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: http://bit.ly/4bhEvpn #pricing #RGM #priceelasticity #commercialstrategy #CPG

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.

Walmart Opens New Stores: You’re Not Allowed In. They look like stores. They stock popular products. But you can’t go inside. Walmart is piloting “dark stores” in Dallas and Bentonville—brick-and-mortar locations that fulfill only online orders. No customers. No carts. Just pickers, packers, and speed. This isn’t omnichannel. It’s reverse omnichannel—physical space built to serve digital demand. It’s working: Walmart’s U.S. e-commerce is now profitable, with Q1 sales up 21%. Deliveries under 3 hours grew 91% year-over-year. They expect to reach 95% of U.S. households within that timeframe. What’s driving this? * Tech-powered logistics (drones, AI, automation) * Streamlined assortments and faster turns * Customers willing to pay for speed What does this mean for brands? If you’re not easy to pick, ship, and deliver, you’re in the wrong place at the wrong time. * Visual merchandising becomes data merchandising. * Packaging becomes performance. * Shelf appeal becomes search appeal. This tactical shift is both a challenge and a call to evolve. The store of the future may not need shoppers. But it absolutely needs suppliers who understand the choreography of fulfillment. Would love to hear how others are preparing for a world where brick-and-mortar goes dark. #RetailStrategy #Ecommerce #Logistics #Walmart #DarkStores #RetailInnovation #ConsumerBehavior #RetailTransformation #LastMile Bloomberg Retail Dive Amazon Kohl's

Loyalty is failing. Gen Z & long-term commitment. 22% of Gen Z consumers consider themselves loyal to one brand is a clear warning for legacy loyalty strategies. Unlike previous generations, Gen Z doesn’t see brand loyalty as a long-term commitment, they’re loyal to moments, not just names. +43% increase in engagement and sales conversions among Gen Z Beauty brands offering "limited-edition drops" and collaborative experiences. +71% Gen Z say they would rather spend money on an experience than a product. >>Loyalty is FAILING, but why<< +Transactional systems feel outdated: Point-based rewards for repeat purchases don’t excite this audience. They expect more than discounts or free samples. +They’re brand-agnostic but experience-driven: Gen Z freely switches between brands if the experience, aesthetic, or values feel fresher or more aligned with their identity. +They buy into stories, not just products: They want to align with brands that represent something, social causes, cultural movements, or communities they relate to. >>DYNAMIC LOYALTY<< What’s this? as it name indicates its a system that rewards interaction, aligns with their values, and constantly evolves. And that is what your brand needs. → Create experience-driven loyalty programs: Offer early access to limited drops, invite-only events, or backstage content. Think like a fan club, not a punch card. +Example: A loyalty tier that unlocks tickets to a pop-up experience or an exclusive AR filter. →Let them co-create: Invite Gen Z customers to co-develop product ideas, designs, or campaign themes. Give them ownership in your brand’s creative journey. +Example: Voting on packaging designs or joining beta tester groups. →Align with their values: Sustainability, inclusivity, and social good aren’t nice-to-haves. they’re expectations. Use loyalty programs to reward actions too, like recycling, sharing causes, or supporting small creators. +Example: “Earn loyalty points by returning empties or attending a sustainability workshop.” →Deliver constant novelty: Rotate limited editions regularly. Use scarcity and surprise to create FOMO and buzz. +Gen Z doesn’t commit to a single brand, but they’ll keep returning if each visit feels fresh and share-worthy. →Go omnichannel but social-first. Should live across TikTok, Instagram, pop-ups, and web. Let them earn or unlock rewards through social engagement, not just purchases. +Example: A user gets exclusive content or perks for creating UGC with your brand. Bottom Line. Loyalty must be earned over and over through experience, relevance, and emotional connection. Think dynamic loyalty: a system that rewards interaction and go for it. Find my curated search of examples and get ready for your next HIT. Featured Brands: Balmain Benefit Chanel Charlotte tilbury Cerave Fennty L’Oreal OGX YSL #beautypackaging #beautybusiness #beautyprofessionals #experienceretail #luxuryexperiences #genz

