Retail & Merchandising

Your Price Elasticity is wrong the moment you use it. If you work in #Pricing or #RGM, you see it constantly: "the elasticity is -2". It's in spreadsheets, dashboards, presentations. It's the foundation for price recommendations, portfolio decisions, promotion evaluations. It feels solid. It isn't. Not because the measurement was bad. That's a real problem, but it's not the interesting one. The interesting problem is structural: Even if the number is perfectly measured, it still is wrong the moment you use it. Here's why. 𝗣𝗿𝗶𝗰𝗲 𝗲𝗹𝗮𝘀𝘁𝗶𝗰𝗶𝘁𝘆 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘄𝗶𝘁𝗵 𝗽𝗿𝗶𝗰𝗲. Say your elasticity is -2 at the current price of €1.00. You're considering a 10% price increase. The elasticity tells you to expect roughly a 20% volume drop. So far, so good. But after you raise the price to €1.10, your elasticity is no longer -2. It might be -2.5. Or -3. The sensitivity of demand has changed. Because at a higher price, a different set of customers is now marginal. The ones who were barely buying at €1.00 are gone. The ones still buying at €1.10 have different price sensitivities. This isn't a measurement error. It's a mathematical certainty. 𝗪𝗵𝗮𝘁'𝘀 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵: 𝘁𝗵𝗲 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘆𝗼𝘂'𝗿𝗲 𝗻𝗼𝘁 𝘀𝗲𝗲𝗶𝗻𝗴. What you actually need — and what the elasticity number throws away — is this full demand curve. That curve encodes the distribution of customer preferences, and it tells you the revenue and profit implications at every price point. Elasticity is a single point on that curve. It captures almost none of the information. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲. When you use an elasticity of -2 to evaluate a pricing decision, you are implicitly assuming three things: 1. The elasticity you measured is still accurate at the price you're moving to. 2. The competitive context that produced that elasticity hasn't changed. 3. The customer base whose behavior generated the number is the same customer base you'll face after the change. None of these are usually true. And the further you move from the price at which elasticity was measured, the less reliable it becomes, precisely when you most need it to be right. This doesn't mean elasticity is useless. It's a reasonable summary statistic for small, local price movements in stable conditions. But it is a terrible foundation for the decisions that actually matter: significant price changes, portfolio restructuring, or anything involving a new competitive dynamic. 𝗔 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗼 𝗮𝘀𝗸. When working with elasticities, try asking: "At what price was this measured? And how far are we moving from that price?". If the answer is more than a few percent, the number has already drifted. See how AI in RGM can help: https://bit.ly/4bhEvpn #pricing #RGM #priceelasticity #commercialstrategy #CPG

"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)

One image just disrupted a £22 billion fashion empire more effectively than a thousand sustainability reports. 🔥 This isn't an official SHEIN campaign gone wrong. It's artist Emanuele Morelli's AI creation—a haunting visualisation showing what fast fashion's "affordability" really costs us. The image speaks volumes: a SHEIN billboard where the model's flowing dress transforms into a cascade of textile waste. Art communicating what statistics alone cannot. 5 uncomfortable truths this image forces us to confront: 1. The scale of fashion waste is staggering → 92 million tonnes of textile waste produced annually  → The equivalent of one rubbish lorry of textiles dumped every second  → Most fast fashion items designed to be worn fewer than 10 times 2. The business model depends on our amnesia → Constantly changing trends keep us buying  → Ultra-low prices remove financial friction  → Digital marketing creates artificial scarcity and FOMO  → We're trained to forget yesterday's purchases 3. The true cost isn't on the price tag → Environmental damage from production chemicals  → Microplastics shedding into water systems  → Supply chain ethics compromised for speed and cost  → Communities near production sites bearing health consequences 4. Our definition of "affordable" is broken → When clothing is cheaper than a coffee, someone else is paying  → True cost spread across communities, environments, and future generations  → Psychological cost of constant consumption never factored in 5. Solutions exist but require systemic change → Circular fashion models gaining traction  → Rental and resale markets growing rapidly  → Consumer awareness rising but needs to translate to behaviour While SHEIN isn't the only culprit in the fast fashion ecosystem, Morelli's artwork throws a spotlight on an uncomfortable reality we've normalised. What we wear reflects our values more than our taste. What is your wardrobe saying about yours? Image: Emanuele Morelli ♻️ Found this helpful? Repost to share with your network.  ⚡ Want more content like this? Hit follow Maya Moufarek.

