Fernando Trueba

One strategy consistently wins: Product bundles on PDPs. Here's why: 1. Bundle Pricing Psychology • Users perceive higher value • More likely to buy multiple items 2. Decision Making • Reduces choice paralysis • Pre-selected combinations simplify purchase 3. Revenue Impact • Higher AOV • Increased items per cart • Better conversion rates Our latest test: 7% lift in conversions $750K additional revenue Want more data-backed CRO insights? Follow me for weekly test results.

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After working with $3.5B ARR brands, here's the truth: Noobs and experts tackle CRO very differently. Here are some key contrasts: 1. Beginner: "Let's change the button color to boost conversions." Expert: "What user behavior data supports this change?" 2. Beginner: "More pop-ups will capture more leads." Expert: "How can we add value without disrupting the user experience?" 3. Beginner: "Our conversion rate is low. We need a complete redesign." Expert: "Let's analyze the data to identify specific pain points first." 4. Beginner: "Mobile optimization isn't crucial for our B2B audience." Expert: "Let's ensure a seamless experience across all devices and contexts." 5. Beginner: "Our test didn't work. CRO doesn't work for our business." Expert: "What insights can we gain from this test to inform our next hypothesis?" The key takeaway? Expert CRO is about asking the right questions, not jumping to quick fixes. It's about: - Testing methodically - Understanding user behavior - Continuously improving based on data Where do you fall on this spectrum? Are you taking a beginner or expert approach to CRO? Share your thoughts below! 👇 - - - Want more expert CRO insights? I've compiled our top 446-point CRO checklist to increase conversion fast based on 2,500 A/B tests 1. Like and repost this post 2. Comment "CRO TIPS" and I'll DM you the full list. (Follow me first)

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What's the cheat code to profitably scaling ads? Expand your top of funnel. 🚀 The bigger your reach at the top, the faster you’ll scale. Don’t stick to narrow tactics—cast a wider net and watch your growth skyrocket. 💥

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Have you ever looked for theory that can answer - how to approach investors - what kind of product to build first - how you’ll defend the company against competitors This new article from CDA partner, Kevin Maney extends Steven Johnson's writing from the book Emergence to #CategoryDesign. All #Startups and #VentureCapital teams should dig in. (link to article in comments)

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59 out of our first 100 customers were fellow Y Combinator companies. 🧡 Post-pivot, the YC network/community, and bookface (YC's internal forum) were mission-critical for us. Critical for so many reasons: 1) These companies bought our product before they should have. Shout out to Gabriel Spencer-Harper for literally buying our product 1-week before it shipped. 2) Allowed us to get initial feedback from people who really cared. 3) Gave us so many customer referrals. 4) Enabled us to show initial traction ahead of a seed-extension round. 5) Basically free CAC. YC has a lot of benefits, but for us, being able to sell into the community has been arguably the biggest.

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I loved Hannah Clark and Daniel Danker 's recent podcast episode. The best piece of advice was on scaling product organizations. Daniel mentioned that when scaling, he avoids the trap of more of the same and instead figures out what they should be doing as he scales. To paraphrase, what are the 10X ideas that they should incorporate? Check it out here: https://lnkd.in/guYMfMm3

