Avoiding Common Professional Pitfalls

Everyone gets ICP wrong. What people think ICP stands for is 'Ideal Customer Profile.' But here's the problem: Most companies define it like this: → 200+ employees → Technology industry → Series B or later → VP of Sales is the buyer That's not an ICP. That's demographics and firmographics. I want you to think about ICP differently. ICP = Ideal Customer PROBLEMS. Your real ICP isn't a company size or an industry. It's the customers who have the specific problems you solve. I was recently speaking at a conference with 150 CEOs in the room. I asked them: "What problems do you solve?" Four or five of them answered. Every. Single. One. talked about benefits. Not problems. "We help companies scale faster." "We improve operational efficiency." "We drive revenue growth." Those aren't problems. Those are outcomes. Problems sound like: "Our reps are wasting 3 hours a day on manual data entry." "We're losing deals because our follow-up takes 5 days." "Our managers have no visibility into pipeline until it's too late." THAT'S the level of specificity you need. Here's the truth: There are plenty of 200-person tech companies that don't have the problems you solve. And there are 50-person companies outside your "ICP" that are DESPERATE for what you do. Firmographics are just prerequisites. They increase the likelihood of the problem existing. But the problem is the actual qualifier. When you take a problem-based approach: → Your prospecting gets sharper → Your messaging gets clearer → Your discovery gets deeper → Your win rates go up Stop defining ICP by company size. Start defining it by customer problems. This will change how you target, who you target, how you message and most importantly how quickly you can close.

Big Consulting is at an inflection point. This is how it all started going wrong: → Top talent rejecting endless pyramid climbing → Clients demanding outcomes, not billable hours → Increased scrutiny and fines eroding client trust → Governance paralysis preventing decisive action → AI shrinking consultants' information advantage → PE firms backing agile challengers, sensing weakness Each factor alone was manageable. Together, they are becoming existential. Here's a recap of how we reached this point: 1. The Regulatory Tightening For decades, consulting giants operated largely unchecked. That era is over: → PwC fined $700M (China audit failures) → McKinsey settled $550M (opioid crisis involvement) → EY fined $100M (CPA exam cheating) Integrity isn't optional when your product is judgment. Clients now question: If you can't govern yourselves, how can you govern us? 2. The Talent Rebellion The pyramid depended on ambitious graduates willing to sacrifice everything for partnership. That's changing: → Gen Z see consulting as temporary, not career-long → Ambitious climbers have PE & tech alternatives → Only 6% aspire to partnership You can't fix a broken value proposition with free yoga classes and wellness sessions, this isn't Severance. 3. The Client Revolution Tired of paying for inputs without seeing results: → US government cut $5.1B in contracts → Saudi Arabia froze major PwC contracts → CFOs insist on outcomes over hourly billing After decades of elegant strategies that failed in execution, clients demand skin in the game: Don't tell me what to do. Show me, and share the risk if it fails. 4. The Governance Trap The partnership model once drove success. Today, it blocks evolution: → EY's "Project Everest" failed due to partner conflict → KPMG struggled to consolidate 100+ units into 32 → Deloitte's major restructuring to streamline operations The paradox: Those who must approve change are those most invested in the status quo. 5. The AI Disruption Information asymmetry was consulting's competitive advantage: → GenAI automates research reducing analyst demand → 72% of corporations now have internal AI capabilities → Market research reduces need for costly global teams Clients are increasingly asking: Why pay premium prices for research readily available through AI tools? 6. The Challenger Invasion New rivals sense weakness: → Unity Advisory raised $300M from Warburg Pincus → Tech-first firms like Palantir & Invisble growing rapidly → Expert networks connect clients directly to specialists Global scale, brand, and legacy are no longer insurmountable moats. Firm's are approaching their "split" moment: → Platform Pivot (cut partner distributions, transform IP to systems) → Hybrid AI (preserve partnerships, automate back-office) → Status Quo (enhance existing models, watch share erode) The stage is set for NewCo (link below). Next week we will start covering what the future holds.