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

Do this one thing with your team (and your CEO)! No budget needed. Get everyone, including your most senior bosses, to go mystery shopping. Get them to start with your website on their mobile phone. It will open their minds to what the real experience is for customers. Then go visit your stores and your competitors’ websites and shops. Don’t rely on reports and spreadsheets to understand your customers. They are helpful but cannot substitute the real thing. I learnt the importance of doing customer listening when working with Tim Copper at British Gas. With my team we spent one day every quarter in our contact centres taking calls from real customers. With Mark Vile and the late John Dalkiran at Compare the Market we read all the NPS commentary from customers and learnt how we could improve our service (and meerkat toy delivery). When I was at Audi UK with Andrew Doyle and Antony Roberts we went mystery shopping in car dealerships and constantly tested new ways to improve our website (as that is the biggest customer shopping window for most brands). One of the key targets at Samsung Electronics is NPS. We ask customers how likely they are to recommend Samsung to friends and family. We constantly review and improve to make sure we provide the best service possible. This is why Samsung TVs are the no.1 choice by consumers for 19 years in a row. Marketing isn’t just about ads and pretty pictures. It is making sure the customer experience lives up to the brand promise. More business stories can be heard on Jon Evans excellent podcast Uncensored CMO. The podcast is free on Spotify: https://lnkd.in/eGPak4GW #CMOUncensored #Samsung #CustomerExperience

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!

🗺️ 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.

#payments rails across the globe and the models behind them have evolved in three major (but very different) patterns and yet they are converging in certain ways. Let’s take a look. About half a century ago, magnetic-striped cards triggered a payments revolution. Swiping plastic cards at POS merchant terminals conquered the west, with Visa and Mastercard managing the rails and becoming an almost mighty duopoly. Cards made a smooth transition into the digitized #economy by embedding in smartphones (and even turning them into processors) and becoming the springboard for the rise of the #ecommerce. While the west was transitioning from old cards to chips, China was driving its own local payments revolution that erupted at the beginning of the 2000s and transformed the country from a purely cash economy to a #digital frontrunner. Starting from high smartphone penetration and bank account ownership, China essentially leapfrogged the card-based (western) model moving directly to a digital set-up built on e-wallets and QR codes and driven by two private companies (Alibaba and Tencent) that managed to build vast (2-sided consumer and merchant) ecosystems that transformed them into ubiquitous SuperApps. In parallel, a third pole had been developing in other parts of the world: —     The payments revolution in Africa was led by telecoms (being the only infrastructure available) by means of an e-#money set-up based on mobile phones. Companies such as Kenya’s M-Pesa (launched in 2007) managed to provide long needed basic financial services (saving and transferring funds, making payments or accepting government subsidies) to large swaths of the population. —     Countries like India or Brazil developed over the past few years state-sponsored real-time payments infrastructures, powering multiple bank accounts into a single app under A2A and P2P models. India’s Unified Payments Interface (UPI) has over 300 mn monthly active users recording 60% y-o-y growth, whereas Brazil’s Pix, launched only in late 2020, has managed to become the most popular payments’ method with over 150 mn users. These parallel evolutionary developments could hardly have been more different: a robust decades-old, card-infrastructure in the west (monopolized by two private companies), against a digital, wallet-based closed-loop model in China (powered by 2 giant ecosystems), versus public, state-sponsored, open, real-time rails in India and Brazil. Despite their very different origins and set-up, digitization has been acting as a huge convergence driver lately: digital wallets, super-apps, real-time payments and CBDCs (Central Bank Digital Currencies) are only some of the common underlying elements. As payments evolve to their next phase, a new digital infrastructure is in the making, fast bridging seemingly big structural gaps. Opinions: my own, Graphic sources: Credit Suisse, Alipay, Matthew Brenan, BCB, Bacancy, Alicriti