresh opportunities.

Bronze Medal: Premature Calculation

If your sales cycle is 30 days, you can’t compare the current month win-rate to previous month and call it settled. It won’t stabilize until 30 days after the month ends. Unless you’re a decorated psychic, you need full cycles to calculate a valid win-rate. Otherwise, you’re just using random numbers to make business decisions. All in on one black, might as well!

The Pure Win-Rate

For the past seven years—ever since I finally reached win-rate maturity—I’ve used what I call the purest win-rate calculation, almost as elegant as E = mc²:

I also make it a point to tell stakeholders—CEO, board, investors—whether I’m sharing a trend (early in the cycle) or a settled win-rate.

The Real Math of Sales

Unless your business fundamentals truly demand a different definition, any deviation from this formula means you’re doing reporting gymnastics—not business analysis. Remember: every at-bat counts. Ignore the strikeouts, and sure—your batting average looks pretty. But you’ll miss your forecast, underestimate your customer acquisition costs, and, get traded to the Colorado Rockies.

So yes, paid marketing can work — but only if your unit economics aren’t a trainwreck. If your numbers don’t support paid, turn off your Google Ads account and pick up the phone. Cold calls are cheaper than letting Larry and Sergey’s machine grind your bank account to dust.

And if you don’t know your unit economics? Hard U-turn. Go read The Paid Media Spiral. If you don’t know what ROAS is? Even harder U-turn. Seriously, you’re not ready for the rest yet. Once you’ve cleared the “math quiz,” here’s the real question:

How much of paid marketing success is strategy vs execution?

The answer lives in four pillars:

1. Have an In-House PPC Expert — Even If You Outsource Everything

You can outsource execution. You cannot outsource judgment. Your in-house paid person’s job is to:

• Ask the uncomfortable questions the agency doesn’t want to answer.

• Understand the weeds. tROAS, tCPA, Manual CPC, Enhanced CPC, PMax, AIMax, DSA, Last Click, First Click, Linear, Time Decay, just to name a few. It’s less a toolkit and more a jungle — and unless you can name every plant, you’ll end up eating the poison berries.

• Translate business goals into campaign rules and KPIs.

The world of Google Paid is sludge. Google Ads, Tag Manager, Merchant Center, Analytics, Business Profile, Looker Studio — for some reason all designed to report slightly different numbers for what should be the same data. A full-on horror film could be made about a newbie trying to untangle them. Unless you’re John Nash with a whiteboard in the garage, you’ll need someone who’s fluent in superlative sludge.

The Google’s ad suite is basically Yoda: ancient, powerful, and wise… but also impossible to understand, contradicts itself constantly, and still somehow runs the galaxy.

By the way — if your team isn’t running 3–5 campaign experiments and A/B tests at any given time, you’re not optimizing. Your “expert” is just babysitting.

Bottom line: your in-house expert should be your agency’s worst nightmare (in the best possible way), and should be leading your optimization efforts.

2. Build a Strong Analytics Backbone

Google’s native reporting is fine… if you enjoy bedtime stories for toddlers. For business decisions? Hot garbage.

Here’s what you actually need:

• Data pulled together across paid, organic, CRM, and revenue.

• The ability to zoom out (big picture) and drill in (what broke and why).

• Real-time visibility into spend and performance.

This usually means Power BI or Looker Studio, but here’s the kicker: Unlike Google Analytics, Google Ads doesn’t connect natively to Power BI. Why? Because Google. You’ll need a third-party connector like Windsor.ai to plug that hole.

Without this backbone, you’re not piloting the plane — you’re reviewing the black box after the crash. And don’t waste your life chasing “perfect” data. It doesn’t exist. You need good enough proxies to make timely calls. Managerial judgment fills the gaps. If you want perfection, fire everyone and hand the keys to Mrs. Algorithm.

3. Don’t Forget About Offline

Even Amazon sells offline. If you have a sales team working abandoned carts, quote requests, or inbound leads, Google attribution is giving you maybe half the story.

You’ve got three options:

• Easiest way: assign average deal values to bottom of the funnel conversions on the Google Platform. Crude, but it sends buying signals back to Google’s machine.

• Easy way: push source info into your CRM (Salesforce, HubSpot). You’ll get precise attribution reports — but they won’t feed signals back to Google.

• Capitalist way: spend enough money that Google assigns you an account manager who’ll help integrate offline conversions into your Ads account. Precise, automated, and expensive. Welcome to capitalism: the more money you give, the better service you get. Just like western democracies.

4. Align Targets for Revenue, Spend, and ROAS

“If you don’t know where you’re going, any road will get you there.” The Cheshire Cat nailed it. Unfortunately, “there” might be permanent un-paid vacations. Targets keep you from wandering off a cliff. So if you prefer week-long paid vacations, definitely the three targets that lock together:

1. Monthly spend

2. Revenue

3. ROAS

And no, ROAS is not redundant if you’re tracking spend and revenue. It’s the glue. Stop managing paid like a static budget line. Tie spend to revenue as a percentage. If sales climb, your budget climbs. If sales tank, pull back.

Better yet: lean in early, over-invest a bit, watch the trend, and course-correct. ROAS is your north star. Hit it, and you’re scaling profitably — even if you blew past the original “budget.”

In Conclusion…

If you business leans on paid marketing, you need three things:

• A leader who understands the fundamentals and can smell BS a mile away.

• A hands-on keyboard expert who lives in Google’s ecosystem (3 years minimum to reach true fluency) and can read the matrix.

• A CRM administrator who can tie online to offline.

Agencies are fine, but they’re execution partners — not your brain. Analytics is mandatory, not optional. Offline attribution is both a reality check and the other half of your paid spend.

Paid marketing is ubiquitous, but it’s not easy. If you can’t confidently say you’re on top of your program, guess what? You’re not.

Meanwhile in Go-To-Market land: “You scaled a SaaS company? Fantastic — now go lead sales in industrial manufacturing.” Because clearly, switching industries is just like switching seats on the same flight… right?

This isn’t another LinkedIn pep talk about impostor syndrome or how feeling “out of your depth” is a beautiful sign of personal growth. No. This is about something more foundational: Are you even remotely qualified to lead your sales team in this industry — or are you just hoping the turbulence goes away?

So I Asked ChatGPT: What Kind of GTM Leader is Needed to Succeed?

Yes, you can outsource a plethora of existential crisis to AI. Productivity win.

First up: What kind of GTM leader thrives in the safety industry?

The answer was alarmingly reasonable: it’s a mature, commoditized industry, where the secret sauce is process discipline, fast response times, and making customers feel like they’re not being ignored. Also, if you manage to upgrade the tech stack beyond “Dave’s Spreadsheet v4,” you’re basically a prophet.

Then I asked the same question for the additive manufacturing industry.

This time, the advice boiled down to: Hire Gandalf.

Apparently, you need someone who can navigate market chaos, sniff out the one promising tech from the dumpster fire of LinkedIn hype posts, and cast spells to convince skeptical buyers to adopt something nobody fully understands yet. Less Six Sigma, more storytelling. Charisma over process. Big vision over consistent execution.

Same job title. Completely different DNA.

We Hire for Culture Fit. We Fire for Personality Mismatch.

We spend hours obsessing over culture fit, industry experience, and the sparkle on someone’s track record. Then someone with a “Top 50 Under 50 While Skydiving” award joins your team… only to crash and burn within 12 months.

