Strategic Market Segmentation

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

There are strong beliefs about "the right way to organize teams," and they are usually wrong. Functional organizations and single-threaded (cross-functional) teams optimize for different outcomes. In a functional org, work is decomposed by discipline. Google and Microsoft have historically organized this way. This structure excels at enforcing standards. Benefits include shared management, clear career paths, and built-in tension that can drive technical excellence. The trade-off is coordination cost. Delivering customer outcomes requires coordination across functions, which introduces friction, makes disruptive decisions harder, and can dilute accountability. Functional structures work best in stable environments where consistency and depth matter. Single-threaded teams bring key functions together around a product or outcome with clear end-to-end ownership. Amazon organizes around this charter with one leader per initiative. Their primary advantage is nimbleness. Decisions happen close to the work, people are encouraged to wear multiple hats, feedback loops are tight, and cross-discipline trade-offs are resolved within the team. Team members are expected to embrace blurry disciplinary lines. The downside is that without strong connective tissue, teams can diverge in practices and be rigid about ownership. Career development can also feel less clear for specialists without a strong functional home. The tension between these models can be a culture shock. Consider what happens when an acquisition gets "functionalized," or when a strong functional owner becomes a general manager. People raised in functional orgs expect clear role boundaries and discipline-first decisions. In single-threaded teams, they may struggle with ambiguous scope and broader accountability. Conversely, people shaped by single-threaded teams expect autonomy and rapid iteration, and may experience functional standards as bureaucracy. Many companies adopt some form of hybrid. For instance, Spotify is functional yet operates in "Squads." In practice, many functional teams are actually hybrids where one function serves as the primary unifying leadership for the others. Hybrids tend to be introduced when a functional organization wants to give a smaller team autonomy and accountability. The bottom line is to use the structure that fits the task. There is no single right structure overall, only the right structure for the problem being solved.

Learning from Airlines: Can Telcos Better Monetize Their Data? In both the airline and telecom industries, we navigate through a landscape characterized by high capital expenditures, stringent regulations, and a fragmented market. Airlines, however, have excelled in one key area: segmentation. By strategically segmenting passengers- first class, business, premium economy, and economy-they manage to extract varying levels of revenue and profitability from the same limited space on an aircraft. This segmentation isn’t just about seat preference; it’s about monetizing each square meter of an aircraft differently, depending on the passenger type. So, how can this apply to Telcos? Just as airlines maximize revenue per square foot, telecom operators could think about deeper segmentation in how they monetize networks. Currently, we see some level of differentiation—enterprise clients, business-critical services, and regular business users all receive varying levels of service and pricing. However, there might be an opportunity to dive deeper into this segmentation. What if Telcos could apply a more nuanced approach to data value? By considering not just who is using the data but how and why they are using it, Telcos could introduce more sophisticated pricing models. For instance, data used for mission-critical operations in a hospital could be valued and priced differently than data used by a small business for basic operations. This kind of deep segmentation could enable Telcos to not only better serve their clients but also maximize the revenue per gigabyte of data. Airlines have shown that a one-size-fits-all approach leaves money on the table. It’s time for Telcos to ask themselves: Are we truly maximizing the value of our ‘square meters’ of our networks? The answer could lie in a more finely tuned segmentation strategy.

