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Why are experienced PMs getting stuck in PM interviews they would have cleared two years ago? Because the interviews have changed. The old rounds are still there. Product sense, metrics, estimation and behavioural. But many PM interviews now add an AI layer on top. That layer does not ask you what RAG is or what an agent is. It asks what you would do when one breaks. That is the gap I talk about in the video above. Many PMs have learned AI. Far fewer have built with it. The new questions are written to tell the two apart. Below are the four types of AI questions we see most. For each one, I show what the question looks like, what it takes to pass, and why building helps. 1. Take-home assignments Some companies now send a take-home assignment. Here is a real one for a Product Manager, Platform Experience role. The problem was customer questions at scale. * You get 4 to 6 hours. If you reach hour seven, you stop and send what you have. * You build a small working prototype of the first thing you would build. Any tool is allowed, including AI tools. A rough script over the sample data, a clickable page or a working demo all count. * You write a PDF of up to 4 pages or 8 slides. It covers how you framed the problem, what you chose to build and not build, and how you would know it is working. * You have five days to send it. * Then there is a 45-minute call where they push back on your choices. The brief says they judge what the prototype shows, not how it is engineered. It also says a prototype that answers one question well and correctly refuses one it shouldn’t beats a polished mock of everything. To pass, you need to do five things. * Pick one narrow problem and make it work on the sample data. * Make it refuse questions it cannot answer, instead of making something up. * Write down what you left out, and why. * Say how you would measure it, with a few test questions and one or two numbers. * Defend every choice in the follow-up call. This is where building helps. If you have built AI products before, you know how much fits in 6 hours, so you keep the scope small. You know an AI tool will answer anything with confidence, so you add the refusal on purpose. Someone who has only learned AI often spends the 6 hours on a polished mock. The brief tells you that it loses. 2. RAG questions RAG is when a chatbot first fetches the right pieces of your documents and then answers from them. It is the AI topic we see asked the most. It rarely comes as a question about what RAG is. It comes like this. Chunking Our chatbot answers from a 200-page policy document. The answers are often half right. What would you change? To pass, talk about how the document is cut into pieces, called chunks. Small chunks lose context. Big chunks bring in text that has nothing to do with the question. Split by headings and sections, not by a fixed length. Keep tables in one piece. Let chunks overlap a little, so a sentence cut at the edge still appears whole in one of them. Reranking The right chunk is found, but it comes back eighth, and the model only sees the top three. What do you do? To pass, explain reranking. First, fetch more chunks, say 30, with fast search. Then a second model scores each chunk against the question again, and only the best 3 to 5 go to the model. Also mention the cost. Every answer gets a little slower. Root cause Our support chatbot started giving wrong answers last week. Find out why. To pass, split the problem into two. Take 20 wrong answers and check if the right chunk was fetched. If it was not, the problem is retrieval, so look at chunking, search and reranking. If it was fetched and the answer is still wrong, the problem is generation, so look at the prompt and the model. Then ask what changed last week. It is usually the documents, the prompt or the model. Coverage You have a Harry Potter book, and Snape is mentioned 400 to 500 times. Sometimes by name, sometimes just as he, sometimes as the Half-Blood Prince. How does your chatbot use every mention when it answers a question about him? To pass, first spot the problem. Normal search fetches only the top few chunks, and it misses the chunks where Snape is never named. Then fix the index. Work out which character every pronoun and nickname points to, and tag each chunk with the characters in it. Add a short line of context to each chunk before storing it. For questions about the whole book, answer from chapter summaries instead of hundreds of chunks. This is where building helps. When you build RAG, you watch a table get cut in half, and the answer go wrong. You see the right chunk come back eighth. You change the chunk size and see the answers change. In the interview, you talk about what you saw, not a definition. 