Why? Because we hired for pedigree, not sales personality fit.

There’s a brilliant little book called Selling the Wheel by Jeff Cox and Howard Stevens. It breaks down four classic sales archetypes — and once you know them, you’ll start seeing them everywhere.

🚪 The Closer

• Lives for: High-stakes, high-velocity sales. Quotas are a love language.

• Strengths: Aggressive, decisive, allergic to silence.

• Weaknesses: Will absolutely try to close the deal before the prospect finishes saying “Hello.”

🧙 The Wizard

• Lives for: Cutting-edge innovation. Selling the dream — even if the product isn’t quite awake yet.

• Strengths: Charisma. Vision. Gets customers to buy PowerPoint decks like they’re finished products.

• Weaknesses: CRM hygiene is a myth. Follows up… eventually. Maybe.

🏗️ The Builder

• Lives for: Building territories from scratch, designing process flows, color-coding their calendar.

• Strengths: Reliable, scalable, and low-drama.

• Weaknesses: Will spend six weeks planning instead of selling. Chaos gives them hives.

📚 The Researcher

• Lives for: Product knowledge, documentation, and fixing the internal mess others leave behind.

• Strengths: Knows more about the product than the product team. Actually reads release notes.

• Weaknesses: Selling? Oh no, no thank you.

Why This Actually Matters for GTM Leaders

You might think: “We’ll just train them.”

But here’s the thing — these aren’t just skills. These are personalities. You can’t train a marathoner to win the 100-meter dash. You’ll just end up with a tired and frustrated sprinter and no medals.

Same goes for GTM leaders. If your VP of Sales built her career pitching bleeding-edge SaaS with visionary decks and a half-working beta, don’t expect her to dominate in a margin-sensitive market where the buyer wants a quote, not a TED Talk. And vice versa.

Worse — products evolve, and your GTM org needs to evolve with them. The same way CEOs in some industries cycle out every 3–5 years, your sales org might need a different dominant personality depending on your product’s life stage.

There was a time when selling insurance required a Wizard to convince people it wasn’t a scam. Now? Visit a website and you’ll get four voicemails, three emails, and a LinkedIn connect request before you click “quote.” It’s a Closer’s world now.

So… Should You Be Leading This Team

Maybe.

But only if you’re not a Wizard trying to fly a Closer’s plane. Or a Builder stuck in a startup that needs pyrotechnics, not process maps.

Because leadership isn’t just about logging hours and working hard. It’s about knowing whether you’re on the right damn flight in the first place.

The better question isn’t just “Am I good at this?” — it’s: Does your GTM org — from top to bottom — have the right sales personalities for the market you’re in today?

Because if not? No amount of Salesforce dashboards is going to save you from a crash landing.

Why? Because all forecasts are wrong. Not some, not most—all. Nobel laureate Daniel Kahneman said as much: we live in an unpredictable world, and despite that, we still cling to forecasts like a toddler with a security blanket. Worse, we plan entire businesses around them. The irony is that the most honest—and accurate—five-year forecast would simply read: “We don’t know. But we’ll try our best.” Say that in your next board meeting and see how fast your badge stops working.

Kahneman’s research shows that algorithmic decision-making consistently outperforms human judgment. In one study, algorithmic diagnoses beat experienced doctors. Not because algorithms are brilliant—but because humans are predictably biased and lazy. Add office politics, sales incentives, and gut feelings into the mix and you get the predictive power of a broken compass.

Forecasting in Go-To-Market: The Theater of the Absurd

Anyone who’s worked at a mid-to-large company knows the five-year planning ritual. Months of spreadsheets, meetings, and slide decks—none of which resemble reality once the ink dries. And yet, sales leaders persist in assigning revenue probabilities like we’re playing Deal or No Deal: “This deal is at 70%.” Based on what? The rep made eye contact with the buyer on the Zoom call?

Even CFOs aren’t immune. Kahneman’s survey found that the correlation between CFOs’ forecasted and actual S&P 500 performance was zero. As in: they did no better than political pundits predicting elections based on lawn signs.

Still, companies continue forecasting revenue as single numbers instead of ranges, as if the universe cares about our spreadsheets. According to ChatGPT, in the last five years, roughly 50% of S&P 500 companies missed their forecast by more than ±5%. That’s basically a coin flip. So yes, you might as well outsource your forecast to a psychic—or better yet, the late Paul the Octopus, who correctly predicted 85% of World Cup match winners.

On the flip side, IBM’s Watson correctly predicted tennis match winners approximately 70% of the time using algorithmic models. Even the original electronic line-calling system in tennis didn’t detect the actual ball—it used statistical modeling to estimate where the ball would land. Let that sink in. Tennis has better forecasting than most companies.

So, What Is Algorithmic Forecasting?

It’s forecasting without gut feelings, sales bravado, or horoscopes. Instead, it uses statistically significant data, processed through a mathematical model that reflects your business dynamics. Yes, it requires math. No, it doesn’t care about your “hunch.”

Your Quick and Brutal Guide to Algorithmic Forecasting:

1. Determine your forecasting cycle.

   Fast-moving businesses might need daily sales unit (sales/day). Selling airplanes? Try annual. The point is to pick a unit of time that reflects how often your business changes.

2. Choose a rolling window and normalize.

   Depending on seasonality, you may want a 30/60/90-day rolling window. To normalize, use a little something called the Central Limit Theorem: take at least 30 random samples to stabilize your data. Yes, the same theorem that made you want to drop out of Stats 101.

3. Measure days to close.

   This gives you a window of precision. If your average deal closes in 30 days, your 30-day forecast is based on actual pipeline. A 60-day forecast? You’re losing precisions, you’re estimating future pipeline.

4. Know your win rate (the clean version).

   No, not the one where reps check boxes in Salesforce. Use this:

   Win Rate = Total Sold Amount ÷ Total Quoted Amount.

   It’s simple, clean, and doesn’t rely on human judgment—always a plus.

5. Avoid “deal aging” weighting.

   It’s tempting to give more importance depending on deal age. Don’t. It complicates your model, incorporates mathematical fallacies and adds no meaningful accuracy.

6. Segment by vertical or cohort.

   Each product line, region, or customer cohort has unique dynamics—days to close, win rates, etc. Treat them differently in your model, or prepare for wildly misleading forecasts. But don’t over do it, stick to meaningful segments with significantly different dynamics. More segments won’t necessarily equal higher precision.

7. Ban subjective inputs.

   Sales reps assigning probabilities? No. Use a coin flip. Or a horoscope. Both are equally accurate—and faster.

8. Build, test, and iterate.

   Use historical data to build a model and test its output against actuals. Tweak your parameters. This is where the magic (and reality) happens.

9. Calculate a forecast range.

   Stop chasing single numbers. Here’s a sample formula, for a monthly forecast:

• Average Monthly Forecast = Actual Sales + (Avg. Sales/Day × Business Days Left)

• High Forecast = Average Monthly Forecast + (2 x standard deviation)

• Low Forecast = Average Monthly Forecast - (2 x standard deviation)

  In other words: forecast with confidence intervals. Not fairy tales.

The Closing Argument: Kill the Crystal Ball

Yes, this is dense and technical. But so is flying a plane—and you wouldn’t want the pilot using vibes and intuition, unless you are Spirit Airlines. Forecasting deserves the same rigor. It’s time to retire the “magic 8-ball” forecasts and hockey-stick graphs that magically curve up in Q4.