I analyzed 12 months of ABM campaigns that actually worked. Here's the data: Most Account-Based Marketing fails before it starts. After analyzing 12 months of successful ABM campaigns (and plenty of failures), I've identified the patterns that consistently drive pipeline. Here's what the data shows: 1. Timing matters just as much as content Accounts that received 3+ touches within 48 hours of showing buying intent converted 4x better than those that received the same content a week later. 2. The magic number is 6.2 (for this brand at least) The average closed-won deal had 6.2 stakeholders involved. Yet most ABM campaigns only target 1-2 personas per account. Expand your reach. 3. The "champion experience" is everything The accounts where we delivered a memorable experience to a single champion (personalized video, custom research, direct exec outreach) had 3x higher conversion rates. 4. Sales and marketing misalignment kills ABM Our most successful campaigns had sales activity within 24 hours of marketing touches. When this alignment slipped to 72+ hours, conversion rates dropped by 48%. 5. Personalization at scale actually works But not how most people do it. We tested 4 levels of personalization: - Generic (18% engagement) - Industry-specific (27% engagement) - Company-specific (42% engagement) - Individual + company-specific (63% engagement) 6. Direct mail isn't dead But swag is worthless (or at least it didn’t work for this audience 🤷♀️). Our highest ROI direct mail: Personalized research reports addressing the account's specific challenges. $250 spend → $45K in pipeline (average). 7. The "Double-Down Effect" When an account engages with ANY marketing touch, immediately increasing the frequency and personalization level produces a 3.5x lift in conversion rates. The companies getting ABM right understand it's not a campaign—it's a complete go-to-market strategy. P.S. I'm working on a new episodic ABM show in collaboration with Clay, so stay tuned 🤗

Over the past decade, I’ve interviewed hundreds of Product Managers, and 99% fail for the same reason: they bomb the case interview. Here’s a classic scenario I present: “If you were tasked with creating an instant grocery app, what would you do?” What happens next? Most candidates enthusiastically dive into frameworks, user flows, and feature ideas. They meticulously explain how they’d build it. That’s when I know—they’ve missed the point. ---------------------------------------------------- ‼️ The Problem‼️ I’m not looking for someone who just knows how to build things RIGHT. I’m looking for someone who first asks: Are we even building the RIGHT THING? Here’s the MAJOR difference: A BUILDER says: “Here’s the PRD, the user flow, and the timeline.” A LEADER asks: “Do people even want this product?” “What’s the size of the market opportunity?” “What problem are we solving, and why would users choose us?” A good PMs validate the idea before drafting a single requirement. They focus on impact, not just execution. ---------------------------------------------------- 🗒️ The Big Miss Many PMs focus on details—features, tech stacks, or UI—without asking the big question: Does this product even make sense? Without a clear understanding of the user, the market, and the business value, they risk building something polished that nobody needs. ---------------------------------------------------- Great PMs think strategically. They don’t just build the product—they ask why it should exist. 💡 Next time you’re in a PM interview, take a moment before diving into solutions. Step back, think big, and focus on solving the real problems. That’s how you stand out as a true product leader.

A company runs an A/B test. Version B wins—12% lift, statistically significant. Champagne. 🎉 Six months later, revenue is flat. What happened? They averaged over their customers. Rookie move. (I've done it too.) Version B: +20% for new users. But -8% for returning customers. New users outnumbered returners in the test, so B "won." Then the customer mix shifted. More returners. The "winning" variant was slowly bleeding its best users. This is Simpson's Paradox—when aggregate trends reverse at the subgroup level. It's not exotic. It's everywhere. Data-driven teams walk into this constantly when the first rule of being data-driven is "run the test and trust the average." The fix isn't more data. It's asking: for whom did each version win? Averages describe populations. They don't describe people. The most dangerous phrase in analytics isn't "we don't have data." It's "the data is clear." For those wrestling with weird A/B results—I see you! Ask who's in your sample before you pop the champagne.

