Merchandising for New Product Launches

You think Shein is just another fast fashion company. But really they are one of the biggest technology companies you've never truly understood. Here are 3 things that this Chinese-owned fashion giant did to be worth more than H&M and Zara combined! 👇 👉 Shein uses its own proprietary tools along with Google trends to figure out what styles are trending through search and social media. These then go to their team of 800 designers who create designs based on the styles. Designs are created within 36 hours. Social trends generate hype before the item has even been produced by Shein. This is incredibly smart...they sell into pent-up demand for a certain style. Then their products leverage that hype to build more hype but this time specifically around their brand. 👉 Shein creates small batches of products - as small as 10 pieces then put them on their site. Then using AI, they're able to track behaviour like browsing product details, the number of add-to carts, and how long people spend on a particular product. All their 3000 suppliers are given access to Shein's ERP platform which allows them to see in real-time what products are being clicked on the most, then instantly create more of those. This real-time model reduces the time from design to finished product to 3 days and drastically reduces excess waste. To give some context, their nearest competitor Zara takes 5 weeks and that's considered fast. There is fast fashion...then there is supersonic fashion. 👉 Shein adds over 1000 new styles every single day in over 220 countries! To put this into context, Zara adds 300 per month. In reality, Shein is really an optimisation machine - ingesting what we like then feeding us more of that...and doing it in a localised way. There is a second algorithm that weighs how deep the actions are of a user on a site - a product which has more viewing time has a higher weighting than one which people merely clicked on for 2 seconds. This then leads to the user being shown similar products. You and I could go on Shein and be shown drastically different products due to our browsing data. If this hyper-personalisation sounds very similar, it's because there is another super app launched in China which has the same thing... TikTok. -- It’s no surprise why Shein has been called The TikTok of commerce. Whether you love them or hate them - and there are many reasons to hate them - you can’t deny that they’re building the future of what commerce looks like. Commerce is moving from a human-designed POV and moving to what the machines tell us. I believe that's generally where consumption is moving, when there is so much choice, let the algorithms decide. There will be no need for human curation. Even more important, I believe we're just at the beginning of this wave...

A Zara store manager notices a jacket selling out on Tuesday. By the weekend, more are on the way. That loop is the entire business model. That feedback loop is a big reason Zara's parent company is worth more than H&M and Gap combined. What makes it possible: Zara's store managers feed sell-through data back to the warehouse within 24-48 hours. Not reports filed at the end of the week. Not monthly aggregations sent to a regional office. Within hours of a trend emerging on the shop floor, the signal is already moving upstream and the factory can respond. Most retailers work the opposite way. Data moves up the chain slowly, gets analyzed at head office, and decisions flow back down on a seasonal or quarterly basis. By the time a response hits the store floor, the trend that triggered it is usually over. Case studies consistently cite Zara selling through its stock ~11-12x per year, with very little left over to discount. Fashion retailers that restock on longer cycles end up sitting on excess inventory and putting everything on sale to clear it. This isn't a technology story. H&M has also invested heavily in store tracking technology and data tools. The real difference is organizational. H&M store managers largely execute the display plans set by headquarters. Zara store managers actively relay customer demand signals back to HQ: what's moving, what customers are asking for, what's sitting. Those inputs shape production and allocation decisions at pace. One analysis estimates ~25% of Zara's parent company's spending goes to labor: more than many competitors. But that workforce isn't just executing tasks. It's feeding the system that determines what gets made and shipped. The traditional retail model treats stores as distribution endpoints. Corporate decides, stores execute. Information flows one way: down. Zara flipped it. Stores became sensing mechanisms. Information flows up, decisions flow down, then loop back again. Continuously.