Imagine a world where go-to-market teams don’t set themselves up for failure every quarter. Where pipeline reviews don’t feel like improv night. Where reps don’t have to pretend they know the odds like a Vegas bookie. Imagine forecasts grounded in math, not hope.

And if that still feels like too much work, don’t worry—Leo season starts soon.

Ah yes, the CRM. The silver bullet of revenue woes. A single, unified platform to manage all your customer-facing activity! What could possibly go wrong?

Well, just about everything.

In mid-sized businesses especially, a CRM often becomes the digital equivalent of a junk drawer. Random things go in, no one knows where anything is, and may god help the next person who opens it.

For most of my go-to-market leadership career, I’ve had to fix the CRM before I could fix strategy or execution. Otherwise, it’s like trying to run a Formula 1 team when your drivers are blindfolded and your telemetry system is just a guy yelling guesses from the pit lane.

As the Cheshire Cat once said: “If you don’t know where you’re going, any road will get you there.” That’s what happens without a functional CRM—you’re making GTM decisions with all the precision of a Magic 8-Ball.

Why Something So Supposedly Useful Ends Up So Spectacularly Broken?

Keep It Simple, Stupid.

The primary killer of CRM adoption is a delightful concept called sludge. According to Nobel laureate Richard H. Thaler and Cass R. Sunstein in their book Nudge, sludge is “excessive or unjustified frictions that make it harder for people to get what they want or to do as they wish.”

And companies? They love sludge. They pile it on like it’s a competitive advantage.

Example: Leadership insists that Sales and Marketing document every single customer interaction, capture every phone call and email, and fill out every field imaginable—across a CRM pipeline with more stages than a Shakespearean play. It’s friction masquerading as strategy. Let me be clear—I like CRMs. But even I wouldn’t use one this demanding.

Worse still, most of the information you’re collecting? It’s never used. You’re asking sales reps to spend time creating data that serves absolutely no one. You are, quite literally, getting in your own way.

Here’s a real-world example: Once upon a time, our Director of Demand Generation requested that Sales collect both Industry and Sub-Industry, 60+ options to chose from. Sounds useful, right? Except… we didn’t segment by industry, didn’t have industry-specific content, and our reps had never been trained to tailor messaging. While collecting Industry might have been a forward-looking investment, Sub-Industry was pure, uncut sludge.

Then we get to everyone’s favorite CRM “feature”: the open text field. You know, those lovely little boxes where reps dump notes that can’t be read at scale, can’t be analyzed statistically, and can’t be used to coach anyone on anything. I’ve often wondered: Did Marc Benioff and the Salesforce team sit around and say, “Let’s create fields that mimic shouting into the void”?

And let’s not forget when a 10-day sales cycle is mapped against 12 pipeline stages. Unless every stage represents a distinct and essential customer behavior, all you’re doing is sludging your sales process into irrelevance.

The Zero-Sludge Recipe for CRM Adoption

Let’s settle something up front: unless you’re a tiny company running a free CRM, there are only two CRMs worth discussing—Salesforce and HubSpot.

• Salesforce is for large orgs with big budgets and messy processes. It’s powerful, flexible, and wildly easy to screw up.

• HubSpot is best for most mid-market companies. It has just enough power to get the job done, and just enough constraints to keep you from creating a CRM-shaped monster.

Anything else? It’s like buying a knockoff Android phone with a Cyrillic keyboard and wondering why it won’t connect to Bluetooth. And yes, for the love of all that is functional, building your own CRM is right up there with New Coke and Blockbuster passing on Netflix.

Also, let’s banish the “we’re unique” excuse. Your business is probably not that special. No really— it’s the sludge creating the illusion of uniqueness. Most businesses don’t need elaborate CRM customizations. What they need is clarity, simplicity, and the willpower to say no to sludge.

Here’s your No-Sludge CRM Adoption Checklist:

• Track only the MVP fields: Close Date, Deal Amount, Sales Stage, Competitors, Close-Lost Reason, and Product. If it’s not directly tied to forecasting or coaching, drop it.

• Automate everything: Most CRMs integrate with Outlook, Gmail, and VoIP tools. If reps have to log calls manually, it won’t happen. Full stop.

• Make performance visible: Build dashboards that show adoption behavior and compare reps side-by-side. Shame is a surprisingly effective motivator.

• Run regular KPI defense sessions: Make every rep explain their metrics in front of their peers. No one wants to be the repeat under-performer with excuses.

• Show value back to reps: If your CRM is just a compliance tool, reps will rebel. But if the data fuels real coaching, peer learning, and better performance, adoption becomes a no-brainer.

And yes, you’ll still have some rotten apples. But your top performers will embrace a system that helps them win more, faster, thanks to less sludge.

In Summary: De-Sludge or Die

A CRM isn’t a magic wand—but when used properly, it becomes the nervous system of your revenue operation.

Keep it simple. Use the data. Coach from it. Inspect what matters. Run the playbook. And for the love of everything sacred in GTM—stop adding sludge.

Only when your CRM is clear, usable, and valuable will it empower your sales strategy with something called go-to-market science. You’ll finally be able to measure, iterate, improve, and repeat.

Until then, you’re not managing a pipeline—you’re babysitting a bloated digital mess that everyone hates.

Commissions, commissions, commissions.

The default response to any sales issue: “Let’s raise commissions!”

Revenue will surely follow, right? Well… maybe. But also, maybe not. Sales incentives, like every other part of your go-to-market machine, are just one gear in a larger system. And when one gear is mismatched, the whole thing seizes up like a 2003 Dell laptop running Photoshop.

Your incentive plan needs to align with three things:

• Your sales culture

• Your objectives

• Your team’s ability to adopt and understand it

And above all: make it simple. As Nobel Prize winner Richard Thaler said in Nudge: “MAKE IT EASY.” If your reps need a calculator, a Ouija board, and a prayer to figure out their commissions, they’ll ignore it. And then you’ve built a useless system.

Culture: Koalas or Sharks?

Culture is the most overlooked piece of any comp plan. Let me paint you a picture: Your frontline manager just convinced you to raise a new hire’s base salary to land the candidate. To stay within budget, you quietly reduced commissions. And just like that, without realizing it, you changed your sales culture.

Here’s a cheat sheet:

• High base, low variable = Koalas. Risk-averse, reactive, sweet. They’ll answer the phone, but don’t expect them to chase.

• Low base, high variable = Sharks. Proactive, hungry, aggressive. Sometimes too aggressive.

If your team is full of couch-bound koalas and you’re wondering why no one is prospecting… check the base salary. On the flip side, if your floor feels like HBO’s Industry—cutthroat, political, and exhausting—you may have accidentally created a Hunger Games economy with oversized commissions.

Be intentional. You should design comp plans to shape your culture, not let culture emerge from a thousand disconnected compensation decisions.

Should We Pool Commissions?

Hard no. In theory, pooled commissions drive collaboration. In practice, they let underperformers hide behind top reps and foster resentment. If I had a dollar for every time I heard, “I’m carrying the whole team,” I’d have… well, enough for a round of pooled commissions.

You want collaboration? Build a strong team dynamic. Hold regular reviews. Celebrate wins together. But don’t expect your comp plan to turn lone wolves into high-functioning wolves of Wall Street.

