𝐈𝐧𝐝𝐢𝐚'𝐬 "𝟏𝟎-𝐏𝐢𝐥𝐥 𝐌𝐢𝐧𝐢𝐦𝐮𝐦" 𝐓𝐚𝐱: 𝐖𝐡𝐲 𝐈𝐧𝐝𝐢𝐚’𝐬 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞 𝐏𝐚𝐜𝐤𝐚𝐠𝐢𝐧𝐠 𝐍𝐞𝐞𝐝𝐬 𝐚 𝐑𝐞𝐬𝐞𝐭 I visited my local chemist recently for a 3-day course of medicine. The strip had 10 pills; I needed 6. The chemist refused to cut it. The reason I asked? "If I cut it, the next customer won’t buy it because they can’t see the expiry date." He’s right - but only because our packaging design is stuck in the past. This isn't just a minor inconvenience; it’s a systemic failure that leads to MASSIVE MEDICAL WASTE and an unfair "waste tax" on every Indian household. 📈 THE NUMBERS TELL A STORY: The Indian pharma industry is a global powerhouse. In 2025, the domestic market grew to ₹2.4 lakh crore, with top companies enjoying operating profit margins of 25% to 32%. When an industry is this profitable and growing at 9-11% YoY, the oft-touted argument that "retooling packaging is too costly" loses its sting. Improving packaging isn't a cost - it’s a basic requirement for patient safety and affordability. ❌ THE PROBLEM: In India, manufacturing and expiry details are usually printed in a single block on one end of a strip. Cut the strip, and you lose the "source of truth." 💡 THE "ZERO-WASTE" SOLUTIONS: (Common elsewhere, missing here) 1️⃣ Vertical Repetitive Printing: Regulators (CDSCO) should mandate that expiry and batch info be printed across every single blister cell, not just once per strip. 2️⃣ Unit-Dose Perforation: Designing strips that are pre-perforated into single, fully-labeled units. You buy one pill; you get the full data for that one pill. 3️⃣ Micro QR Codes: Every pill pocket could carry a 2D data matrix. A quick scan by the consumer verifies the batch and expiry instantly, no matter how the strip is cut. 🎯 THE BOTTOM LINE: We are the "Pharmacy of the World," yet we are forcing our own citizens to buy 40% more medicine than they need just because we haven't updated our printing standards. Pharma companies have the margins to absorb this transition. It’s time for regulators to move from "bulk-first" to "PATIENT-FIRST" packaging. What do you think? Is it time for a mandate on unit-dose labelling?

Buyers are showing up to sales calls more informed than ever. That means sellers need to show up more prepared than ever. In the past, buyers would reach out to sales when they still knew very little about a product. They wanted to learn more about the features, pricing, and how it compares to competitors – and they expected the salesperson to provide that information. Today, buyers are gathering all that information (and more) long before they talk to sales. They’re reading review sites like G2. They’re scrolling communities like Reddit. They’re watching product walkthroughs on YouTube. Most significantly, they’re doing deep research with LLMs like ChatGPT, asking questions like “Is Product A or Product B better for my business?” Now, when a buyer gets on a call with sales, they expect more than basic information. Instead, they're looking for: Detailed examples of how other companies in their industry are using the product. Custom demos that show how the product works in their specific use case. Clear plans for how the product will be implemented and adopted. Here’s the good news. Just as buyers use AI to learn more about products, salespeople can use it to learn more about prospects. If I were in sales again, I would: 1. Use an AI assistant to do advanced research about your prospects before every call. 2. Use AI to find the best examples of similar companies seeing success with your product. 3. Build bespoke demos that highlight the most relevant features. Buyers today are more informed than ever. The best sellers I know are more prepared than ever. The result? More productive conversations, deeper connections and higher trust.

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

Having a dominating share on e-commerce marketplaces has been one of the pillars of our growth. 10 pointers for founders to keep in mind while scaling e-com: 1. The fundamental equation of e-com is “Sales= Traffic*Conversion”. Not meeting sales numbers is either a traffic problem or a conversion problem. For every SKU, figure out whether it is a traffic problem or a conversion problem. Do not try to solve traffic problems with conversion levers. And vice versa. 2. Like all performance marketing, e-com media also has diminishing returns. Beyond a point, increasing spends will not increase sales at the same speed. Stop at that point 3. If you want to increase profitability, you need to increase your organic discoverability in the platform. Amazon is a search led platform with search contributing to 60-70% views in most categories. For Flipkart, along with search, merch and reco are equally important. But the fundamentals of organic discoverability is same. Both platforms have an algorithm where SKUs with the best reviews, highest listing quality score, lowest time to delivery and highest conversion rates get pushed. Optimize for these parameters and see organic discoverability skyrocket 4. The other way to reduce dependency on platform ads( and hence increase profitability) is to ensure your branded searches increase. This is directly a function of your off platform marketing activities, word of mouth and repeat customers. So, work on those parameters 5. Category Relationships matter a lot. Understand what the number 1 objective of your category manager is for the year. And help them achieve it. Eg: If they are looking to improve ASP, help them with your premium assortment. If you help them achieve their number 1 KPI, they will ensure you do well on the platform 6. Whatever the ads team tell you, take it with a pinch of salt. Most times they are very helpful. But their number 1 KPI is to sell ads. Not your success. So, sometimes what is good for them might not be good for you 7. All SKUs will not do well. All sub-categories won’t do well. If there is no PPCMF, no amount of good execution will cut it. So, important to cut your losses and stop investing more money on losers. Instead, allocate to your winners in the portfolio 8. Have a E-Commerce dashboard which goes beyond the L0 metrics. Look at your L1 and L2 metrics daily and hold teams accountable for these metrics. Ads driven sales, share of search, organic visits, conversion rates etc are all examples of L1 metrics 9. Sometimes there will be irrational competition and they will bid crazily for keywords. Do not compete with them. They are burning cash and because blind venture money is running out quickly in consumer brands, they will fizzle out. 10. Do not overdo discounts. Discounts are like antibiotics. You use it 2-3 times a year, you see huge spikes. Use it every alternate day, and that becomes your market operating price.

