James Harvey

Intercom’s new pricing page absolutely crushes it. I have been following Intercom’s pricing page evolution for over a year. They have had at least three to four renditions where they played around with... Should Fin be a separate box at the top above the regular plans? Should they use a grid to show all the feature details? How many plans should they show? How do they position their add-ons? Their current version is clean, clear, and follows a number of winning best practices we see from our database of A/B tests here at DoWhatWorks. These include… ✅  A clear pricing structure. They charge for resolutions, and they charge by seats. Straightforward. ✅ Persona-based subheaders beat out outcome-focused ones. You want to signal whether this plan is for solopreneurs/startups or for companies that need security functionality. The idea of the pricing plan subheader is just a very quick visual nudge to say, “This plan is for people like you”. Intercom does good here. ✅ Use straightforward (and consistent) CTA language. We all know what “Get a free trial” or “book a demo” means. The addition of the duration of the trial just adds to expectation-to-reality matching. ✅ Limit line items to ~5 per plan. There is no golden number, any more than there is a perfect number of characters on your header or subheader. But what we know from a lot of testing data is that variants that have closer to 5 line items tend to win at a high percentage versus those that have 10 or more. On top of this, qualitative user testing shows that having an excessive number of line-items is hard to digest, and users typically only walk away remembering one or two things per plan. ✅ Use hover effects to explain jargon. By default, I have no idea what a “Lite seat” is, but I hover over it and get the explanation. More brands should do this. ✅ Set expectations around add-ons. Intercom states right above their add-ons, “Included in your free trial. Add or remove anytime”. Prospects understand from this that they can test out the add-ons, and they will be added by default. Overall, they nailed a simple, easy-to-digest pricing page, despite having multiple pricing mechanisms (resolutions + seats) and add-ons. Give the team behind this page a raise.

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The fastest-growing SaaS companies don’t start with messaging. They start with reality. Not personas. (Seems like a joke but no shortage of personas out there like "Digital-First Dave" and "Cost-Conscious Carla") The best companies go straight to the source: their target market. What’s actually happening in their target customers’ world? I’ve seen it play out across 200+ buyer research projects at Wynter. The companies that nail positioning and marketing? They ask sharper questions: → What slows you down in your workflow? → What’s the trigger that gets you searching for a solution? → What would actually make your job easier? They don’t assume. They investigate. The quality of your decisions comes from the quality of your information. And the results show up fast because now you’re speaking to real problems, in real language, with real stakes. It’s not just tighter messaging. It's marketing that finally lands. Stop guessing what matters to your buyers, ask them. It's the only way to know, really. Use tools like Wynter to get insights straight from the source. Run a target customer survey with Wynter to learn what your ICPs want https://wynter.com

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Bolt-on AI is killing your GTM. And most won’t realise until it’s too late 👇 I’m speaking to more and more CEOs, CROs + Board Members. The story is the same: They’ve invested in AI. Results haven’t changed: 📉 Revenue growth has stalled 📉 Efficiency is down 📉 Teams drowning in busywork And the “fix” is always: → More tools → More dashboards → More headcount That’s not strategy. That’s life support for a broken GTM operation. 1️⃣ The problem no one wants to admit I’ve spoken to 100+ GTM leaders. They’re excited by AI’s promise but frustrated by the reality. The problem isn’t AI. The problem is bolt-on AI. SaaS-native vendors are slapping LLMs onto old tools - and calling it “AI-powered GTM.” You’ve seen it: → Summarise a call → Auto-write a follow-up → Get another report Fun for a week. Forgotten the next. Without the right foundation, AI’s a novelty - the last roll of the SaaS incumbent’s dice. What SaaS insiders whisper: → “Mergers” are survival plays (more coming) → Customers churning faster than replaced → Investors done funding ‘AI features’ without ROI → Leadership burnt out; talent leaving for AI-native 2️⃣ Why bolt-on AI fails GTM is messy, dynamic, contextual. Bolt-on AI doesn’t understand it. Every Agent needs: 👉 Context — ICP, personas, scores, usage, sentiment ❌ Without it → wrong priorities, wasted effort 👉 Intelligence — Next best move from history + live signals ❌ Without it → random activity, missed opportunities 👉 Memory — Short & long-term recall of what worked, failed, and why ❌ Without it → siloed GTM, repeat mistakes, no compounding gains Plus: tools (200+ GTM Agents & integrations) to act. 3️⃣ What works instead With GTM Intelligence Infrastructure: Raw GTM data → Context → Signal → Action → Learning Powered by the GTM Knowledge Graph - your live 🧠: → ICPs + personas tied to live data → Positioning, pricing, customer journey → Win/loss patterns, churn + expansion triggers → Sales process + value mapping → Scoring models + reasoning for propensity, health, engagement Always on. Always learning. Now 200+ GTM Agents can: → Research prospects 24/7 → Enrich accounts across GTM tools → Score risk & opportunity with precision → Orchestrate workflows → Learn from every loop The result: → Marketing stops wasting 99% of spend → Sales knows which deals to prioritise and why → CS sees risk + expansion before it happens → Leadership gains trust, confidence & visibility → Everyone contributes to a shared intelligence 🪦 SaaS-native GTM: Too many tools, reactive, no memory ⚡ AI-native GTM: Unified context, intelligent orchestration, automated execution, adaptive learning The companies winning in the AI era will have context, intelligence and memory baked into their GTM. If you don’t - your competitors will. 📩 Tired of too many SaaS tools with bolt-on AI? DM me. 💬 Building GTM for the AI era? Get in touch. 🔔 Follow for more on GTM & AI.