In my experience, the sweet spot lies somewhere between 50/50 and 70/30 (base/commission). Once you cross the 70/30 threshold, you’re in Koala Country. And no, they don’t sell there.

Objectives: Pay on Revenue or Profit?

The debate never ends:

• Pay on revenue, and reps will discount to close.

• Pay on profit, and you create alignment… if reps like math and access real-time margin data (hint: they don’t)

Again, Thaler: MAKE IT EASY.

Unless your reps have instant access to cost data and can calculate profit on the fly, paying on profit is a fantasy. You’re basically asking them to fly a plane blindfolded while you whisper, “Trust your instincts.” Instead, I recommend a commission multiplier based on discounting behavior:

• Sell above list price? Get a higher multiplier.

• Discount too much? Lower multiplier.

Simple. Visible. Teachable. Effective. It nudges behavior without requiring a spreadsheet wizard badge.

Product-Specific Commissions: A Well-Intended Mess

Want to pay more commission on new products? Sounds smart. Never works.

Why? Because:

• The payout is fractional.

• The logic is too complex.

• Reps don’t notice the difference.

• It turns every comp conversation into A Beautiful Mind.

Instead: use SPIFFs.

Short-term, focused, behavior-changing incentives. Roll them out with energy, structure, and a clear follow-up plan. When done right, SPIFFs work like instant-yeast for your pipeline (yes, I’ve been baking lately).

Can Commissions Be Too High?

Ah, yes. The age-old conundrum: “What if I pay my rep too much?” Well, first of all, congratulations. But also: you might be accidentally funding your local golf course economy. If commissions outpace company growth, two things happen:

1. Your top rep coasts the back half of the quarter.

2. Your struggling new rep quits before ramping up.

A good plan ensures hunger lasts longer than the quarter, and no one gets fat while the company’s still lean.

And please—don’t even mention capped commissions. Unless you’re The Office Dunder Mifflin (Saber at that point) when Jim hits his cap and checks out from work. Capped plans kill the illusion of unlimited potential, and if reached will make your sales people check-out. Keep in mind that Koalas won’t mind caps as much, but sharks will.

Temporary caps during ramp-up? Fine.

Permanent caps? You might as well issue salespeople punch cards and ration slips.

Adoption: The Most Important Thing You Forgot

The best-designed plan on Earth is worthless if your reps don’t:

• Understand it

• Trust it

• Know where they stand at all times

Your plan must be top of mind, not buried in a SharePoint folder no one’s opened since the offsite. You need real-time dashboards, one-on-ones, and open books. I’ve seen more reps get motivated from a simple Google Sheet tracking attainment than from a glossy comp PDF emailed once a year.

TL;DR – Or, The Laws of Incentive Plans According to Sales Calamity:

1. Culture first. Are you hiring Koalas or Sharks? Make it intentional.

2. Make it simple. If it’s not easy to understand, it won’t drive behavior.

3. Avoid common traps:

   • Pooled commissions

   • Product-specific payouts

   • Capped earnings

4. Use SPIFFs for focus. They work.

5. Don’t forget adoption. A comp plan they don’t look at is just a Word doc with delusions of grandeur.

And finally: don’t let the tail wag the dog. Build your plan to reflect the culture, performance, and profitability you want—not the random collection of habits pass down through generations.

“We are a premium brand, like Apple. We should be charging premium pricing”.

Ah yes, Apple. The corporate north star. The favorite excuse of every C-suite that wants to charge more without delivering more. Let me give you some friendly advice:

Never - Use - Apple - As - Your - Benchmark.

Apple is a unicorn riding another unicorn, powered by a software platform moat so deep, medieval kings would’ve traded their kingdoms (and probably a few thousand peasants) for it. They are the exception to every rule. Emulating them is like picking up a tennis racket for the first time and deciding to serve like Federer. Trust me—I’ve tried. It ends with pulled muscles and broken dreams. And it will for your brand too.

Apple didn’t decide to have a premium brand. They earned it over decades through iconic products, unmatched user experience, and billions in R&D. Their “brand” wasn’t the spark—it was the smoke from a raging fire of product and platform excellence. Let’s break it down.

Brand. Product. Platform.

Three pillars. Only one of them is the result, not the input. You don’t start with brand. That’s not how it works. Apple’s brand equity wasn’t willed into existence by a clever campaign or some visionary on LinkedIn. It took 34 years, a near-death experience in 1997, and the launch of a product that fundamentally changed how humans interact with technology.

Remember BlackBerry? Yeah, they launched a smartphone five years before the iPhone. But their UX was so painful in comparison that all of a sudden it felt like texting with oven mitts. Apple didn’t just build a better phone—they built a better experience. And then they built a world around it. Your phone syncs with your laptop, your watch, your TV, your car, and—if you’re truly cursed—your gym equipment. That’s platform power. And that’s what makes customers stick around even when the new iPhone looks exactly like the old iPhone, only with “slightly rounder corners.”And yet, more often than not, companies think they’re just one “brand refresh” away from world domination.

Using a real example—let’s call them ACME, mostly to protect the guilty. What’s ACME’s plan? Raise prices. Chase complex projects their competitors turned down. Deliver half-baked services and then blame marketing when customers start ghosting them. You can’t skip the hard part. You can’t brand your way out of a weak product. Marketing isn’t a wand—it’s a megaphone. If what you’re selling is broken or boring, all marketing does is help people figure that out faster.

If your fundamentals suck, your brand will suck. If your customer experience is subpar, no amount of slick design or poetic web copy will save you. If your product can’t meet expectations, your premium pricing is just a tax on disappointment.

You Don’t Get the Marketing Flywheel Until You Earn It.

That magical place where your brand, product, and experience compound into a self-reinforcing growth machine? It’s real. But it’s on the other side of years of product-market fit, customer obsession, and operational discipline. Until then, your biggest problem isn’t awareness. You’re not under-marketed, you’re under-built.

Set Realistic Brand Goals

Not being at the flywheel stage doesn’t mean brand doesn’t matter—it means your expectations (and budget) should come with a dose of reality. At this stage, the goal isn’t to be iconic and premium; it’s to be recognizable and relevant. A strong early milestone? That your target customers know your name and have a clear sense of what makes you different. And that every touchpoint—your website, your digital ads, your sales team, your executives, and yes, every order you actually deliver, stress on every order—consistently reinforces that message. That’s how real brands are built: not through slogans or ad spend alone, but through repetition, clarity, and delivery. Do that well, and then and only then, you’ve started your journey towards the brand flywheel, where pricing and success is no longer bounded by the laws of reality.

Let’s rewind. It’s 2002. I bought my first MP3 player. Not an iPod. I bought an iRiver. Never heard of it? Exactly.

Now, back then Apple wasn’t the Apple you know today—it was clawing its way back from near-bankruptcy. The iPod had a 10GB hard drive, grayscale screen, and no video. The iRiver? Color display, video playback, voice recording, 20GB of storage, high-quality audio, and a design that looked like it came from the future. So naturally, it failed. Why? Because no one could find the damn thing.

The iPod? It was everywhere—Best Buy, Circuit City (RIP), Sam Goody (RIP). It owned the shelf space. You could see it, touch it, and buy it. iRiver? You had to go on a digital scavenger hunt just to figure out how to order one online.