Org Charts of the Future Don’t Look Like Org Charts at All. Once upon a time, functions sat neatly in their boxes. HR owned people. IT-owned systems. BUT .. here's what may happen in 2 years. Companies like Moderna are already experimenting with the next move, rolling HR + IT into a single role “Chief People & Digital” groups. Why? Because they are hedging that talent strategy is inseparable from tech fluency. And that’s just the beginning. So, here’s my Future Roles Forecast once AI is materially embedded into the operating model: BUSINESS 💥 AI Monetization Strategist – designs usage tiers, pricing models, API monetization. 💥 Head of AI Partnerships – builds trust-based ecosystems with providers, infrastructure, and academia. TECHNICAL 💥 Prompt Engineer – closes the gap between human intent and model execution. 💥 Synthetic Data Architect – builds edge-case datasets for safe, scalable validation. 💥 RLHF Lead – runs human feedback pipelines to keep AI aligned and safe. OPERATIONS 💥 Model Governance Officer – enforces policies around black-box decisions. 💥 AI Cost Controller – audits GPU spend, runtime economics, dollar-per-output. TALENT 💥 AI Workforce Planner – forecasts job impact, redeploys people, sustains morale. 💥 AI-Diversity Advocate – ensures underrepresented voices shape both data and design. Also, Cross-functional hot zones will emerge: 💡 Product ↔ Engineering (build vs. viable) 💡 Ops ↔ Tech (deployment vs. scalability) 💡 Talent ↔ Business (hiring vs. GTM) Recommendations: - Start with overlap. Early AI teams wear multiple hats. Split later, when load—not ambition—demands it. - Recruit “interface thinkers.” The ones who thrive at boundaries—product ↔ tech, ethics ↔ infra, adoption ↔ change. I think the new org chart isn’t lines and boxes—it’s interfaces and leverage.

Your CEO just approved another $100K for Google Ads. Meanwhile, your biggest competitor's CEO was quoted in three industry articles this month—and guess which company AI assistants are recommending? We're living through the biggest shift in business discovery since Google launched. 💥 Here's what's happening: When potential customers ask ChatGPT or Claude about solutions in your space, these AI models aren't parsing your ad copy or landing pages. They're referencing authoritative sources—trade publications, industry reports, expert commentary. And the executives who show up in those sources are the ones getting recommended. Think about the last time you saw a CEO quoted as an industry expert. A single quote in that trade pub you used to ignore is now worth more than months of paid search campaigns. Why? Because that quote becomes part of the training data that shapes how AI models understand your market and who they recommend. The math is brutal but simple: - Your $100K ad budget reaches people actively searching (maybe) - Your competitor's CEO quote reaches every AI query for the next several years Guess which investment has better ROI? Earned media isn't "just PR"—it's the new customer acquisition funnel. 💥 What this looks like in practice: → CEO spends 2 hours/month talking to industry reporters → Leadership team contributes expert insights to trend pieces → Company spokespeople become the voices journalists call for quotes → Executive thought leadership drives long-term AI visibility Your competitors are already having these conversations. The question is: will your leadership be part of them? The future belongs to companies whose leaders are industry voices, not just industry participants.

Cross-Functional or Siloed? Let’s Talk Data Teams. 🤝 When you think about Data Engineers, Data Scientists, ML Engineers, and Data Analysts, do you see a seamless orchestra—or isolated soloists? Let’s break it down, not just by title, but by impact: 🔷 Data Engineers – The Architects of Flow They don’t just build pipelines—they design the highways of data. → Think of them as the ones who ensure data is clean, structured, and accessible. Without them, the rest of the team is flying blind. 🔶 Data Analysts – The Business Translators They turn numbers into narratives. → Analysts are the bridge between raw data and business decisions. They ask the right questions and surface insights that drive strategy. 🟢 Data Scientists – The Pattern Seekers They’re not just coding models—they’re solving puzzles. → From hypothesis to experimentation, they rely on engineered data and business context to build models that predict, classify, and optimize. So, how do they all work together? The magic happens when these roles collaborate, not compete. Here’s what that looks like in action: ♐️ Data Engineers & Data Scientists → Engineers ensure the right data is available, while Scientists define what “right” means. It’s a feedback loop of clarity and precision. ♐️ Data Scientists & ML Engineers → Scientists prototype, ML Engineers productionize. It’s a handoff that requires trust, documentation, and shared goals. ♐️ Data Analysts & Everyone → Analysts surface trends that inform what models to build, what data to collect, and what business problems to prioritize. The Real Question: Is your data team cross-functional or siloed? Because here’s the truth: 🚫 These roles aren’t in competition. ✅ They’re interdependent gears in a well-oiled machine. If you’re building or working in a data team, here’s my advice: 🔹 Foster open communication 🔹 Define clear responsibilities 🔹 Encourage empathy for each other’s challenges 💬 What’s your experience been like? Have you seen these roles collaborate effectively—or operate in silos? Drop your thoughts below—I’d love to hear your take! 👇 #Data #Analytics #DataEngineering #ML #DataScience #Teamwork