3. AI agent questions An AI agent is a model that decides its own next step and uses tools, like search or email, to finish a task. Agent questions look like this. Agent or workflow Would you build this as an agent or as a fixed workflow? To pass, say that a fixed workflow is better when the steps are known in advance. It is cheaper, faster and easier to test. Use an agent only when the next step depends on what the user says or what a tool returns. Good answers start with the simpler option. Handoffs between agents Your system passes a task from one agent to the next. How do you know the handoffs are working? To pass, name the metrics. What share of tasks finish end to end. At which step tasks fail the most. Whether the next agent got everything it needed. How many tasks end up with a human. Time and cost per task. Stuck agents Your agent keeps calling the same tool again and again. What do you do? To pass, start with the trace, which is the step-by-step log of what the agent did. Then set a step limit and a clear stop condition. Write better tool descriptions, so the agent knows when each tool is the right one. Human approval What should the agent never do without asking a person? To pass, point to actions that cannot be undone. Refunds, payments, emails to customers and deleting data. Those wait for a human to approve. This is where building helps. Once you build an agent, you see it loop, pick the wrong tool, or pass half the information to the next step. You learn to read traces. That is exactly what these questions ask about. 4. AI product sense and metrics questions The classic product sense round now has an AI version. Should this be AI at all? Design an AI feature for a food delivery app. To pass, start with the user problem, not the model. Say when a simple rule would do the job. Then cover what AI adds, what each answer costs to run, and what the user sees when the AI is wrong. Launch decisions Your model is right 92% of the time. Do you launch? To pass, ask what the other 8% looks like. A bad song suggestion is fine. A wrong refund amount is not. Group the failures by type, decide what a wrong answer costs the user, and set the bar from that. After launch How would you know it is working? To pass, use two kinds of checks. Before launch, build a test set of real questions with the right answers marked, including questions it should refuse. After launch, watch how users react. How often they accept the answer, give a thumbs down, or ask for a human. This is where building helps. When you build, you write those test questions yourself. You watch a bot you thought was good fail more of them than you expected. After that, you stop treating one accuracy number as the answer. How to prepare for the new PM interviews Look at what every question above has in common. None of them asks for a definition. Each one asks what you would do when something breaks, and why. You can only answer that well if you have seen it break. That is why we built the AI Product Builder Cohort at Technomanagers around one objective. To make you job-ready to build AI products. * 12 weeks of real projects across RAG, AI agents, evals, prototyping and AI product strategy * 10+ projects you build yourself, with mentor feedback * One portfolio-ready AI product as your capstone * 20 hours of AI PM interview prep, including mock interviews on questions like the ones above Our 1st cohort ended with 10 job offers, 4 of them Senior AI Product Manager roles. This cohort is not for everyone. If you want a certificate, it is the wrong program. It asks you to build every week. That is why there is an application. We have started taking registrations for our 3rd Cohort. Currently, we are running an Early Bird Discount for our 3rd Cohort — Fill the Form if you are interested AI Learning Resources * 12 Weeks LIVE AI PM Cohort (Alumni now ship AI at Google/Microsoft, etc) * Self-Paced AI PM Cohort Recordings (65 Hours of Content) | Use AIBUILDER20 for 20% Discount This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