Here’s the point: being the best product doesn’t matter if customers can’t find you. Controlling shelf space—physical or virtual—is the difference between becoming the iPod or the iRiver. And yes, iRiver is now dead.

In B2B, shelf space isn’t as straightforward as an endcap at Walmart. It’s a chaotic, dimension-hopping, multichannel Marvel-style multiverse—e-commerce, direct sales, channel partners, inside reps, resellers. The more channels you add, the more likely they are to fight like siblings in the backseat. Each channel requires unique expertise: e-commerce (digital expertise), Amazon/Walmart (tough negotiators), direct sales (lone wolves and high SG&A on your P&L), VARs (good luck getting mindshare). They all compete with each other, and they all need their own systems, incentives, and handlers. So unless you enjoy chaos and underperformance, pick wisely.

Here’s a framework to help you define your B2B Shelf:

1. Where Do Your Customers Buy?

Amazon has 310 million users, but that doesn’t mean you can sell MRI machines there (yet). Want to sell cars? A dealership still beats a shopping cart. Find your competitor’s products. Where are they being sold? That’s your first breadcrumb.

2. Product Complexity.

The more complex the product, the more “convincing” the buyer needs. Want to sell a six-figure software platform? You’ll need a few whitepapers, some case studies, a proof of concept, and probably a steak dinner. This isn’t a “click and buy” situation.

3. Push vs. Pull.

Is your product pulling in buyers or do you need to push it down their throats? iPhones today? Pull. People walk into stores demanding them. Tesla (pre-politics)? Massive pull. General Motors’s E.V.s? Push—plus rebates, discounts, and begging.

Now combine all that and map your channel strategy. See the 2-axis chart below, x-Axis: Push vs. Pull, and Y-Axis: Product Complexity.

Each quadrant demands different channels:

• High Push and High Complexity: You need a field sales army and maybe specialty resellers.

• High Pull and Low Complexity: Online and distribution all day long.

• High Push and Low Complexity: Maybe inside sales or value added resellers.

• High Pull and High Complexity: Congrats, you’ve built something magical. But you still need field sales and maybe specialty resellers.

So, what’s the moral of the story? Choosing your GTM channel mix isn’t about throwing spaghetti at the wall and seeing what sticks—it’s about knowing where your buyers are, how much convincing they need, and whether they’re actually looking for you or just squinting at a shelf full of shinier options. The graveyard of “better products” that lost the channel war is deep—don’t let yours be the next iRiver. Start with clarity, stay focused, and remember: in GTM, visibility beats virtuosity every time.

And this, dear reader, is just the start. Next week, we’ll get into the messy fun of how to actually manage each channel, structure incentives, and minimize bloodshed.

Hopefully by now, you’re convinced that even in B2B, e-commerce is a must. So let’s talk about what your e-commerce site should do and how to measure success.

E-commerce ≠ Marketing Website

Your e-commerce platform has one core job, to generate revenue. This includes both:

1. Closing transactions, and

2. Initiating qualified sales opportunities that require follow-up.

Too many companies think of their marketing website as the driver of #2, while their e-commerce portal is focused only on #1.

However, an effective B2B e-commerce site seamlessly integrates both sites and supports customers not only in the decision stage but also during consideration. Apple’s website is a great example, you go from learning to buying in one click. Most companies only optimize their e-commerce journey for the decision stage—making it as easy as possible to click “buy.” But even best-in-class sites only convert 2-3% of visitors into purchases.

What about the rest?

Turning the 97% into Gold

Using real numbers from the past, let’s say you drive 200,000 visitors to your site per month. If your fundamentals are solid, you are likely to convert 4,000 into transactions (2% standard e-commerce conversion rate). That leaves 196,000 engaged visitors who didn’t buy.

These aren’t cold leads. Some of them are warm highly qualified leads. But unless you have a plan to capture and follow up with them, they’re gone. At the 3DP OEM, actual growth didn’t happen until we effectively targeted the 97% that didn’t convert online.

Step 1. Making sure your site is fundamentally sound:

• SEO tools like SEMrush or SEO Site Checkup will assess your site’s performance and suggest improvements across meta tags, headers, broken links, site speed, etc.

• PPC strategy must be aligned with your industry (make sure to review “The Paid Media Spiral”).

• Product pricing and margin expectations should be aligned with the market

Once your fundamentals are solid you should achieve conversions close to 3%, so there’s still a massive opportunity in the 97% that didn’t transact.

Step 2. Identify and Nurture High-Intent Visitors:

This is where elite e-commerce strategies shine: identifying and segmenting high-intent visitors for follow-up. This implies that you can identify your prospects. But some wonder about the impact of forcing users to sign-in before checkout. There is a trade-off, asking to register will impact traffic, but leads will be of higher quality. My rule of thumb is the more specialized and higher-priced your product, the more important it is to at least capture an email address as early along the journey as possible.

One of the most obvious purchasing signals is abandoned carts. And even though these are by definition some of the warmest leads, many companies at best only send automated follow-up emails. That’s fine for $50 t-shirts. But for $3,000 equipment? A human touch might be that differentiation that gets you the deal.

Let’s go back to our example from real life. If your site meets the industry standard 8% add-to-cart rate, it will generate roughly 16,000 carts per month. It isn’t profitable to follow up on everything, so prioritization is key. Applying general filters—like business email only, cart value thresholds— and industry-specific filters that signal high intention —like uploaded CAD files (in online manufacturing services)— will be a powerful way to prioritize.

Step 3. Sales Execution, The Final Mile:

Continuing with our example, lets assume that after applying your filters and customer segmentation, you site is able to generate 300 highly qualified abandoned carts per day. That’s the blue ocean for your Sales Development Representatives (SDRs). Here’s a baseline of what a properly managed SDR organization should yield:

• Each SDR should call and email 100 leads/calls per day

• Convert 15% of their calls into sales opportunities

• Close 5% of their calls into actual revenue

Success here depends on two things:

• Speed: Follow up within minutes. (Just try filling out a form on an insurance site—they’ll call you in seconds.)

• Skill: SDRs need to nurture, not just pitch. That means that regular training, call shadowing, and coaching is critical. I was skeptical of AI coaching tools at first, but they have proven to be highly valuable.

The Right Tools for the Job

Finally, let’s talk about the tech stack that will help you maximize conversion and follow-up efficiency:

1. Heat Mapping

Tools like Hotjar and Crazy Egg track where users click, where they drop off, and what frustrates them. You can even record real sessions to watch actual user behavior. These tools help refine the customer journey and identify the behaviors of high-intent users, critical to prioritizing the right leads.

2. Online Revenue Benchmarking

Grips Intelligence benchmarks your e-commerce performance vs. competitors—average order value, conversion rates, customer demographics. It will help identify gaps and opportunities in your strategy, including performance by customer segments.

3. Sequencing and Outbound Calling

For SDR follow-up, outreach.io is gold. It integrates with popular CRMs and auto-generates daily call lists, tracks outbound calling KPIs, and even analyzes calls with AI. Initially, I thought the AI feature was a gimmick, but it’s helped my SDR managers identify coaching opportunities and improve results.

Final Thoughts

Your e-commerce site is not just for transactions. In many B2B businesses, it’s your best demand-capture tool. If you’re only optimizing for online conversion, you’re leaving 97% of your demand on the table.