Traditional Design vs Generative Design – A Shift in Engineering Thinking In the world of mechanical and aerospace engineering, design methods are evolving rapidly. The image above clearly illustrates the contrast between Traditional Design and Generative Design using an example of aircraft seat mounting brackets. 🔹 Traditional Design This approach relies on human intuition, experience, and established standards. Designers use basic geometric shapes and overengineer components to ensure safety, often leading to excess material usage and heavier parts. In the image, the traditional bracket weighs 1,672 grams, made with solid material and a blocky design to ensure strength. However, it lacks material efficiency and may contribute to increased fuel consumption in aircraft. 🔹 Generative Design This is an advanced, AI-driven design process. Engineers input goals (like weight reduction, strength requirements, material type, and load conditions), and the software generates multiple optimized design solutions. The result is often an organic, lattice-like structure that removes unnecessary material. In the image, the generatively designed bracket weighs only 766 grams — a 55% weight reduction — while still meeting performance criteria. 💡 Key Differences: Design Process: Human-driven vs AI-assisted Material Usage: Excessive vs optimized Shape: Simple, blocky vs complex, organic Efficiency: Heavier and stronger than needed vs lightweight and just as strong Generative design is not just a trend—it's a strategic shift toward sustainable, high-performance engineering. It helps industries like aerospace, automotive, and manufacturing to save weight, reduce cost, and innovate faster. This transformation is a perfect example of how technology is redefining the boundaries of what's possible in design and engineering. --- #TraditionalDesign #GenerativeDesign #MechanicalEngineering #CAD #DesignInnovation #AerospaceEngineering #LightweightDesign #TopologyOptimization #FutureOfEngineering #AutodeskFusion360 #EngineeringTransformation #ProductDesign #AIInEngineering

PMMs, Stop saying your product is "better" 14 ways you should differentiate 👇 Check out this masterclass by Scott Jones (Scott is the SVP Marketing at Agentsync) Let's dive in! 👇 Scott has been launching B2B products for 25 years. The biggest challenge for PMMs? Differentiation. You need to show EXACTLY how you're better. With concrete specific examples. 📌 First, understand why B2B buyers make changes: There are only 4 scenarios that drive B2B purchase decisions: 1. More for less: Better outcomes, lower investment 2. More for same: Better outcomes, same spend 3. More for more: Better outcomes, higher investment (rare) 4. Same for less: Same outcomes, lower costs The key? You're not just competing against other vendors. You're really fighting: - Status quo (manual processes) - Internal development - Alternative vendors Here's how to prove you're actually better: 1️⃣ Every claim needs THREE elements: 1. Defendable adjectives with metrics 2. Clear "from/to" state 3. Demonstrable capabilities 2️⃣ Scott shared 14 concrete ways to differentiate: The gold standard? "New and never achieved" Example: "First platform to fully automate account targeting, media execution, and paid optimization in real-time" But there are 13 others including: - More actionable - More comprehensive - More responsive - More compliant - More accurate - More predictable 3️⃣ You need THESE for enterprise deals - Steering committee sign-off - Multiple management layers - CFO approval That's why... Your differentiation must tie to financial outcomes like: "Company X reduced spend from $5M to $4M" "Company Y grew topline 15% YoY" 📌 TLDR for you PMMs; Stop saying you're "better." Start proving it with: 1. Customer validation 2. Financial outcomes 3. Concrete metrics -- P.S. What else would you add PMMs? (Make sure you give Scott a follow btw!)