₹900 Crore Fast-Fashion Engine: How Snitch Built India’s Most Profitable Style Machine India doesn’t have a fashion demand problem. It has a speed problem. 1. Traditional fashion is 6-month design cycles, seasonal inventory bets, high dead stock & discount-driven sales. 2. New fashion is daily launches, real-time trends, low inventory risk and full-price sellouts. This shift is powering SNITCH. From ₹498 Crore → ₹900 Crore in 1 year. This isn’t growth. This is velocity compounding. ✅ THE NUMBERS 1. Revenue: ₹498 Cr → ₹900 Cr (+80%) 2. EBITDA margin: 2%–3% 3. EBITDA: ₹18–27 Cr 4. FY25: ₹1.7 Cr loss → FY26: Profitable 5. Daily launches: 10–15 new styles In fast fashion, profitability is rare. Speed with profitability is a different league. ✅ The Core Engine: Daily Fashion Drops Snitch doesn’t follow seasons. It follows signals. 10–15 new styles every day. Constant refresh of inventory, no end-of-season dependency; if a trend works → scale instantly. If it fails → kill immediately. ✅ Where Real Money Is Made Revenue doesn’t come from volume alone. It comes from control. 55–60% sales from own website/app. Lower marketplace commissions and higher margin capture add to premium categories (co-ords, jackets), lifestyle positioning vs. single-product brands, and Gen-Z + millennial repeat buying. More control = more margin. ✅ From B2B to D2C Pivot - 2019: Offline B2B brand - 2020: Forced D2C pivot - 2026: ₹900 Crore digital-first business The pandemic didn’t disrupt the company. It reset the model. Because D2C isn’t about selling online. It’s about owning the customer. ✅ Omnichannel Flip After winning online, Snitch went offline. 25+ physical stores, tier 1 + Tier 2 expansion, and higher AOV in stores led to success. Touch → Try → Upsell. Offline isn’t dead. It’s margin accretive. ✅ The Shark Tank Multiplier Founder Siddharth Dungarwal didn’t just raise capital. He reduced CAC permanently. All-shark deal = instant credibility. Organic trust → higher conversions. Brand recall without ad spend in D2C is a key to success. Trust is the cheapest acquisition channel. ✅ The Hidden Moat: Speed + Data Behind the brand is a system: 1. Trend tracking via social + search data 2. Rapid prototyping in 48–72 hrs 3. Local manufacturing agility 4. Trend spotted today → product live this week By the time competitors react, Snitch has already sold out. ✅ Let me share the #Rajspectives 1. Fast fashion is not about price. It’s about speed. Daily drops beat seasonal collections. 3. Owning distribution = owning margins. Offline stores are conversion engines, not just branding. 3. In fashion, the fastest brand wins, not the biggest. India’s ₹6 lakh crore fashion market isn’t waiting. It’s moving in real time. And Snitch isn’t chasing trends. It’s manufacturing them. Because in 2026, the best fashion brand isn’t the one with the best designs. It’s the one that gets there first and sells out before anyone else. #india #fashion #d2c #startups #business #strategy

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

When a brand as iconic as Glossier, Inc. comes to you with a growth challenge, you pay attention. But what fascinated me most wasn't their status in DTC beauty — it was the counterintuitive problem they were trying to solve. Glossier’s story challenges a common assumption in DTC — that you have to choose between brand magic and performance marketing. When we first started working with them, Glossier had already built an iconic brand presence. But they faced an interesting challenge: their product pages had to serve too many use cases, making it hard to create targeted experiences for specific campaigns. Here's how they leveraged FERMÀT to bridge that gap: 1. Brand-Performance Harmony → They found a way to maintain their signature aesthetic while incorporating conversion elements → Smart move: pulling in educational content from their Sephora listings → This wasn't just about conversion — it was about enriching the customer experience 2. Payment Innovation → What I love about this: they identified a simple friction point (payment method visibility) and tested a hypothesis → By bringing Apple Pay to the PDP level before BFCM, they saw nearly 50% of purchases flow through this path → It's these seemingly small optimizations that often drive outsized results 3. Strategic Merchandising → Identified key SKUs for growth (their fragrance line was particularly interesting) → Positioned hero products above the fold with intentional discount pricing displays → What I love here: they let data drive merchandising decisions while maintaining brand standards 4. Launch Architecture → Their Black Cherry collection launch really showcases the power of alignment → Every touchpoint, from ad creative to checkout, told a cohesive story → This is what great merchandising looks like in the digital age Glossier has launched 24 new shopping experiences in 8 months, with 6 emerging as clear winners. They’ve seen up to 65% lift in ROAS and a 25% reduction in CPA across FERMÀT funnels. But what really strikes me about Glossier, Inc.'s approach is their commitment to learning. Each experiment, whether successful or not, informed their broader strategy. As Madeline Kuttner, their Director of Performance Marketing, said: "Working with FERMÀT has given our growth team the ability to cohesively align our ad-level creative and full-funnel experiences, bridging the gap between our brand vision and performance marketing capabilities." It's not about choosing between brand and performance; it's about finding creative ways to serve both masters.