Invest in tools. Train your SDRs. Treat your site like the demand engine it is—and you’ll unlock growth that most companies miss.

Value pricing is often seen as a silver bullet to boost profits — charging more without increasing costs. In many cases, it works. Take insulin: Visual Capitalist published a global comparison showing the U.S. on average pays $99 per vial, while Turkey pays just $3. The cost to produce it? Around $2. Regardless of the price controls or regulatory context, what stands out is how pharmaceutical companies have leveraged willingness to pay to maximize profit.

A more relatable (and less controversial) example is airfare. It’s common knowledge now that two people sitting side-by-side on a flight, getting the exact same service, may have paid wildly different prices depending solely on when they booked.

With examples like these, companies — especially in B2B — start to think, “If the airlines can do it, why can’t we?”

That’s where it breaks.

The Real Requirements of Value Pricing

Successfully implementing value pricing is not just about slapping higher prices on your products. It requires complex systems that can analyze a host of variables — competitive data, historical pricing, elasticity, segmentation — and generate actionable insights. And more importantly, the organization must be able to digest those insights into real initiatives, drive adoption, measure outcomes, and iterate. This smells like a cross-functional effort, involving GTM, finance, ops, and likely more.

Starting with systems: I’ve seen companies spend a fortune on pricing consultants (I could name a few…) only to receive a white paper full of elegant math models based on theoretical scenarios. Beautiful, academic — and utterly useless when you’re trying to make weekly decisions with messy, real-world pricing data.

This is what I call the small dog phenomenon at SMBs — a topic for a future post. But the short version: just like small dogs bark at much larger dogs without fear, SMBs try to implement enterprise-grade initiatives requiring enterprise-grade data, capital, and headcount.

Value Pricing, Scoped for Reality

There are ways to make value pricing work — if you scope it properly.

A few years back, I worked with a hardware company that launched a new B2B product for $5,000, with about 30% margins. They had an omni-channel GTM strategy, but couldn’t afford to offer distributors 25% incentives. They didn’t have dynamic pricing tools, but they did have enough operational capability to launch a bundling strategy: a premium maintenance plan, available only through distributors, that pushed margins to 60%. That was manageable — and effective.

Another example had the right idea but poor execution. A distributor with over 40,000 SKUs for contractor materials updated their pricing using competitive benchmarks and implemented auto-margin locks. Prices would go up with costs but wouldn’t adjust down when costs dropped. No review process. No alerts. So when inflation eased and competitors dropped prices, they got priced out of the market. Revenues plummeted. They’re now on the verge of bankruptcy.

It wasn’t the only mistake, but it was a big one. And it’s common. Many companies overestimate their ability to de-commoditize. If customers can easily compare your product, buy it online, and you can’t demonstrate clear differentiation, you won’t be able to sustain a price premium. One possible lever? Brand. CPG companies pull this off — we pay more for Unilever’s soap when “all soap is created equal.” But building brand equity takes years of consistent investment in marketing and customer experience delivery. And this is where we inch closer to The Sales Blame Game where the company expects sales to command a brand premium that marketing just started building. And when that fails? Reorg time.

Simplifying to Win

There are success stories, especially in services companies where deals are priced individually. A salesperson with deep knowledge of the customer can adjust pricing based on past behavior or perceived willingness to pay. But how do you scale that model when it’s all tribal knowledge?

This problem is actually more manageable. Instead of managing 40,000 SKUs, manage your top 50 customers. Another mistake? Trying to fix everything at once — every SKU, every channel, every customer. You’ll either fail outright or spend millions doing it. Instead, focus on long-term profitability for the 20% of customers who drive 80% of revenue. There are great tools — Salesforce, Hubspot and NetSuite add-ons — that can surface live reports on customer pricing and margin. And here’s the key: it’s okay to make pricing mistakes along the way. As long as you’re achieving healthy long-term profitability at the customer level, you’re on the right path. Perfect pricing at all times isn’t the goal — profitable relationships are.

With the right tools, processes, and training, your odds of success improve dramatically. Start by equipping a pricing expert with clear dashboards and well-defined metrics. Pair them with the enterprise sales team to craft customer-specific proposals that reflect real value. Then make it a company-wide effort — backed by a pricing steering committee that reviews outcomes regularly and drives continuous improvement. When pricing becomes a disciplined, cross-functional habit, not just a one-time initiative, the results are more likely to follow.

Final Thought

Value pricing is not for the faint of heart. When improperly scoped, it’s almost guaranteed to fail — especially for SMBs without the data infrastructure and human capital to support it. But with a tightly scoped strategy, cross-functional alignment, and a clear focus on your most valuable customers, value pricing can be a powerful lever to transform your margins and financial health.

There’s been no shortage of coverage around the new tariffs—but two critical points are being overlooked. First, the myth of their strategic value. Second, the devastating short- and long-term impact on SMBs—the backbone of the U.S. economy.

The Myth of Strategic Value

Tariffs can be a powerful tool when applied with purpose. For example, the U.S. imports the majority of its pharmaceuticals. If the global supply chain were disrupted—as it was during COVID—we’d face serious risks to public health. In cases like this, tariffs can be used to reduce dependency by making imports more expensive and encouraging local manufacturing.

But that only works when combined with investment. Take semiconductors: The Biden administration imposed tariffs on Chinese semiconductors, but also passed the CHIPS Act in 2022, allocating $280 billion to rebuild domestic semiconductor production. Even with that funding, progress is slow. In three years, only $33 billion has been awarded. Why? Because you can’t fast-track decades of lost expertise, supply chains, and infrastructure. Even with a flood of public money, it takes time. Still, that investment has catalyzed over $400 billion in private capital. South Korea’s Hyundai has invested $12.6bn to build an electric vehicle and battery cell plant, which took about two and a half years from deal signing to production.

Now Let’s Talk Bananas

The new round of tariffs? Not strategic. They’ve been applied broadly—across all imports—with no plan to build local capacity. So now, we’re in a situation where even basic commodities are being impacted.

Take bananas. Only a few U.S. states—California, Florida, Hawaii—have the climate to grow them. In 2022, bananas from Hawaii cost $1.14 per pound. From Guatemala? $0.34. That’s an $0.80 loss in value per pound if we try to grow them here instead of importing. Multiply that kind of inefficiency across the entire economy, and we’re talking trillions of dollars in destroyed value.

It’s like shopping at two grocery stores side-by-side, one cheaper, one more expensive—but being forced to buy from the more expensive one. That extra dollar you spend isn’t just gone—it’s a dollar you can’t put toward your mortgage, your retirement, or your kid’s education. That’s why as investors are figuratively forced to buy the expensive bananas, the pending tariffs have destroyed more than $6 trillion in economic value in just one week.

Tariffs and the SMB Death Spiral

Now let’s talk about who’s really going to pay the price: small and medium-sized businesses.

“Made in America” is one of the most misunderstood labels in the country. According to the FTC, a product must be “all or virtually all” made in the U.S.—but in practice, that means about 60% of components and final assembly are domestic. The rest? Imported.

In industries like automotive or industrial machinery, components can cross the border seven times before a final product is ready. Tariffs disrupt every one of those crossings.

Large companies will survive. They have procurement departments, supply chain redundancy, and negotiation leverage. Sure, they’ll take a hit in revenue, but they’ll restructure (hello lay-offs), cut costs, and ride it out.