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$100M ARR in 8 months. Not a new funding round. An actual cash in bank milestone. Lovable just did what most SaaS companies spend a decade chasing. And they did it with 45 people. This changes some long-standing SaaS myths: 1. Hypergrowth isn’t about headcount. The idea that you need a 100+ person Sales team to scale is obsolete. 2. Brand is doing more heavy lifting. Not just the logo, but how quickly your product makes sense to a new user. Lovable didn’t just build features. They delivered an Aha moment in minutes. 3. Bad experience can't be taken for granted. If your signup flow, pricing, or onboarding feels like work, you’re losing. The bar has moved dramatically!

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SaaS Startups Can’t Afford to “Wing It” on their GTM   Great to have lunch with some fellow SaaS experts to talk about this very topic.   Why?   Because we've seen countless amazing startups fail to scale when it matters most.   Initial traction - and then the slow down hits - typically at around $2m ARR.   Taping into the brilliant SaaS minds of Evan Galasso and Josh Axler, CFA at Flow Capital, Seth London at Meltwater and Irene de Gooyer-Collins, CPA, CMA of Growth Partners, we hashed out best practices.   Go-to-Market (GTM) metrics are make-or-break for SaaS growth.   For founders, the stakes are simple: 1️⃣ You’re spending cash now on sales, marketing, and CS. 2️⃣ You need to prove that spend will return - not just in ARR, but in sustainable, cash-efficient growth.   The challenge? Too many teams think they know what’s working, but without clear GTM metrics, you can’t confidently answer: ❓ Are we acquiring customers efficiently enough to protect runway? ❓ Which actions are delivering ROI - and which are just burning cash? ❓ How quickly are we converting pipeline into revenue?   Whether you’re talking to an investor or lender, the fastest way to lose credibility is to not know your numbers- or to have them but not tie them to decisions.   The takeaway from our discussion? 📊 Track your GTM like you track your Burn📊 Every dollar spent should be tied to a clear hypothesis, measurable outcomes, and cash flow implications. The founders who win are the ones who can say: “Here’s what we tried, here’s what worked, here’s what didn’t, and here’s how we shifted our resources accordingly.”   That’s not just good finance, it’s how you de-risk your growth story.   Happy scaling 🚀

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Positioning mistakes cost more than product mistakes. Here’s what I'm seeing over and over again in SaaS: 1. 50-75% of your leads aren’t ICP? That’s not a lead quality issue — it’s a positioning quality issue. Bad inputs = bad funnel. 2. Killer UX doesn’t matter if buyers don’t get your category. Education comes before differentiation. Always. 3. Value-based pricing? Smart in theory. But if customers start gaming the metric, your incentives are broken. Behavior follows pricing — not the other way around. 4. More features ≠ more value. Without clear context, capabilities become clutter. Use frameworks like the Value Matrix to separate the noise from what actually drives willingness to pay. 5. Four tiers is often one too many. Three gives you a “Goldilocks” middle. Two forces trade-offs. (Okta recently simplified their tiers — and conversions improved.) 6. Don’t call it ‘Enterprise’ unless it acts like one. Slapping a label on your highest plan isn’t strategy — it’s theater. You need true enterprise readiness (support, SLAs, integrations, etc.). 7. Stuck between SMB and Enterprise? That’s a roadmap problem, not a sales one. If leadership pushes upmarket without resources to match, you’ll alienate both sides. Pick a lane. Commit. 8. Trying to be everything to everyone? You’ll end up irrelevant to most. Positioning isn’t about being better — it’s about being different enough that comparisons break down. Your product can be perfect. But if your positioning is off, it’s like shouting into a void. Get it right, or nothing else matters.