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I distilled 𝟭𝟯+ 𝘆𝗲𝗮𝗿𝘀 of my go-to-market experience as a CMO/VP/Head of Product into this 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲👇 So, if you need help building a go-to-market plan, this resource is for you. I originally built this for myself for my consultation work. → I wanted a template that covered all of the key considerations in an effective and efficient go-to-market engine. → While also still being lightweight enough to be completed quickly (and no more than ~2 pages if printed out). Here's a summary of what the template covers: 𝗣𝗮𝗿𝘁 𝟭 - 𝗬𝗼𝘂𝗿 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 • 𝗠𝗮𝗻𝗶𝗳𝗲𝘀𝘁𝗼 - what you stand for, the change you're championing, why you should be trusted • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 - the urgent & important problem you're solving and how you're solving it better than alternatives • 𝗧𝗮𝗿𝗴𝗲𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 - segments, ICP and buying committee 𝗣𝗮𝗿𝘁 𝟮 - 𝗬𝗼𝘂𝗿 𝗕𝘂𝘆𝗲𝗿'𝘀 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝘆 • 𝗪𝗵𝘆 𝘁𝗵𝗲𝘆'𝗹𝗹 𝗯𝘂𝘆 - jobs to be done, buying triggers • 𝗪𝗵𝘆 𝘁𝗵𝗲𝘆 𝘄𝗼𝗻'𝘁 𝗯𝘂𝘆 - objections, fears, alternatives • 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 they trust - where they learn and make decisions 𝗣𝗮𝗿𝘁 𝟯 - 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴, 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 & 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 • 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 and 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 - per customer segment • 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 strategy - how you price vs the market 𝗣𝗮𝗿𝘁 𝟰 - 𝗚𝗼-𝗧𝗼-𝗠𝗮𝗿𝗸𝗲𝘁 𝗙𝘂𝗻𝗻𝗲𝗹 • GTM 𝗠𝗼𝘁𝗶𝗼𝗻 - sales-led, PLG, etc. • 𝗛𝗼𝘄 𝗪𝗲'𝗹𝗹 𝗪𝗶𝗻 - one sentence statement uniting GTM teams • 𝗗𝗲𝗺𝗮𝗻𝗱 Gen - how you'll generate demand • 𝗟𝗲𝗮𝗱 𝗚𝗲𝗻 - how you'll collect demand • 𝗦𝗮𝗹𝗲𝘀 - how you'll prospect, nurture and close • 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 - how you'll grow revenue 𝗣𝗮𝗿𝘁 𝟱 - 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗼𝗳 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 • 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀, 𝗧𝗮𝗿𝗴𝗲𝘁𝘀 and 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - for Product, Marketing, Sales and CS As I said, it is comprehensive. But as you can see below, it's all done in a lean package. ___ Want the FREE template? 👉 The 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲 𝗰𝗼𝗺𝗲𝘀 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 using a fictional company to help guide you through it. 1. CONNECT with me    ❗don't miss this step, I can't send you the post without :) ❗ 2. LIKE this post 3. COMMENT "GTM" And I'll share it straight with you :) Plus all my future training videos on it. ___ So why am I doing this? → Help better define what a GTM strategy consists of → Help others get a head start building theirs → Get your feedback to make the template better :) So what are you waiting for? ___ ✍️ Connect with me, like the post and comment "GTM" to get the template. ♻️ Reshare if you found valuable. #B2B #B2BMarketing #B2BMarketingStrategy #GoToMarket #Marketing #SaaS #Enterprise #BrandAwareness #DemandGen #LeadGen  #Sales #Data #ROI

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Excited to announce that Stigg has raised $17.5M in Series A funding! 🎉 Stigg is revolutionizing how software companies build and manage pricing models by introducing the first unified monetization platform for engineers. On a personal level, the story of Dor Sasson and Anton Zagrebelny is super interesting and inspiring, all the way from early ideation and co-founder search, to the exceptional product Stigg is today, and the exceptional team that makes this magic happen!

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Contrarian thinking from Gokul to win big. In my talks to VCs, many still comparing to SaaS era. Why they do so? Let’s compare another contrarian path in Enterprise AI too. Which Vaayu - AI for Finance & Sales is following. Eg Compare trajectories of Enterprise AI based on outcomes vs SaaS. End Target to reach $100m ARR. 1. Annual Contract Value Enterprise AI - $200k per client SaaS - $20 x 12 - $240 2. Upsell possibility Enterprise AI - $1M per annum SaaS - $240 per annum 3. Defensibility Enterprise AI - Strong as u integerate at multiple touchpoints by being expansive SaaS - Many new players will enter Why VCs still endorse SaaS? I dont contradict them - afterall they have to get their investors & LPs onboard . It’s easier for most VCs to sell SaaS to their investors cz 1. Risk Enterprise AI - More upfront technical risk, more capital required initially. SaaS - Less capital in beginning, gives impression of Product Market fit early. 2. Sales Cycle Enterprise AI - 12-18 months SaaS - 1-10 days 3 Past Validation Enterprise AI - Completely new. Very few things to compare with SaaS - VCs say $1m has been inflexion point in past. So they want to invest at this sweet spot. Take away- For low cost SaaS - real test will be after spending $10-$20m. It may give impression of product fit early. Path to $100m ARR will be extremely tough. Flown waters never come back. Referring past is risky when AI is changing everything. For enterprise AI, it’s contrarian, new. It’s steeper in beginning, path to $100m is clear very soon with $3m spend after initial steep is covered. Great returns are always on contrarian paths. Not what everyone follows. Achal Mehra #enterprise #ai #stsrtups

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Optimizing growth: the “leaky bucket” isn’t an early-stage retention problem Product managers and data analysts tend to focus on optimizing the user onboarding funnel, or FTUE, more than other segments of the user lifecycle. As tempting as it is for a product team to focus a disproportionate amount of its energy on optimizing the onboarding funnel, ignoring the elder period of the user timeline is a mistake. In reality, user churn is more expensive later-stage than during the onboarding process: a user who has been in a product for a year is, by definition, more engaged than one who just joined. Losing the user with a year of tenure is more damaging to the business than losing the early-stage user because of that proven affinity and acquired behavioral data.

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This is a great read by Sachin Rekhi that outlines the deliberate startup methodology. If you want to learn more - join me while I facilitate his Finding PMF course where we cover the methodology in depth, including case studies. Link to the course in the comments.