Startups: use customer feedback to actively refine your products. Your customers are your best critics. As an operator, a startup I worked with saw 15% month-over-month growth. The game-changer? I asked 100 potential customers why they weren't buying our product. Five years later, our product’s core is still based on insights from that survey. Regularly ask your frequent buyers what they love about your product and what they'd like to see improved. Use feedback forms and direct conversations. This makes them feel involved in the product's evolution and helps you create something they'll truly adore. When considering feedback, look for common themes from multiple customers instead of making changes based on a single opinion. This ensures that your modifications resonate with a broader audience.

I'd like to discuss using Customer Feedback for more focused product iteration. One of the most direct ways to understand customers needs and desires is through feedback. Leveraging tools like surveys, user testing, and even social media can offer invaluable insights. But don't underestimate the power of simple direct communication – be it through emails, chats, or interviews. However, while gathering feedback is essential, ensuring its quality is even more crucial. Start by setting clear feedback objectives and favor open-ended questions that allow for comprehensive answers. It's also pivotal to ensure a diversity in your feedback sources to avoid any inherent biases. But here's a caveat – not all feedback will be relevant to every customer. That's why it's essential to segment the feedback, identify common themes, and use statistical methods to validate its wider applicability. Once you've sorted and prioritised the feedback, the next step is actioning it. This involves cross-functional collaboration, translating feedback into product requirements, and setting milestones for implementation. Lastly, once changes are implemented, the cycle doesn't end. Use methods like A/B testing to gauge the direct impact of the changes. And always, always return to your customers for follow-up feedback to ensure you're on the right track. In the bustling world of tech startups, startups that listen, iterate, and refine based on customer feedback truly thrive. #startups #entrepreneurship #customer #pmf #product

Customer feedback is the compass that guides product engagement. Incorporating a well-established feedback loop is essential for any successful product. It's not just about listening, but also understanding, adapting, and continually improving. How do you know your feedback loop is strong? Some key principles to keep in mind ✅ Clear Channels: Provide easy and accessible ways for customers to share feedback—whether it's through surveys, user interviews, or dedicated feedback forms. ✅ Active Listening: Pay close attention to what customers are saying. Actively listen to their concerns, suggestions, and pain points. ✅ Prompt Response: Acknowledge and respond to feedback promptly. Show that you value their input and are committed to addressing their needs. ✅ Structured Analysis: Organize and categorize feedback systematically to identify trends and prioritize improvements effectively. ✅ Transparency: Share insights gained from feedback with your team and, where possible, with your customers. Transparency builds trust. ✅ Iterative Approach: Use feedback to inform product iterations. Continuously evolve your product based on what you learn. #customercentricity #productinnovation #productmanagement #productleadership

Shipping features isn’t the finish line—it’s just the starting gun. 🔫 Here's why 👉 Too often, teams treat a feature launch like a mic drop. But in reality, successful products come from a constant loop of 𝗿𝗲𝗹𝗲𝗮𝘀𝗶𝗻𝗴, 𝗼𝗯𝘀𝗲𝗿𝘃𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗻𝗴. Here’s what I’ve learned about turning a launch into lasting value: ▶︎ 1. Launch with intent. Every feature should address a real customer pain point or unlock clear value. If it’s just a checkbox on the roadmap, you’re missing the point. ▶︎ 2. Measure what matters. It's not enough to track deployment stats. Dig deeper: How are customers interacting with the feature? Are they hitting roadblocks? What feedback (or complaints) is support hearing? Is it achieving the business results you expected? ▶︎ 3. Adapt and improve. Data isn’t just for dashboards—it’s a guide for action. Use what you learn to: Fine-tune the feature. Adjust your product direction. Deepen your understanding of what your customers really need. The best features aren’t necessarily the flashiest—they’re the ones that evolve based on how people actually use them. ------------------------------------- 👨💻 I help ambitious companies build software, grow engineering teams and implement AI to Drive Change 📩 Drop me a line to discuss your project #productdevelopment #productteams #product #launch #softwaredevelopment

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.