A very easy way to improve your Amazon ads efficiency by at least 10% Let’s say you’re spending ₹4–5 lakhs/month on Amazon ads. Your ACoS looks okay. Conversion rate seems fine. But your gut tells you—you’re still wasting some money on irrelevant traffic You’re not wrong At Atomberg, we had found that some of our Amazon spend was going toward search terms that had no business seeing our ads: - “cheap fan” -“rechargeable fan” - “usb fan under 1000” None of these users were in-market for a ₹3,000+ BLDC ceiling fan. But we were still showing up. And paying for those clicks. And it’s not just us. I’ve seen 6–7 brands' Amazon ad accounts across categories over the last few years—same problem, every single time The fix? N-gram analysis Takes less than an hour. You don’t need to be a performance marketing expert. But the results compound What’s N-gram analysis? It’s breaking down every search term into its word components—1-grams, 2-grams, 3-grams—and then identifying patterns that consistently drive waste… or conversion. Example: “cheap rechargeable fan for hostel room” turns into: 1-grams: cheap, rechargeable, fan, hostel, room 2-grams: rechargeable fan, hostel room 3-grams: fan for hostel, etc. When you do this across all your search terms, you start seeing the real picture. Why this matters more than just checking your search term report: Search terms ≠ keywords a) One keyword can trigger 100s of different queries. Some convert. Most don’t. You need to find the patterns. b) Waste is diluted across low-volume terms. Maybe “rechargeable fan for hostel” spent ₹300. You ignore it. But what if 12 other queries with “rechargeable” spent ₹6,000 in total with zero conversions? c) Long-tail is infinite. N-grams are finite. You can’t negate every bad search. But you can block the core terms—“cheap”, “usb”, “mini”—once and be done with it. d) It helps you scale campaigns too. You can find goldmine phrases like “white ceiling fan”, “silent BLDC fan”, “fan for living room”—with 5x+ ROAS. Those became exact match campaigns What you should do: a) Pull last 3 months of search term data b) Break them into unigrams, bigrams, trigrams c) Create a pivot with spend, orders, ROAS by N-gram d) Negate high-spend, low-conversion N-grams (e.g., “cheap”, “rechargeable”) e) Boost high-ROAS ones (e.g., “bldc”, “ceiling fan white”) f) Add exact match campaigns g) Rinse and repeat monthly Try it. Guaranteed to improve efficiency at whatever scale you are operating If you want to read an expanded version of the post, link is in the first comment

Cosmetic Sampling, gateway to conversion. Once seen as simple promotional giveaways, sampling has evolved into a strategic powerhouse for brands and retailers seeking deeper engagement and stronger conversion rates. Yet, as with all packaging-dependent formats, sustainability remains a critical concern. +75% users more inclined to purchase from a unknown beauty brand after sampling. +56% U.S. beauty shoppers prefer to try a product in-store before committing to a purchase. →From silent testers to social media stars. Sampling isn't just about trial, it's about storytelling. Premium brands now design their samples to look as good as they feel, giving them visual appeal both on shelves and screens. Bold forms, textured finishes, and branded pouches or vials are turning samples into social-media-friendly content that supports product discovery and brand desirability. +33% consumers would have visited a store to try the product if they hadn't received the sample online. →Entry point to the brand world. Sampling allows consumers to experience a brand’s essence at zero or low cost. It lowers the barrier for first-time users and helps convert interest into loyalty. For new brands or product lines, samples are an efficient way to drive trial and build early awareness, a small gesture with big potential impact. +30% of U.S. fragrance users would not purchase a fragrance they hadn't smelled in person. →Designed for a “try-before-you-buy” culture. Modern consumers want to test and compare before committing. Sampling aligns perfectly with this mindset. It offers a tactile, sensorial experience that digital can't fully replicate, especially vital for texture-based categories like skincare and makeup. It minimizes buyer’s remorse and increases trust. +40% consumers who try a sample proceed to purchase the full-size during the same shopping trip. →Unsustainable reality of sampling. Use of trial kits generates around 980 tones of plastic waste annually in the UK, with only 9% recycled. To reduce this impact, brands must adopt sustainable sampling, using recyclable materials, biodegradable films, or refillable formats, while designing smarter, minimal packaging that maintains effectiveness without the waste. Conclusion. Are indispensable for cosmetic brands seeking to engage consumers, showcase product efficacy, and drive sales. The integration of innovative, sustainable packaging solutions and an understanding of evolving consumer preferences are crucial for the success of sampling initiatives. Find my curated search of examples, and get inspired for your next success. Featured Brands: Byredo Chanel Damiana Dsd Glossier Khus+khus Lelabo Ohii Ouai Rhode Sooyanng #beautybusiness #beautyprofessionals #beautydesign #beautysampling