Why great consultants crash in industry and as start-up founders (If you’re thinking of making the jump, read this first.) Since I left McKinsey, I’ve been lucky to live in both worlds: consulting-style projects and assignments very close to “operator” roles. And now I understand why so many brilliant consultants, people I’d trust with my life on a tough case, crash when they land in industry or become founders. 1. PowerPoint ≠ Reality In consulting, once the deck is done, the job is done. In industry, the deck is just the beginning. Now comes the hard part: aligning teams, fixing blockers, chasing timelines, getting it done. 2. Speed is uneven In consulting, 90% of the team is a Formula 1. In industry, it’s a mix: some Ferraris, some scooters. You move at the pace of the slowest. No way around it. 3. Course correction is constant In consulting, you pick a path and stick to it. Mid-project pivots are a nightmare, and you avoid them at all costs. Planning wins. In industry, changing direction halfway through is normal. You try, you learn, you shift, and you better be ready. 4. Feedback is slow (or doesn’t come) In consulting, the feedback loop is constant. You know within 48 hours if you nailed it or not. In industry? You might be underperforming for six months… and nobody says a word. 5. Impact > Insight Consulting rewards clarity, polish, sharp ideas. Industry rewards traction. Execution. Results. Every. Single. Day. A brilliant idea with no buy-in or follow-through? Worthless. 6. When you screw up, it hits your P&L In consulting, if you miss the mark, a partner or client tells you. You fix it. In industry, when you miss the mark, the numbers bleed. And suddenly, it’s your revenue, your margin, your team. It’s not better. It’s not worse. But it’s different, brutally different. And if you don’t adjust your playbook, the frustration hits hard. In my next post, I’ll share what I had to unlearn and adapt. But I’d love to hear from others: If you’ve made the switch from consulting to industry, what shocked you most? Let’s make this the real conversation no one speaks about.

As more companies embrace A/B testing, the bottleneck is no longer running experiments—it’s ensuring those experiments lead to trustworthy decisions. In this tech blog, the data science team at Booking.com explains how they scaled experimentation quality across the organization. Rather than enforcing rigid rules, they chose to preserve team autonomy while building the supporting systems needed to encourage better experimentation practices. The team’s solution followed a simple but thoughtful progression: process, metric, then tool. They first invested in community initiatives like Experiment Ambassadors and peer experiment reviews to build a shared experimentation culture. They then introduced an Experimentation Quality framework that evaluated every experiment across three dimensions—Design, Execution, and Decision—making experimentation quality measurable and easier to improve. Finally, they embedded those standards directly into their internal experimentation platform through features such as quality checks, power-calculation guidance, and stronger defaults that naturally guided teams toward better decisions. The goal was to maintain flexibility while making good experimentation practices easier to follow. This work highlights an important lesson: improving experimentation at scale requires more than statistical knowledge or individual discipline. Sustainable improvement comes from combining strong organizational processes, meaningful quality metrics, and tooling that reinforces good practices into the everyday workflow. When these pieces work together, teams can make more reliable decisions. #DataScience #MachineLearning #Experimentation #ABTesting #Analytics #SystemDesign #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gg8eX3Yv 

Don’t make these common mistakes in techno-economic assessments (and avoid misleading conclusions.) TEA is a powerful tool to assess the feasibility of emerging technologies. But even small mistakes can lead to misleading conclusions and poor decisions. Here are 5 key mistakes I’ve seen repeatedly—and how to fix them: 1. Overestimating Technology Performance Challenge: Assuming ideal or lab-scale performance when scaling up. Real-world conditions often bring inefficiencies. Fix: Use conservative assumptions, validate with experimental data, and conduct sensitivity analysis. 2. Ignoring Uncertainty Problem: Treating input values (e.g., costs, energy efficiency) as fixed leads to rigid, unreliable results. Fix: Perform sensitivity and scenario analyses to identify critical variables and explore best/worst cases. 3. Using Outdated or Poor-Quality Data The Problem: Relying on old data or inconsistent sources reduces the credibility of your TEA. Fix: Source data from updated literature, validated models, or credible industry benchmarks, and clearly document assumptions. If data is missing for new technologies, use proxy technologies and check uncertainties. 4. Oversimplifying Economic Analysis Problem: Focusing only on capital costs (CAPEX) while ignoring operating costs (OPEX), maintenance, or financing impacts. Or focusing on single metrics, like NPV. Fix: Include all cost components—CAPEX, OPEX, and life-cycle costs—and calculate key metrics like NPV, IRR, and payback period. 5. Neglecting Policy and Market Factors Problem: Ignoring factors like carbon pricing, subsidies, or fluctuating raw material costs can skew results. Fix: Integrate policy scenarios, market trends, and potential incentives to build a more realistic TEA. Techno-economic analysis is only as good as its assumptions and methods. Avoiding these mistakes will help you deliver insights that are credible, actionable, and valuable for decision-making. We’re going to discuss all these challenges with TEA and more during my workshop in Q1 2025. What challenges have you faced when conducting TEA? I’d love to hear your thoughts in the comments! #Research #ChemicalEngineering #Economics #Energy #PhD #Scientist #Professor