💡 Insightful conversation with Penny Kozakos, Alison DaSilva & Paul Holmes on how companies should navigate stakeholder expectations in light of social & political activism. Thankul to work with smart people. 👀 My take below: 1️⃣ Issue Preparedness Companies must be better prepared before engaging in social or political issues. This includes having formal processes to evaluate potential backlash and the impact of their actions on their reputation and stakeholder relationships. 2️⃣ Core Value Alignment Companies must ensure that their engagement in societal issues aligns with their core values, mission, and business relevance. This alignment helps maintain authenticity and consistency. 3️⃣ Navigating Regional Differences Multinational brands must understand and respect geographic and cultural differences in stakeholder expectations. Tailoring responses to fit local contexts can prevent conflicts and be more impactful. 4️⃣ Use Data & Analytics Leveraging data and analytics is critical to guide corporate decision-making. Narrative intelligence can help brands anticipate issues, tailor personalized responses, and see around corners. 5️⃣ Balancing Risk & Reward Companies must learn to balance the risks and rewards of engaging in social issues. This involves careful consideration of potential backlash versus the benefits of being seen as a proactive, socially responsible brand. 6️⃣ Strategic Business Imperative Social responsibility should be viewed as a moral obligation and a strategic business imperative. Effective engagement in social issues can enhance a company's competitive advantage, employee satisfaction, and customer loyalty. 7️⃣ Dynamic and Ongoing Evaluation The corporate approach to social issues should be dynamic, involving ongoing evaluation and adaptation to changing circumstances and stakeholder expectations. This agility helps companies stay relevant and effective in their social engagements. Full video discussion below 👇🏼 https://lnkd.in/eG4uTPUC ~~~~~~~~~~~~~ #brandmanagement ‣ #brandreputation ‣ #dataanalytics ‣ #corporateresponsibility ‣ #crisimanagement ‣ PRovoke Media

One of the most common questions I get is, "How do we position a product with no differentiation?" I'm going deep on this in the newsletter this week. In my positioning process we start by listing the true competitive alternatives (including the status quo), then we list the capabilities we have that that alternatives do not. Then we translate those capabilities into a set of value themes. This "differentiated value" is the answer to the question "Why pick us over the alternative solutions?" When I hear "we have no differentiation," one of two things is true. Either: 1️⃣ There is truly no distinct value for customers or 2️⃣ Customers see differentiated value in your offering that some folks on the team do not understand. If we truly have no differentiation, then sales is a disaster. There is no reason to pick us so prospects do not. That is not a problem that positioning can solve - in B2B, we don't get to simply make up value. Buyers have to justify their choices to a buying team and saying "I was just into the vibes man" isn't likely to cut it. That said, you would be surprised at how often I see the second option, where at least one person on the executive team does not see any value vs the competition, despite the fact that every single day, prospects choose their solution over the alternatives. When we get the entire team together, it turns out there is a LOT of differentiated value for us to build a story around. What’s going on? Why do some folks on the team see clear value while others do not? I’ve seen this enough that I can point at a set of root causes: 💠 Value Blindness – the business is winning deals, but parts of the org aren't really sure why 💠 Product Illiteracy – the product has significant differentiated capabilities, but the value of those capabilities for customers isn’t clear to the marketing and sales teams 💠 An inside-out view of competition – members of the team are attempting to differentiate against many alternatives customers never consider or miss a key alternative (often the status quo). 💠 Loss obsession, Win ignorance – parts of the team are overly focused on lost deals (often a poor fit for the product in the first place) and under-exposed to won deals. 💠 Product Pessimism – the product team is overly focused on gaining feature parity with every alternative in the market and has become blind to the places where the product is ahead. 💠 Sloppy Segmentation – members of the team are trying to find differentiated value for an overly broad slice of the market, making it seem impossible. Notice the common theme? Parts of the organization understand the value and other parts do not. More on this and how to fix it in the newsletter this week (link in comments).