Everyone thinks Apple is losing the AI race. But here’s the truth… they’re playing a completely different game. To dominate AI, companies need four things: * Infrastructure, * Models, * Data, * and Distribution. Apple may be weak in AI models, but they are insanely strong in distribution. They sell 200+ million iPhones every year. And think about how we use phones. We ask simple things — summarise emails, find photos, send quick messages. So Apple’s strategy is simple: use our personal data to give hyper-personalised AI, and build the best interface on devices. and let others build the giant models. For this, Apple is simply partnering with Google. So Apple may never build the smartest AI… but they might build the most useful AI on our phone. Subscribe for more business strategy breakdowns. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

Resources * For more such Strategy and Tech Case Studies, you can download Strategies for Everyone and AI/Tech for Everyone * If you like our work, you can support us here About Me Hey, I’m Shailesh Sharma! I help PMs and business leaders excel in Product, Strategy, and AI using First Principles Thinking.For more, check out my Live cohort course, PM Interview Mastery Course, Cracking Strategy, and other Resources Remember when WhatsApp swore off ads? “No ads. No games. No gimmicks.” That was their promise. But fast forward to today, and that promise is broken. WhatsApp is rolling out ads and paid channel subscriptions — silently, carefully. A move over a decade in the making. Why now? Why did the world’s most private messaging app finally give in to the ad game? And what does this mean for you, for Meta, and for the future of messaging itself? Let’s unpack it — step by step — from tech constraints to business strategy, from privacy to platform power plays. Why Ads Now? To understand this shift, we need to zoom out. WhatsApp was never meant to be a business. In 2009, it launched as a pure, ad-free communication tool. And when Meta bought it for $19 billion in 2014, it still didn’t have a business model. That was intentional. The founders — Brian Acton and Jan Koum — believed ads would destroy the user experience. And for years, Meta respected that vision. But a few things changed: One, Meta’s revenue still depends — over 95% — on advertising. As they invest billions into AI, they need more revenue engines. Two, WhatsApp’s infrastructure has scaled massively. Over 2 billion users. 100 billion messages a day. That’s not cheap. Three, Meta found a workaround: Status and Channels. These aren’t encrypted chats. They’re public-ish spaces. So for the first time, Meta found a place in WhatsApp to show ads — without breaking encryption. This wasn’t a sudden shift. It was strategic. Years of building WhatsApp Business APIs, Payments, and now — Channels — set the stage. And today, that stage is ready. What It Means for Consumers Here’s the good news: ads aren’t showing up in your private messages. They’re only appearing in the Updates tab — between statuses and channels you follow. That’s by design. It’s low-friction, high-visibility real estate. But even this subtle change hits a nerve. WhatsApp always felt sacred — quiet, personal, unlike Instagram or Facebook. And this move shifts that perception. The fear? A slippery slope. If ads work in Updates, could they creep into inboxes next? Could privacy slowly erode? This is WhatsApp’s tightrope: monetize… without losing trust. What It Means for Advertisers For advertisers, this is a goldmine. WhatsApp’s open rates are insane. Users check it multiple times a day. No algorithm filters. No clutter. And now, you can place ads where users are already scrolling — Status and Channels. Even