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Tech companies are always tempted to create a new category. After all, what they're building is different & unique... right? Not so, argues positioning rockstar Anthony Pierri 🎸 "You should almost always anchor your product to an existing category," he told me last year. James Evans, co-founder and CEO of Insight-backed CommandBar, learned this lesson firsthand. Creating a category propelled CommandBar to a $19M Series A, but it meant building on “hard mode”. Shifting to “easy mode” — disrupting an existing category — helped CommandBar double their growth rate. Check out the full story in Growth Unhinged: https://lnkd.in/evxjWjfv The biggest pitfalls with category creation: 1. No competition. This meant CommandBar wasn't tapping into the demand that already existed -- and instead had to create entirely new demand. 2. Low product understanding. "We explained what we did—a lot. We’d spend the first 20 minutes of a discovery call getting people to understand the fundamentals of our product." 3. Mixed buyer profile. "We sold to different buyers. Sometimes the founder, sometimes the CTO, sometimes a PM, sometimes an engineer with a hackathon project." The TL;DR: you (probably) shouldn't create a new category. Disrupt an existing one instead. #marketing #positioning #messaging #categorycreation #startup

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A marketing measurement perspective that I don't see represented often is that, for a substantial proportion of advertisers, last-click really is optimal. If a fairly new mobile app with low TAM penetration scales exclusively on Facebook, how much actionable insight is gained by introducing complex measurement machinery? While I'm certainly guilty of advocating it, the viewpoint that more sophisticated measurement infrastructure is always better strikes me as overly generalized. In certain contexts, orchestrating experiments or building, tuning, and deploying probabilistic models probably won't meaningfully impact advertising efficiency. And in those specific (again: not all!) contexts, with limited data and a homogenous (possibly, single-channel) channel mix, reducing interpretability and overhead might do more harm than good. Creating complexity where last-click may perform just as well as an ensemble of methodologies doesn't actually optimize the bias/variance tradeoff -- it may not reduce bias sufficiently to compensate for a large increase in variance.

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Apollo might’ve just changed outbound forever. And we may see a product war in real-time because of it. This story doesn’t start with Apollo though. It starts with Retention—the tool for ecommerce sites that helps collect first-party data upon site visits. Adam Robinson, their CEO, bootstrapped the company to $20M ARR in under 4 years. Earlier this year, they launched a B2B product, properly-named RB2B (Retention B2B). The idea’s the same—collect first-party data from site visitors so you can retarget them with outbound after the fact. Things were going well. Word was spreading and people were using the product. But then Apollo ruined the fun. A couple weeks ago, seemingly out of nowhere, the launched their own Website Tracking feature. It’s a direct competitor to RB2B. While I can’t speak to the product’s effectiveness, I do know Apollo’s user base is massive—so adoption shouldn’t be an issue. Now what? Even if RB2B builds the better product, can they beat Apollo’s baked-in distribution? Time will tell, but RB2B doesn’t seem too worried. 2 weeks after the post, Adam posted that they’ve reached $500K ARR, with only 1.3% of free tier users converting to paid. There’s much more to come there. My takeaways: It’s crazy that an entire product for a decent-sized player can be a feature for the big guys. RB2B isn’t anywhere near done. I bet you see more feature announcements and pricing changes. I feel like this is just the beginning. I also wouldn’t bet against Adam Robinson. What are your thoughts?

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How We Helped Kiehl's Since 1851 Boost Revenue by $1.6M Through CRO. Kiehl's faced a common ecommerce challenge: increasing conversions while minimizing acquisition costs. Our "Conversion Blueprint" process uncovered 100s of optimizations. ✅ Reduced abandonment ✅ Increased purchases ✅ $1.6M in additional revenue 👉 Check out the full case study below.

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This is our first major new product launch at UserEvidence. Introducing UE Research Content. The feedback from customers has been incredible - incredibly disruptive to the traditional model of expensive and slow Analyst research. Thought leadership content is crucial for building awareness and moving B2B prospects down the funnel. But here's the problem: 71% of decision-makers say less than half of thought leadership provides powerful insights (Brighttail). Original Research - e.g. the "2024 VENDOR State of INDUSTRY Report" with substantive, interesting, survey data and industry trends projections - is the way to go, and is consistently a high-performing and wide-reaching asset type that works well in Content, PR/Marcomm, Analyst Relations, and Demand Gen. But creating these original research reports is a painful, slow, expensive process, regardless if you're doing it internally, or working with an agency or Analyst firm. - source a survey panel of 500+ industry professionals - come up with good questions that pain the industry narrative/direction you want - analyze the data and write copy about they key themes and takeaways - hand it to design to turn into a designed report - atomize the report into snackable content It's par for the course for these reports to cost $60+ ($100k+ if it's Gartn3r or Forre$ter ;) - and take 4-6+ months. UserEvidence Research Content enables marketing teams to quickly and cost-efficiently (like 1/3 of the cost and 1/2 the timeline) create substantive and thought provoking long-form industry trends reports - "2024 State of X" reports, Buyers Guides, and more - and use the UserEvidence Platform to atomize and create derivative short-form content and social content from the underlying research. In the words of our first UE Research Content client (who has now done several projects with us), Immuta, "Our newest report launched only two weeks ago. We've already gotten picked up by three media outlets, are capturing leads, and the sales team is leveraging stats in their presentations" + "We couldn't be happier with the results from UE Research Content, the content is THAT good." Check out the blog post for more info, and to see example Research Content created by Immuta and others. Boost! 🚀 #thoughtleadership #contentmarketing