Medium businesses? They don’t have that luxury. No leverage. No supply chain depth. No room for error. They’ll be forced to raise prices to offset costs—and lose customers as a result. Many won’t make it. And here’s the real kicker: SMBs account for 45% of U.S. payroll. When they slow down, unemployment follows. And with it, a recession.

The Bigger Picture

The best-case scenario? These tariffs are a negotiation tactic, and the administration will walk them back soon. Many CEOs are holding onto that hope.

But damage has already been done. Global trust in the U.S. as a stable, fair player is eroding—and trust is the fuel of investment.

We are in for a tough ride. And worse, it’s hard to see what lies on the other side. The free-trade world we’ve known—driven by western coalitions—is over. Something new is coming. We just don’t know what it is yet. But it doesn’t look promising.

So… how’s that working out for everyone? Is your current go-to-market data yielding a 7x ROI?

Data, Data Everywhere—And Yet…

Not long ago, just having data was the problem. Back in 2008, most companies juggled homegrown CRMs and clunky web apps. Salesforce had only 1.1 million users (compared to 250 million now), and if you needed customer data, you better have printed that spreadsheet before leaving the office. Fast forward to today, and collecting and integrating data is no longer the issue—cloud-based SaaS solutions and APIs have made sure of that.

So, what’s the real challenge? Using data to actually change behaviors.

When Data Alone Isn’t Enough

Let’s talk about Company ACME (names changed to protect the guilty). ACME wanted to shift from a transactional sales model (customers buy easily online from multiple suppliers) to a high-touch enterprise model (where sales interactions deeply impact revenue generation). Naturally, they had tons of data—leads, conversion rates, pipeline reports, win rates, industry breakdowns—you name it.

But here’s the problem: the data wasn’t influencing behavior.

Managers used it for inspection—to track progress, compare performance, and hold sellers accountable. But it wasn’t changing how the team approached the business. The data was like a dashboard on a parked car—nice to look at, but it wasn’t getting them anywhere.

Making Data Work: Cadence & Process

To fix this, we built a cadence of structured conversations where salespeople—not managers—used data to assess their business and plan their next moves. Two types of meetings proved invaluable:

1. Pipeline Reviews (Twice Weekly, 5 Min per rep)

   1. Focused on deals closing in the next 10 business days and early-stage high-touch conversations

   2. Everyone used the same auto-populated dashboard (no “my numbers are different” excuses)

   3. Regular use made CRM adoption natural—no more resistance to data entry

2. Portfolio Reviews (Biweekly, 15 Min per rep)

   1. Focused on long-term account planning rather than just chasing the next deal

   2. Tracked the top 20% of accounts driving 80% of revenue

   3. Tied pipeline to yearly revenue forecasts, forcing a proactive approach

Key Rule: The salesperson, not the manager, led the discussion—publicly. If someone came unprepared, it showed. And nothing drives accountability like a little peer pressure.

The Result?

Palpable behavioral change. The CRM was no longer an inspection tool; it became a decision-making engine. Salespeople took ownership, managers had better visibility, and most importantly, persevering after many awkward and tough meetings—the company unlocked consistent and predictable growth after a decade of stagnation.

The Takeaway

GTM data is incredibly useful—but only if you create the space and cadence to act on it. It takes effort to build a process that turns raw data into behavioral change, but once it clicks, watching your team evolve week over week is priceless.

So, next time someone throws a fancy dashboard at you, ask yourself: Is this changing how we work? Or just how we report?

The Distributed GTM Model: A Symphony or a Circus?

The distributed model, still popular in Europe, sticks to the classic structure: separate heads for Sales, Marketing, Customer Success, and other customer-facing functions. In theory, this allows each department to dive deep into its specialty. In practice? It often plays out like an orchestra without a conductor—everyone’s playing, but are they playing the same tune?

Take, for example, an inside sales team that depends on Marketing-generated leads. Without clear alignment, Sales starts pointing fingers at Marketing for delivering “bad leads,” while Marketing fires back that Sales isn’t working them properly. Each team has its own KPIs, but who’s actually measuring lead quality without bias?

I lived this exact scenario at MakerBot. At one point, Sales was complaining that Marketing was generating unqualified leads, while Marketing insisted that Sales was dropping the ball. We lacked a shared definition of lead quality, which meant neither side was fully accountable—and ultimately, the business suffered. This dynamic is incredibly common in distributed GTM models, where alignment falls to the CEO (who has plenty of other things to worry about).

Or picture a big marketing campaign that requires Sales to execute flawlessly. If Sales doesn’t buy in, the campaign might flop—not because it wasn’t great, but because no one made sure Sales and Marketing were rowing in the same direction. Sound familiar?

The upside? A distributed model allows for deep expertise in each area. But without strong cross-functional alignment, internal friction can slow progress, leaving customers caught in the middle.

The Centralized GTM Model: The Rise (and Fall?) of the CRO

Now let’s talk about the centralized GTM model. Most organizations that go this route do so under a Chief Revenue Officer (CRO), a relatively new role that started gaining traction around 2012. Back then, Paul Albright, Marketo’s CRO at the time, wrote an article calling the role the “CEO’s Secret Weapon”—and for good reason. A well-structured CRO isn’t just a glorified VP of Sales. Just as an orchestra needs a conductor to ensure the brass section doesn’t overpower the strings, a CRO ensures Marketing, Sales, Revenue Operations, and Customer Success are in sync—playing from the same sheet of music rather than competing for the spotlight.

I’ve personally seen this model work twice, especially during major transformations where change management and constant realignment were critical. Like a complex symphony, transformations require precise coordination, dynamic adjustments, and a leader ensuring every section is aligned. In these cases, the CRO role was instrumental (pun intended) in keeping different revenue functions in harmony—preventing the usual blame games and silos that can derail progress.

However, I’ve also seen how companies fall into the quarterly trap, slowly turning the CRO into nothing more than a VP of Sales with a fancier title. It’s like starting with a grand orchestra and ending up with a rock band that only plays hit singles—chasing short-term wins instead of executing a long-term composition. This happens when the CRO becomes too focused on quarterly revenue targets and loses sight of strategic initiatives, customer experience, and long-term market positioning. The end result? The alignment that once made the company’s GTM strategy powerful fades into discord, and the company reverts to the same fragmented structure it was trying to escape.

The key to making this work is having strong second-line functional leaders who can each act as expert musicians in their domain while trusting the CRO to keep the full composition on track. Otherwise, the risk is turning an orchestra into a collection of soloists—each talented, but ultimately playing different songs.

So… Which Model is Right for You?

Not sure whether to centralize your GTM under a CRO or keep it distributed? Here’s a quick gut check:

1. Do you need a long-term, multi-year roadmap to win in your industry? Yes or No

2. Is your company a market-maker rather than a market-follower? Yes or No

3. Can you afford strong second-line functional leaders? Yes or No

4. Is customer satisfaction highly dependent on post-sales execution (e.g., installation, onboarding, manufacturing)? Yes or No

5. Do customers frequently move across different teams in their journey with your company? Yes or No

If you answered “yes” to two or more, it might be time to consider centralizing your GTM. Of course, how you do it depends on your company’s culture, industry, and product complexity.

At the end of the day, whether you go with one Conductor or the battle of the bands, the key to success isn’t just the org chart—it’s making sure everyone is playing the same game, with the same goals, and marching in the same direction.