better? These ads can link directly into a chat. Imagine clicking on an ad and starting a conversation instantly with the brand. Yes, targeting is limited — Meta still can’t scan chats. But it can use metadata, language, location, and Meta’s cross-platform profile data. This creates a new type of performance ad: high intent, high context, and instant conversation. What This Means for WhatsApp’s Future WhatsApp isn’t just a messenger anymore. It’s evolving — into a business tool, a content platform, and maybe soon… a marketplace. Think about it. Payments via UPI. Business catalogs. Verified shops. Creator channels. And now ads. This is Meta’s WeChat moment — especially in India and Brazil. A vision where messaging, media, commerce, and discovery all live in one app. And the monetization levers? Ads. Subscriptions. Promotions. Just like every other platform — but in a more personal space. Meta’s Broader Strategy Zoom out further. This isn’t just about WhatsApp. It’s about Meta’s survival. With Apple blocking tracking, and TikTok stealing attention, Meta is rebuilding. They’re investing in AI chips, foundation models, smart glasses. And they need cash flow to fund all of it. That’s where WhatsApp comes in — a sleeping giant, now finally opening its doors to monetization. And it’s also where Meta’s super-app play comes together: Facebook, Instagram, Threads, WhatsApp — linked by identity, data, commerce, and of course… ads. Should WhatsApp Offer an Ad-Free Tier? Could WhatsApp offer a paid version? Ad-free. Maybe even with premium features? It’s possible. Telegram already does it. YouTube’s doing it. Even Instagram is exploring it in the EU. But here’s the tension: most users won’t pay. And Meta doesn’t want to train them to avoid ads. Still, in privacy-conscious markets, it could be a valuable escape hatch. Let’s not forget: WhatsApp’s founders left over this exact issue. Brian Acton famously tweeted, “Delete Facebook.” He later funded Signal — a true privacy-first app. Could a new player emerge today? Technically yes. But scale is hard. Network effects are real. More likely? A slow user shift to platforms like Signal or Telegram if trust breaks down. How Ads Actually Work So how do these ads work, technically? They’re not based on your chats. Instead, they rely on: * Location * Language * Channels you follow * Meta-wide profile data (if connected) Ads are likely preloaded into your Updates feed. Like Instagram story ads — but without personalisation from chats. Businesses can promote channels. Creators can offer subscriptions. And Meta gets clean, privacy-compliant monetisation. Meta is rolling this out quietly. India. Brazil. Spain. They’ve added toggles to control what you see. They’re watching feedback. And they’re betting most users won’t notice — or care. But the risks are real: * Regulatory backlash * User attrition to rivals * Ads creeping into core messaging experience If things go wrong, Meta can slow down. But if it works? WhatsApp becomes Meta’s most profitable platform yet. This is no small experiment. WhatsApp is shifting — from pure messenger to monetized platform. From no-ads… to maybe your next favorite marketplace. Is this the beginning of something powerful — or the end of something private? You tell me. Would you pay for WhatsApp to stay ad-free? Or are you okay with curated commerce in your updates tab? Clap, follow, and drop your thoughts below. Resources * For more such Strategy and Tech Case Studies, you can download Strategies for Everyone and AI/Tech for Everyone * If you like our work, you can support us here About Me Hey, I’m Shailesh Sharma! I help PMs and business leaders excel in Product, Strategy, and AI using First Principles Thinking.For more, check out my Live cohort course, PM Interview Mastery Course, Cracking Strategy, and other Resources This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

About Me Hey, I’m Shailesh Sharma! I help PMs and business leaders excel in Product, Strategy, and AI using First Principles Thinking.For more, check out my Live cohort course, PM Interview Mastery Course, Cracking Strategy, and other Resources This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