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Shopify is organizing the ranks to continue its move towards the enterprise via its System Integrator strategy 🏗️ This weekend, Scott Ward (former Shopify Global Global System Integrators Strategy Lead) announced that he will now focus exclusively on their Accenture relationship. Right now, Shopify is an "emerging partner" for Accenture, but Scott mentioned a goal of getting to a "Business Group" by 2028 which means $1B+ of services associated with the platform. As a $64B business, this is still fairly small, especially compared to Accenture's Salesforce group, which has over 50,000 professionals working on it (though that involves MUCH more than commerce), but it's certainly a move in the right direction. I'd imagine the last few years have been building the foundation and framework for Shopify to work with the GSI ecosystem. Now it's time to start focusing on specific partners to nurture and transform those relationships from the "innovation" bucket to the core business unit bucket. Historically, large commerce tech stacks (think SFCC) have been co-managed with support from large 3rd party players like Accenture, who build entire practices around these legacy platforms. While Shopify's roots are in the "solo-preneur can manage everything on our simple platform" segment, as they start to support larger, more sophisticated commerce operations, these system integrators will start to do more with the platform. One of the most interesting parts of this shift will be the "build vs. buy" decisions these GSI's make. There are incredible upmarket Shopify agencies like Domaine that have built enterprise-level expertise that might be a logical tuck-in as these GSI's try to establish themselves in the category. I'm sure some will go this route, and others might build from the ground up internally. No matter what, this is certainly a good thing for Shopify and more supporting evidence of their momentum moving into the enterprise!

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🪂 Dropping a new resource we’ve been working: 📶 Signal guides But first, a little story… It all started with a spreadsheet. A few months ago, we shared a simple Gsheet of 💯 GTM signals. (sheet here: https://lnkd.in/gABzNVym) That resource went “viral” (well, for B2B content that is). ⦾ It’s been shared across many newsletters. ⦾ It’s be reposted and shared by SMEs. ⦾ It’s been downloaded 1000s of times. ⦾ And, whenever I jump into the sheet, there always seems to be a handful of anonymous animals poking around in there. BUT For as much value as this asset offers, it still leaves a lot to be desired. ⦾ What signal(s) should we start with? ⦾ What signals should we prioritize? ⦾ How do we capture that signals we’re blind to? ⦾ How should my team go about taking action? ⦾ What should we say in our outbound messages? +++ So today I’m thrilled to share the next phase of our 100 GTM signals sheet: 🧭 Go-to-market signal guides — https://lnkd.in/gRpqA2FV *What is it?* We’ve started with 10 guides that expand on the signals from the original spreadsheet. In each, you’ll find: ⦾ Meta data context like signal strength, type, and relevant GTM motion ⦾ Brief description of why the signal matters and where it originates ⦾ Questions to ask that will inform your outbound approach ⦾ Step-by-step playbooks to run plays via Common Room ⦾ Templates to copy/paste into your outbound tool *What’s next?* We’ll be rolling out guides for the rest of the 90 signals over the next few months. We also plan to make them even better with videos and more playbooks. These will all be ungated, but you can always subscribe to get notified when the next new guide is available ;) === HT Eric Wendt & Josh Lind for taking a simple concept and running with it :)

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This 2% conversion projection is brought to you by ‘Optimism Inc. Let’s talk financial projections. I build mine grounded in real numbers, tracking down to every API call, because after years in metric land, I know where actual conversions come from. And I’ve seen it too many times: someone tosses in a '2% conversion' assumption, but when you do the math, that number should be closer to 0.3%. Yet with that magical 2%, suddenly we’ve got a gold mine… until reality hits, and it’s more like, 'Oops, things aren’t as rosy as we pitched. Time to dilute and hit another round.' Investors, are you really swayed by unicorn dust? Wouldn’t a real, grounded plan be worth more than another 2%-of-something illusion? Let’s end the hockey-stick fantasies and get back to what’s real. #RealMetrics #NoMoreHockeySticks #FinancialReality

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