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Most CRO programs are backwards. Here's what I learned after tracking $50M+ in revenue optimization… Everyone's obsessing over A/B vs. Multivariate testing. But they're asking the WRONG question. It's not "Which test should I run?" It's "Do I have the foundation to test at all?" After auditing 200+ optimization programs, here's the uncomfortable truth: 90% of companies fail CRO before they even start testing. They skip the fundamentals and jump straight to tactics. Here's the systematic approach that actually works: 🟡 The CRO Foundation Framework Layer 1: Traffic Qualification → Under 10K daily visitors? A/B only → 100K+ daily? MVT becomes viable → Between? Sequential A/B wins every time Layer 2: Resource Reality Check → Team expertise assessment → Statistical significance planning → Timeline vs. impact prioritization 🟡 The Scale Decision Matrix Most agencies get this backwards: Low Traffic + Complex MVT = Wasted months Here's what elite teams actually do... Enterprise Scale (1M+ monthly): ↳ MVT for incremental gains (2-5% lifts) ↳ Test element interactions Growth Stage (100K-1M monthly): ↳ Hybrid: A/B validation → MVT refinement ↳ Sequential testing protocols Early Stage (<100K monthly): ↳ Pure A/B for radical changes ↳ Speed over sophistication 🟡 The $5M Mistake Pattern Running MVT without sufficient traffic. Result? → Inconclusive data for months → False confidence in losing variants → Massive opportunity cost The fix: MVT needs 16x more traffic than A/B. 🟡 What The Best Teams Actually Do They don't choose A/B vs. MVT. They architect testing systems: Phase 1: A/B for foundational wins Phase 2: MVT for interaction discovery Phase 3: AI-powered personalization This compound approach generated 32% conversion lifts in our portfolio companies. Because here's what most miss: CRO isn't about testing. It's about building systematic optimization engines. The companies crushing it in 2025 treat testing like investment portfolios: → High-probability A/B bets → High-upside MVT experiments → Diversified across user segments With iOS tracking changes, testing becomes MORE critical. But only if you solve attribution first. Your conversion rate is just a reflection of how well you understand your customers. And testing is how you systematically improve that understanding. === 👉 What's your biggest CRO challenge - insufficient traffic for meaningful tests, or too many variables to test systematically? ♻️ Kindly repost to share with your network 💌 Join our our newsletter for premium VIP insights. Link in the comments.

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I’m obsessed with no shows, and uncovered some very interesting data. We analysed all our SDRs, about 15,000 calls per week. Average connection rate 10% Average conversation rate 40% Average meeting booked 25% Simple math: Every 100 calls 10 people pick up 4 of those have a conversation of above 1 minute 1 of those normally book a meeting But not all meetings are created equal. Some have high no show rates, but why. So we then dug deeper into: Call duration Speaker time The callers with a high no show rate had an average call duration of 2 mins. The callers with high sit rates had an average call duration of 5 mins. No discovery was happening on the 2 min calls. Pick up > permission based opener > pitch > close The high call durations were Pick up > opener > discovery questions > conversation > tailored CTA close based on their pain point So what’s the problem and fix. The SDRs with lower call duration are not comfortable with the product and need to be further enabled. Other factors do play in like confirming invite on call, asking if they will attend. But 90% is enablement: Understanding the persona Pain points Current state Existing case studies Listening to other calls So going deeper on enablement with the SDRs with high no show rates, will provide an update in 4 weeks. Looking for higher call duration More discovery questions Less talking It doesn’t matter how good you are at calling, how well you handle rejection if you don’t know your stuff you will get found out.

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Designing self-serve onboarding that *actually converts*: ✅ Show value in 90 seconds. ✅ Kill friction, keep control. ✅ One clear win before trial ends. Most SaaS onboarding fails because it's built for demo calls - not customers. Your goal: → Strip out distractions. Less is more. → Guide every user to one aha-moment, fast. → Make it self-serve but not self-doubt: avoid "what do I do next?" moments. Checklist for self-serve onboarding: 1. Instant welcome. 2. Quick walkthroughs. 3. Show the core feature in action. 4. Progress bar: Give a finish line. 5. Trigger quick wins. 6. Remind users what they'll miss if they bounce. Tighter onboarding – happier users, higher conversions.

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