For More, check out our PM Interview Mastery Course( Crack PM Interview with First Principle Thinking like the top 1% ) * Module 1 — First Principle Thinking * Module 2 — Asking Great Clarifying Questions in PM Interview * Module 3 — How to Solve Guesstimates * Module 4 — Cracking Product Design Questions * Module 5 — Cracking the “Favourite Product Question” * Module 6 — Cracking Metrics & Product Execution Questions * Module 7 — Cracking Root Cause Analysis Questions * Module 8 — Cracking Growth Strategy & Pricing Questions * Module 9 — Cracking Market Entry & GTM Questions * Module 10 — Cracking Behavioral Interview Questions This has helped more than 3000+ candidates getting their Dream Job Technomanagers More about PM Interview questions and Mock Interviews | YouTube | Website | Tech & Strategy Newsletter * 50+ Real PM Interview Questions with Detailed Solution 2024 * PM Mock Interview, Resume Review and PM Resume Template * Download Tech for Product Managers This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

Ever noticed how the thumbnails on Netflix change? That’s not random. It’s personalization at work.With 130M+ subscribers across 190 countries, Netflix fine-tunes what you see using smart algorithms.One person might see a thrilling action shot, while another gets a dramatic close-up—all for the same show.Why? Because the right image can make you click. And more clicks mean more watch time.Netflix’s strategy isn’t just about content - it’s about hacking our attention with data-driven precision. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆 𝗶𝗻 𝗳𝗶𝗻𝗱𝗶𝗻𝗴 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝗿𝗼𝗼𝗺𝘀 𝗶𝗻 𝗼𝗳𝗳𝗶𝗰𝗲𝘀? Google has fixed one of the most annoying problems.No more echoes when multiple laptops join the same meeting in the same room.This is enabled by Google Meet’s new Adaptive Audio Technology. Here is how it works:1️⃣ Laptops near each other send high-frequency sounds, like a secret handshake, to sync up.2️⃣ It picks the best microphone based on who’s speaking and aligns all devices to avoid echoes.3️⃣ Google’s cloud cleans up noise and makes the audio clear for everyone.This is a great strategy for hybrid work. Companies needed quick solutions for meetings without buying extra hardware. Google made it so every laptop is a meeting room.Next time you’re in a Google Meet, remember: behind the simplicity is a smart strategy. Follow me for more business insights.hashtag#google hashtag#strategy hashtag#innovation hashtag#tech This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

Every time I walk into Zara I wonder how they have new designs on the display.Let's Decode their Business Strategy in [ 2 X 2 ]Zara positions itself as High Speed & Low Cost ( Mass Premium Fast Fashion ) 𝗛𝗶𝗴𝗵 𝗦𝗽𝗲𝗲𝗱, 𝗟𝗼𝘄 𝗖𝗼𝘀𝘁: Zara & H&M dominate.𝗟𝗼𝘄 𝗦𝗽𝗲𝗲𝗱, 𝗛𝗶𝗴𝗵 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Luxury brands like Gucci focus on exclusivity.𝗟𝗼𝘄 𝗦𝗽𝗲𝗲𝗱, 𝗟𝗼𝘄 𝗖𝗼𝘀𝘁: Discount retailers like Walmart prioritize basics.𝗛𝗶𝗴𝗵 𝗦𝗽𝗲𝗲𝗱, 𝗛𝗶𝗴𝗵 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Emerging niche players focus on micro-trends.These 3 things help Zara to win that Strategic Position:1️⃣ 𝗦𝗽𝗲𝗲𝗱 𝘁𝗼 𝗠𝗮𝗿𝗸𝗲𝘁 🕒 Traditional fashion brands take 𝟲-𝟵 𝗺𝗼𝗻𝘁𝗵𝘀 to launch a collection, Zara does it in just 𝟭𝟱 𝗱𝗮𝘆𝘀! Yes, they’ve mastered the art of agility—short production cycles, real-time trend tracking, and an ultra-responsive supply chain.2️⃣ 𝗙𝗢𝗠𝗢 & 𝗘𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗶𝘁𝘆 💰Have you ever bought something from a fast fashion store because you felt it might not be there tomorrow? That’s intentional. They produce 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗯𝗮𝘁𝗰𝗵𝗲𝘀, 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗙𝗢𝗠𝗢 leading to impulsive buying.3️⃣ 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 📈Fast fashion thrives on data, not guesswork. Brands like Zara & Shein constantly analyse posts from 𝗳𝗮𝘀𝗵𝗶𝗼𝗻 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝗿𝘀, 𝘀𝗼𝗰𝗶𝗮𝗹 𝗺𝗲𝗱𝗶𝗮 𝘁𝗿𝗲𝗻𝗱𝘀 and hashtags in 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲 using AI.𝘽𝙪𝙩 𝙝𝙚𝙧𝙚’𝙨 𝙩𝙝𝙚 𝙥𝙧𝙤𝙗𝙡𝙚𝙢This strategy comes at a cost 💵 - Fast fashion leads to overproduction, wastage and environmental issues. What do you think? Can brands balance 𝙨𝙥𝙚𝙚𝙙, 𝙘𝙤𝙨𝙩 and 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆? hashtag#FastFashion hashtag#Strategy hashtag#Zara hashtag#BusinessInsights hashtag#Sustainability hashtag#DataDriven This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