During my MBA, I loved tackling case studies. Of course, if they had been real businesses, most of mine would have folded before their first anniversary.

One case that stuck with me was about a group of Harvard MBAs who opened a gelato shop in Miami right after graduation. You can probably guess how that ended—melting away into bankruptcy. Their fatal flaw? They never bothered to analyze their breakeven dollars per square foot, a critical metric in retail. Long story short, they needed the same revenue per square foot as an Apple Store—at the time, the highest in retail.

The lesson? Even smart, well-educated people ignore the obvious.

Cue for applause. Enter Paid Media, stage right.

Act 1: The Google Search Era

According to Google Gemini, around 80% of companies run PPC and AdWords campaigns. And why not? Google claims an average ROI of 200%. Sounds great, right But is 200% actually good?

(Lights dim, ominous music plays.)

Enter Customer Lifetime Value (LTV), stage left, wearing an eye patch and twirling a villainous mustache.

Act 2: The B2B Math That No One Wants to Do

Let’s say you’re in a competitive B2B industry with well-funded players, where success is often defined by customer acquisition rather than profitability. Your average order value is $2,500, and the cost-per-quote in paid search is a hefty $300.

If your win rate is 30%, that means your customer acquisition cost (CAC) is $1,000 ($300 per quote ÷ 30% win rate).

With a 50% gross margin, you’re left with $250 in gross profit per order—just 10% of revenue. That’s before accounting for operating expenses. So, any order won through paid media is sold at a loss. And depending on how much of your business relies on paid media, your entire company might be running at a loss.

And yet, in this scenario, Google’s ROI calculation shows an eye-popping 833% return—while the business is still losing money.

Act 3: The “Trust Me, We’ll Fix It” Agency Pitch

Ask a paid media agency, and they’ll tell you their secret sauce—better targeting, demographic segmentation, superior bidding strategies—will magically solve your CPC problems.

In 20 years, I have yet to see that happen.

Instead, let’s go back to fundamentals. The two biggest levers in this equation are gross margin and customer retention:

1. Gross margin – Unless you’re in the software business with 80%+ margins, there’s usually not much wiggle room here.

2. Customer retention – This is where most sales and marketing leaders ignore the obvious.

How often will a customer realistically buy your $2,500 product or service? If the answer is “not often,” then you’re reacquiring the customer for every single transaction—driving up CAC and crushing profitability.

This is the gelato shop problem: No customer can physically eat enough gelato to match Apple’s dollars per square foot.

Final Act: The Critical Questions

If you’re relying on paid media, ask yourself:

1. Can my customer realistically buy enough of my product to cross the breakeven point?

   1. If your customers can’t eat enough gelato, your company will always operate at a loss.

2. Can my customer buy often enough so that I don’t need to re-acquire them?

   1. If they forget about you between purchases, they’ll start another Google search—and you’ll pay to acquire them again.

3. Can I effectively estimate LTV for customers acquired through paid media?

   1. What you can’t measure, you can’t improve. If you can’t even come up with a rough LTV estimate, your organization probably isn’t equipped to take a first-time customer to their third order.

Curtain Call

If you answered no to any of these questions, you should probably rethink your paid media strategy. It might be time to explore a better GTM approach—which we’ll cover in a future edition of this newsletter.

So, can you confidently say your paid media spend is profitable?

(Spotlight fades. Curtain drops.)

That’s why The Sales Calamity exists—not just as a blog, but as a community. This is a space for go-to-market professionals to share war stories, trade lessons from the trenches, and learn from each other. Sales can be a lonely game, but it doesn’t have to be.

So let’s swap insights, laugh at the chaos, and get a little smarter (and maybe a little saner) along the way. Welcome aboard!

Which got me thinking: Is sales an art or a science?

As with most things in life, the answer is it depends. Sales exists on a spectrum, ranging from pure science to pure art, depending on the complexity and price point of your product or service.

The Science of Sales

At one end of the spectrum, transactional sales are ruled by science. Winning in this space requires speed, discipline, and efficiency. Metrics like the number of daily touches per salesperson, follow-up cadence, and conversion rates become critical differentiators. Here, success is about process optimization—structured workflows, automation, and rigorous performance tracking.

The Art of Sales

On the opposite end, enterprise sales rely less on science and more on human connection. Success hinges on deep discovery, trust-building, and long-term relationship management. The personality suited for this type of selling often resists rigid structures, favoring intuition over spreadsheets. But dismissing science entirely in enterprise sales is a mistake.

Where Science Meets Art in Enterprise Sales

While enterprise sales may be less transactional, science plays a crucial role in focus, alignment, and scalability. Here’s how:

1. Pipeline Discipline: A structured pipeline keeps your sales team forward-thinking, planning not just for the deal in front of them but for the entire year. Without this long-term view, sustained growth becomes nearly impossible.

2. Regular Pipeline Reviews: Early on, these meetings can feel awkward—often focused on CRM inaccuracies rather than meaningful discussions. But over time, they become invaluable, shifting the conversation to deal blockers, differentiators, and next steps.

3. Cross-Functional Alignment: A well-maintained pipeline informs the rest of the organization about upcoming opportunities that might require special support from operations, product teams, or other departments.

Sales as Art Alone Is No Longer Enough

It is still truth that buyers may prefer doing business with people they like, but they also expect a professional sales process that instills confidence. In high-touch sales, the person on the buyer’s side is often putting their own reputation on the line by choosing you. A mix of art and science ensures they feel both comfortable and assured in their decision.

The best sales organizations master both—the human touch to build trust and the structured process to drive predictable success.

A 2010 Bain & Company study of 57 major reorganizations found that fewer than one-third led to meaningful improvements. That’s not surprising. Managing change is far more complex than maintaining the status quo. So if a company struggles with day-to-day execution, the odds of successfully navigating a major sales reorganization are slim.

This raises an important question: How often does a sales reorg actually solve the problem? And how often is it just a distraction from deeper issues—ones the company is either unwilling or unable to fix?

Diagnosing the Real Issue

If you’re wondering whether sales is truly the culprit behind stalled revenue, here’s a simple framework to consider:

1. Is your go-to-market strategy similar to that of your top competitors?

   1. If yes, you’re likely meeting customer expectations at a basic level.

2. Is your sales execution at least average?

   1. If so, you’re probably keeping pace with competitors. Sustained excellence is costly and difficult to maintain, so over time, most organizations settle into “average” execution.

3. Is your sales and marketing budget comparable to competitors’?

   1. If your rivals outspend you 10-to-1, no amount of creativity or hustle will close that gap. But if your spending is in the same ballpark, then competitive bidding should balance out over time.

If you answered yes to all three, then the problem likely isn’t your sales execution. Sure, there’s always room to optimize, but tweaks alone won’t drive transformational growth.

The Real Danger: The Sales Blame Game

This is where many companies fall into the Sales Blame Game—pouring resources into sales reorgs while ignoring deeper issues like product gaps, customer dissatisfaction, or operational inefficiencies. This cycle is especially common in mid-market firms with limited resources, where leadership focuses on what feels controllable rather than tackling the harder, systemic problems.

If you’re a sales leader in an organization unwilling to look beyond sales for answers, it might be time to consider your options. Breaking free from the Sales Blame Game requires bold leadership, a willingness to challenge assumptions, and sometimes, a change in direction altogether.