Lesson for Consultants - Don't Give Away Too Much in Consulting. Protect Your Value. Here's a lesson I've learnt over the years that I thought I'd share with you. It also cost my firm over K1 million in lost opportunities in 2024 alone. If you're in the consulting or advisory space, remember not to give away too much to clients during the initial exploratory stage where you're just having preliminary meetings and submitting proposals. This phase is about understanding the client's needs and assessing if there's a mutual fit for collaboration — not a free masterclass in your expertise. If anything, use this time to share success stories, highlight the outcomes you've achieved for other clients, and showcase the impact of your work. Avoid revealing your unique methodology or problem-solving frameworks too early. In my experience, many potential clients, unfortunately including ASX-listed corporations and even PNG government agencies, engage consultants to "pick your brain," extract ideas, and then never follow through with the engagement. Worse, if your proposal is detailed enough, they might implement your strategies internally — essentially receiving tens of thousands worth of consulting, problem-solving, and advisory services for free. Don't undervalue your expertise. Don't give away too much. Key Recommendations to Protect Your Value 1. Share Results, Not Processes Focus on case studies, testimonials, and measurable results rather than how you achieved them. I share outcomes, not my detailed methodology. 2. Limit Proposal Detail Provide a high-level overview of your approach but avoid detailed strategies or step-by-step solutions 3. Introduce a Discovery Phase Offer a paid discovery session where deeper insights and strategies can be shared in exchange for compensation. 4. Use NDAs When Necessary For sensitive or proprietary strategies, request a non-disclosure agreement before sharing insights. 5. Qualify Clients Early Identify whether the client has the genuine intent and budget to engage you before investing significant time. Also, make sure you are dealing with a decision maker, not someone who does not have the authority or influence to formally engage you. 6. Package Your Expertise Create premium reports, webinars, or workshops that can be monetized rather than giving insights away for free. Note that you can give away some services for free, but only if that is part of your strategy to secure more paid work from clients. 7. Set Boundaries in Meetings Politely steer conversations back to the problem and expected outcomes rather than delivering solutions. This is perhaps the most difficult to do, especially if you are trying to impress your clients with your subject matter expertise and you end up oversharing. Your expertise has value. Protect it, respect it, and get paid for it. 💡 #Consulting #BusinessAdvice #ProtectYourValue #Entrepreneurship #ProfessionalServices #SuccessMindset #Leadership #BusinessGrowth

A canteen sign caught my attention this week. 👇 Well-intentioned. Visible. Updated daily. In many ways, admirable. But there's a problem. A behavioural science problem. When anyone reads that sign, they do the maths instinctively. 45kg. 180 people. That means hundreds of people wasted food yesterday. Which means wasting food is simply what people do here. It's the social norm. And we are hardwired to follow norms. Robert Cialdini, Professor of Psychology at Arizona State University, calls this negative social proof — and Richard Shotton explores it brilliantly in his book The Choice Factory. When we communicate how widespread an undesirable behaviour is, we accidentally normalise it. The message designed to change behaviour ends up reinforcing it. It happens constantly in sustainability communication. And in safety. And in HR. And in finance. Anywhere people are trying to shift behaviour by leading with the scale of the problem. The fix is simple. Flip the statistic. Lead with what people are doing right. ✅ "Yesterday our diners ate 97% of everything they took. Help us make it 100% today." ✅ "97 out of 100 diners here take only what they need. Be one of them." Same situation. Completely different behavioural signal. Before you next communicate a sustainability message, ask yourself one question: am I leading with the problem or the norm I want people to follow? The answer might surprise you. Where have you seen negative social proof at work? Drop your examples below. 👇