Why did New Coke Failed Miserably but Diet Coke is an Instant hit?Let's Decode the Business Strategy in [ 2 X 2 ]In 1985, Coca-Cola made one of the boldest (and worst) decisions in business history - they killed 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗖𝗼𝗸𝗲 and replaced it with 𝗡𝗲𝘄 𝗖𝗼𝗸𝗲.𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘦𝘥 𝘯𝘦𝘹𝘵 𝘸𝘢𝘴 𝘤𝘩𝘢𝘰𝘴.- Coca-Cola's hotline, which typically received about 400 calls a day, was flooded with up to 1,500 complaints daily 📞- Protesters poured New Coke into sewers. 💢- One guy was so upset, that he sued the company for emotional damages! 🗯️Within months, they had to bring back Classic Coke, branding it "𝐂𝐨𝐜𝐚-𝐂𝐨𝐥𝐚 𝐂𝐥𝐚𝐬𝐬𝐢𝐜."𝑺𝒐 𝒘𝒉𝒚 𝒅𝒊𝒅 𝒔𝒖𝒄𝒉 𝒂 𝒎𝒂𝒔𝒔𝒊𝒗𝒆 𝒇𝒂𝒊𝒍𝒖𝒓𝒆 𝒉𝒂𝒑𝒑𝒆𝒏 𝒊𝒏 𝒕𝒉𝒆 𝒇𝒊𝒓𝒔𝒕 𝒑𝒍𝒂𝒄𝒆 𝒂𝒏𝒅 𝒘𝒉𝒂𝒕 𝒄𝒂𝒏 𝒘𝒆 𝒍𝒆𝒂𝒓𝒏 𝒇𝒓𝒐𝒎 𝒕𝒉𝒊𝒔?𝚆̲𝚎̲𝚕̲𝚕̲,̲ ̲𝚒̲𝚝̲ ̲𝚠̲𝚊̲𝚜̲𝚗̲’̲𝚝̲ ̲𝚋̲𝚎̲𝚌̲𝚊̲𝚞̲𝚜̲𝚎̲ ̲𝚘̲𝚏̲ ̲𝚝̲𝚊̲𝚜̲𝚝̲𝚎̲.̲𝗡𝗲𝘄 𝗖𝗼𝗸𝗲 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗱 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗖𝗼𝗸𝗲 𝗮𝗻𝗱 𝗣𝗲𝗽𝘀𝗶 𝗶𝗻 𝗯𝗹𝗶𝗻𝗱 𝘁𝗮𝘀𝘁𝗲 𝘁𝗲𝘀𝘁𝘀.It failed because Coca-Cola underestimated the power of 𝗲𝗺𝗼𝘁𝗶𝗼𝗻 and 𝗻𝗼𝘀𝘁𝗮𝗹𝗴𝗶𝗮 and how deeply people connect with brands they love.Coca-Cola wasn’t just selling a drink - it was selling an 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒, a 𝑙𝑒𝑔𝑎𝑐𝑦, and a 𝑙𝑖𝑓𝑒𝑠𝑡𝑦𝑙𝑒. New Coke disrupted that brand identity by dismissing the intangible value consumers placed on the original formula.𝗦𝗼 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻 𝘄𝗲 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀? 𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝟮 X 𝟮. 𝗢𝗻 𝗼𝗻𝗲 𝗮𝘅𝗶𝘀: Preserve Brand Legacy vs. Compromise Brand Legacy.𝗢𝗻 𝘁𝗵𝗲 𝗼𝘁𝗵𝗲𝗿: Incremental Innovation vs. Radical Innovation.1. Incremental Changes (Safe Zone)2. Radical Changes (High Risk)3. Fail to Evolve (Irrelevant)4. Brand Dilution (Disaster)New Coke? It landed in the Disaster Zone—a radical change that compromised their legacy. What should Coca-Cola have done? What should Coca-Cola have done? They could’ve introduced New Coke as an option alongside 𝐂𝐥𝐚𝐬𝐬𝐢𝐜 𝐂𝐨𝐤𝐞 like they later did with 𝐂𝐨𝐜𝐚-𝐂𝐨𝐥𝐚 𝐙𝐞𝐫𝐨 𝐒𝐮𝐠𝐚𝐫.What do you think?Can innovation hurt a brand? Have you seen other examples like this? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com

In the world of Swiggy & Zepto be YuluHere's how Yulu became the unexpected champion and achieved EBITDA profitability: Rise of platforms like Swiggy, Zepto, and Blinkit has created a 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗱𝗲𝗺𝗮𝗻𝗱 for affordable last-mile delivery solutions. These days I see a bunch of food delivery partners delivering on Yulu in Bangalore. Yulu's electric vehicles offer flexible and cost-effective(KM-based pricing, free battery swaps). Many 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝘂𝘆/𝗿𝗲𝗻𝘁 a Scooter or Bike. That’s where Yulu comes in handy.Yulu has 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝗲𝗱 with major players like Zomato, Zepto etc. to help make 𝗴𝗿𝗲𝗲𝗻 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗲𝘀, strengthening its position in this growing market. On top of this, Yulu's success is also a result of its own smart strategies:- Yulu's 𝘣𝘢𝘵𝘵𝘦𝘳𝘺-𝘴𝘸𝘢𝘱𝘱𝘪𝘯𝘨 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 along with designing simple yet innovative app has a great role to play. - Yulu has focused on 𝘬𝘦𝘺 𝘶𝘳𝘣𝘢𝘯 𝘮𝘢𝘳𝘬𝘦𝘵𝘴 including Bengaluru, Delhi NCR, Hyderabad etc. with high demand for last-mile connectivity. Gradually it is expanding into other growing markets like Noida, Vadodara etc.- Yulu has improved its operations, focusing on 𝘶𝘯𝘪𝘵 𝘦𝘤𝘰𝘯𝘰𝘮𝘪𝘤𝘴 (break even in approx. 2 years), better vehicle utilisation and customer satisfaction. Tech & Strategy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Follow Priyanka Dalmia on Linkedin Follow Shailesh Sharma on Linkedin This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.technomanagers.com