Excited to share insights from Walmart 's groundbreaking semantic search system that revolutionizes e-commerce product discovery! The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce. Key Technical Highlights: • The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching. • Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types. • The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs. • To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples. Production Implementation Details: • The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency. • Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production. • The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors. Results: The system showed significant improvements in both offline metrics and live experiments, with: - +2.84% improvement in NDCG@10 for human evaluation - +0.54% lift in Add-to-Cart rates in live A/B testing This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-commerce challenges!

At airtel, I ran an iPhone giveaway marketing campaign three times, and to my surprise, none of them performed. Logically, you’d think offering a prize as attractive as an iPhone, especially during the launch period, would drive massive engagement. The assumption is that everyone would rush to participate, download your app, engage with the campaign, and complete the required actions. But what actually happened was the opposite. Engagement was shockingly, embarrassingly low. In contrast, campaigns that offered much smaller rewards—like a ₹1,000 or ₹500 voucher, or even just a free mobile recharge—generated higher participation rates, more app downloads, and greater overall engagement. But why does this happen? This outcome can be largely attributed to consumer psychology. When the reward seems too large or unattainable, people instinctively doubt their chances of winning. The concept of *perceived probability* comes into play here. When the prize is something as high-value as an iPhone, people immediately think, "What are the odds that I’ll actually win?" This skepticism causes them to disengage and not even bother trying, as they don't see the reward as realistically achievable. On the other hand, smaller, more attainable rewards feel within reach. A ₹1,000 voucher or a free recharge doesn’t carry the same sense of improbability. People feel like they have a real shot at winning something smaller, which encourages them to take the necessary actions, leading to better campaign results. In essence, psychology plays a far more critical role in shaping consumer behavior than we give it credit for.

🗺️ User Journey Maps vs. Service Blueprints (+ Templates) (https://lnkd.in/d8tNmKe2), a fantastic article explaining differences between the two, when to use each, along with a free practical guide to get started. Kindly put together by Morgan Miller and Erika Flowers. As Morgan and Erika write, mapping experiences is a key part of a human-centered business. We need to look at both perspectives — what the person experiences (UX, front stage), and what went on outside of their view to make it happen (Service Design, backstage). With user journey maps, we visualize and document user’s experience. We interview customers to capture their insights, then map patterns. We list steps and actions they go through to meet their goals — sometimes with storyboards, or Jobs-to-Be-Done, or emotional responses. The outcome is an aggregate, real-world experience (front stage) — framed as a narrative. Those user journeys often start way before users start interacting with your product — so we need to include non-digital touch points as well. Customer journey maps are just like user journey maps, just for a different persona: e.g. in B2B, customers might not be end users. Service blueprints are not about documenting the user experience. They apply user experience as starting point, and unpack it to expose how it is *internally* created — with technology, people, operations, processes involved (backstage). Journey maps and service blueprints highlight different sides of the experience story. But they have one thing in common: they help us understand the broken parts and fix them. The outcome, then, is a great UX and great internal processes that shape and enable it. Useful resources: Guide to Journey Maps + Templates, by Stéphanie Walter https://lnkd.in/erheegtf UX vs. Service Design, by Sarah Gibbons https://lnkd.in/d5mw3vVu UX Mapping Methods: A Cheat Sheet, by Sarah Gibbons https://lnkd.in/eSnExG4h Guide To Customer Journey Mapping (+ free template), by Taras Bakusevych https://lnkd.in/e-emkh5A User Journey Maps: Guides and Templates, by yours truly https://lnkd.in/dY5NtqSf ✤ Service Blueprints Service Blueprint Design System (Figma), by Jacopo Sironi https://lnkd.in/d-qrSFRY Service Blueprint Kit, by Julien Fovelle https://lnkd.in/dXmkCPDm Service Blueprint Templates, by Theydo https://lnkd.in/dUsDzYCA A Guide to Service Blueprinting (PDF), by Nicholas Remis https://lnkd.in/ejY82P5M Your Guide To Blueprinting (free PDF + Miro), by Morgan Miller, Erika Flowers https://lnkd.in/efFPAeU9 #ux #design