Dove fly-posted negative Reddit reviews across NYC. They launched “r/eal reviews” built entirely from unfiltered Reddit commentary about its Intensive Repair 10-in-1 Serum Mask. The first 50 consumer reviews, positive, negative and neutral, appear exactly as written. No attempt to steer sentiment. 𝗥𝗲𝗱𝗱𝗶𝘁 𝗻𝗼𝘄 𝗵𝗮𝘀 𝟭𝟬𝟬,𝟬𝟬𝟬+ 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝟭𝟭𝟲𝗺+ 𝗱𝗮𝗶𝗹𝘆 𝗮𝗰𝘁𝗶𝘃𝗲 𝘂𝘀𝗲𝗿𝘀. And content views on Reddit are growing 30%+ year on year. It’s where people get answers, share ideas, offer advice and go for real product reviews. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁: → Review culture has moved from brand-controlled testimonials to open community commentary. → Consumers are actively seeking out unfiltered opinions in places like Reddit before they buy. → Brands are responding by engaging directly within communities rather than relying solely on influencer amplification. → On Reddit, that means participating in the format of the platform. AMAs, community threads, content that sits naturally within the ecosystem. The campaign launched with a pop-up takeover of Flatiron Plaza in New York, where oversized, unedited reviews sit alongside product sampling. To honour Reddit’s anonymity, Dove covered all participant faces with their Snoo avatars. There’s a HUGE opportunity on Reddit that most brands are still underestimating.

One of the most practical AI use cases in eCommerce right now isn’t a chatbot or a fancy personalization layer. It’s predicting a shopper’s future LTV before you spend the budget, and routing spend toward the people most likely to buy again. This is what I learned recently from Pecan AI which is quite interesting to me. And because most teams can’t do that today, they keep allocating budget evenly and running broad promos, hoping it works. 𝐏𝐞𝐜𝐚𝐧 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 changes the workflow: • You define the goal (e.g. “Predict 90-day LTV by channel and creative”) • It builds the predictive model for you • Then outputs ranked audiences and campaigns to scale, cap, or test, pushed directly into the tools you already use (ad platforms, CRM, email) No dashboards. Just actionable predictions. 📚 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐭𝐡𝐞𝐲 𝐬𝐡𝐚𝐫𝐞𝐝: A DTC apparel brand had strong AOV but low repeats from a few ad sets. Pecan flagged those cohorts as low predicted LTV, capped spend, and shifted budget to a lookalike built from high-LTV buyers → ROAS went up and discount costs dropped. This is the kind of AI that actually drives growth, not just adds another layer of complexity. Demo link → https://hubs.la/Q03BJHTF0 #AI #ecommerce #predictiveanalytics #martech

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

It’s an oxymoron, but in the era of AI moving funds from one account to another has been signaled as one of the major payments’ trends. Where’s the catch? Let’s take a look. Transferring funds between accounts is not a novelty, but rather one of the oldest and more basic payments’ use cases. And yet if you look at today’s increasingly complicated payments landscape, there is an entire debate going on, hooked on the principle that Account-to-Account (A2A) payments can lead the next wave of payments #innovation. The numbers from the 2024 Global Payments Report are telling: —     A2A was in 2023 the leading #ecommerce payment method in Finland, Malaysia, The Netherlands, Nigeria, Norway, Poland, Sweden and Thailand —     In established card markets (Australia, Canada, UK, USA) A2A growth has been considerably slower —     In emerging markets (i.e. India, Brazil) A2A schemes have risen mainly due to strong government support as a means to achieve financial inclusion and promote digital payments, whereas in more advanced markets the use of A2A schemes is driven by collaborative initiatives between banks Despite their differences, A2A schemes across the globe have one common denominator: they are all local. Interoperability is almost non-existent (Alipay+ is an exception), reflecting a plethora of challenges: different geographies, local consumer preferences, infrastructure, regulation, etc. The rise of A2A #payments is driven by: 1. The proliferation of real-time rails that bring novel use-cases 2. The growth of Open Banking schemes (OB payments are by default A2A Payments) 3. Major actors (merchants, payments players) in an increasingly digital and mobile-first FS ecosystem looking for reliable, efficient and price-competitive payment alternatives 4. Regulation - the recently adopted Instant Payments Regulation (IPR) in Europe is a primary example Despite all the above and the attractiveness of the model, there are 2 main challenges holding back #A2A payments and schemes from dominating, especially in card markets: —  The lack of all the bits and pieces adding value around simple payments’ transactions: chargebacks, disputes, exceptions and fraud protection. These are areas where the big schemes (i.e. Visa, Mastercard) have a competitive advantage, having spent decades optimizing them —  Loyalty schemes and premium perks (i.e. hotel credits, airline miles, discounts) funded from card interchange fees (that don’t exist in A2A set-ups) that drive card adoption These challenges notwithstanding a significant part of the payments’ innovation is heading back to where it started from: the bank account. It’s not therefore by accident that the card networks have bought their way into the space (i.e. Mastercard has bought Finicity and Aiia and Visa has acquired Tink). In the new, multi-rail payments landscape A2A is here to stay. Opinions: my own, Graphic sources: Arkwright Consulting, The Paypers