🚨Amazon has built a really cool new ad tech to monetise Prime videos, but it’s not what you would have thought! 🚨 To appreciate this new ad tech we need to go back in time and look at some history. We would have all watched on movies and tv shows where products have been strategically placed to drive brand awareness and recall. The hit show Stranger Things had about a 140 brands featured in the 4th season with some estimates sizing it to $27million in brand placement value. And this is just one season of one show. As more and more people are disengaging with intercepting ads, brands and media producers are trying innovative ways to gets brands in front of eyeballs without being skipped. Now if a studio had to integrate with brands, it requires for them to coordinate before hand with the brands and figure out where to strategically place the products and shoot the content. Enter Amazon’s Virtual Product Placement Technology. Virtual product placement is an emerging technology that inserts a digitally rendered product, billboard, or logo into a movie or TV series after it has been filmed. Amazon collaborates closely with content creators when determining placement locations and available product categories for each participating title. All decisions are made in line with the artistic vision for each movie or series, with a shared goal that placements will not interfere with the story or affect the viewer’s enjoyment. Brands are expected to spend upwards of $125bn by 2026 on video ads, so it’s a pretty huge market they are going after. Stats also show that 63% of viewers say they feel the urge to buy a product when they see it featured in a TV show with GenZ leading the pack. In a specific case study, Bubly a sparkling water brand saw a 18.1% lift in aided recall, 6.8% lift in brand favourability, 16.5% lift in purchase. This ad format becomes even more powerful when you combine it with Amazons e-commerce marketplace where marketeers can do full funnel advertisements all the way from awareness to purchase. Secondly, with post production virtual product placement, the same product placement could be bid by different brands for e.g the scene having bubly could very well also have any other canned drink which ever fit into the category. I must say this is by far one of the most impressive ad tech I have come across in recent times and Amazon is truly Priming us to purchase.

Nature's Hacks for Success. Biomimicry might sound complex, but it's simply about learning from nature to enhance our designs. It's like learning from the best teacher—Mother Nature herself. Defined by the Biomimicry Institute, this approach guides us toward sustainable solutions by mimicking perfected patterns and strategies found in nature. Nature has already solved many of our challenges. So, why not apply its genius to our packaging designs? It offers patterns and relationships that inspire better, eco-friendly packaging designs—whether in structure or materials, designers can draw from nature's beauty, texture, and flow. We discover materials that are waterproof, breathable, flexible, and more—it's as if nature has already completed the heavy lifting of innovation, evolution, and adaptation for us. Think of the honeycomb structure in beehives—it's not only sturdy but also space-efficient. A great example of biomimicry in packaging design is the SIS bottle by Backbone Branding. Their designers draw inspiration from a flower's pistil to shape a two-litre juice bottle. The design not only stands out with its natural juice colour but also resolves many stacking, storage, and merchandising challenges through its interlocking form. Rooted in geometry with equilateral triangles, these bottles fit snugly together, saving space. Every aspect of the bottle, from its size and proportions to its lines and curves, has been carefully considered. Even the label has been specially designed to adhere to the bottle's irregular surface, eliminating the need for glue. Consider adding nature's strategy into your design process. It will help you close the loop and build a solution that resonates with the ecosystem we breathe in. Biomimicry enables us to develop sustainable systems rather than short-lived, isolated solutions that may soon become outdated. One thing's for sure, we stand at a crucial juncture in human history. The challenges ahead demand designers and innovators capable of creating resilient, adaptable solutions. Our path forward must consider the well-being of future generations across the planet. We must continually draw inspiration from nature and reciprocate by nurturing and preserving it. In doing so, we'll not only enrich our designs but also contribute to the greater ecosystem. Let nature continue to inspire us, and in return, let's contribute to its well-being—a cycle of respect and reciprocity where our designs and actions reflect a deep reverence for the natural world. Ready to take a cue from nature's playbook for your next packaging design? 📷Backbone Branding