Retail & Merchandising

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.

Customers didn’t stop spending. Companies stopped serving. That’s the headline of an interesting article from CNN, which is linked in the comments. As we move into retail earnings season, it’s important to keep this nugget of truth in mind. A lot of companies, especially those which are performing badly, will blame external factors like the economy. Is this valid? Sometimes; but more often than not, it isn’t. The chart below partly shows this. It maps the Q1 revenue growth for retailers against customer satisfaction scores from the same period. We measure 42 different aspects of satisfaction on our consumer panel, and the scores below are an average. There is a general trend. A lot of low- or no-growth companies like CVS, Walgreens and Kohl’s also have low satisfaction scores. Basically, they’re not getting things right for customers. Comparatively, retailers like Dick’s, Abercrombie, TJMaxx, Amazon, Walmart and so forth, broadly satisfy their customers and secure growth. The most interesting part, however, is the top left: companies that have good satisfaction scores but are not generating much growth. Generally, these are firms that have some can, genuinely, blame external factors. Home Depot and Lowe’s are excellent retailers, but they’re coming off enormous revenue growth during the pandemic at a time when the housing market is incredibly soft. And Bath & Body Works is still resetting after the big home fragrance surge during the pandemic and is facing stiffer competition. The lesson is don't automatically believe the excuses. Look and see what other things are driving success, or failure. #retail #retailnews #perfromance #economy #customersatisfaction __ Chart shows Q1 growth rates against customer satisfaction scores from 0 to 100 for the same period.

🚨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.

Tesco’s share price is up almost 50% in the last two years. The business is on a roll, reporting strong financial performance in the latest update which I recently discussed with BBC News. ➡ Strong Financial Performance: 💠Retail like-for-like sales up 2.9%, with UK (+4.0%), ROI (+4.7%), and Central Europe (+0.6%) 💠Adjusted operating profit increased 10.0% to £1,555m 💠Market share reaches 27.8% - highest since January 2022 ➡ Omnichannel Excellence: 💠Large stores leading with 4.2% growth 💠Digital sales surging - online up 9.3%, now 13.5% of UK sales 💠Whoosh rapid delivery expanding to 1,460 stores 💠1.3M online orders per week ➡ Digital & Loyalty Innovation: 💠Clubcard penetration reaching new heights: UK 82%, ROI 85%, CE 87% 💠4.9M customers receiving personalised 'Clubcard Challenges' 💠Growing retail media platform showing strong advertiser engagement What's fascinating about Tesco's performance isn't just the numbers - it's the sophisticated ecosystem play that's emerging. Here's why I believe Tesco are in such a commanding position: 📲 Data Monetisation Tesco isn't just running a retail media network; they're orchestrating the UK's largest closed-loop retail ecosystem. With 23M Clubcard households, they've created a virtuous cycle: customer data drives personalisation, which increases engagement, which generates more data. Their partnership with dunnhumby transforms this into a powerful advertiser proposition - true closed-loop measurement across online and offline touchpoints. This is retail media 2.0. What's more, retail media typically delivers 60-70% margins, compared to traditional grocery retail's 2-5%. By building this high-margin revenue stream, built on strong tech underpinnings, their media tech proposition has become a key strategic asset boosting their valuation. It's a page out of the Amazon/Walmart's media businesses playbook where this part of the business is valued at multiples of their retail operations. 🎯 Core Business Focus The 4.0% like-for-like growth tells a deeper story about strategic discipline. Over the past decade, Tesco has systematically simplified its business model - exiting non-core markets and doubling down on what they do best: food retail with a magic sprinkle of data. It’s been fascinating to see how sometimes doing less allows businesses to achieve more. 🤖 AI-Powered Customer Centricity The deployment of AI to analyse shopping patterns isn't just about efficiency - it's about reimagining the customer relationship. By using predictive analytics to anticipate customer needs and suggest relevant products, Tesco is moving from reactive to predictive retail. In my view, the next stage of personalisation. These results showcase Tesco's ability to balance traditional retail strength with digital innovation while maintaining strong market leadership.

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

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

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

"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)

The Evolving Face of the US Homebuyer The National Association of Realtors' (NAR) 2024 report provides a fascinating snapshot of the US housing market’s buyer profile that looks significantly different than it did just a few years ago. The data reveals a changing homebuyer. The average buyer age has climbed to a record 56, underscoring the impact of high housing costs and rising interest rates that have sidelined younger would-be buyers. For first-time buyers, the average age is now 38, nearly a decade older than it was in the early 1980s. These changes signal a more mature buyer who brings accumulated wealth and likely more significant financial security to the table. Additionally, a fifth of all home purchases were made by single women, a notable demographic shift reflecting both a societal change in homeownership goals and an economic shift in who can afford to buy. By contrast, single men comprised only 8% of recent buyers. This snapshot highlights what many are calling a “bifurcated housing market,” where those able to buy homes are increasingly established, wealthier individuals, often using home equity from previous properties to secure cash purchases or make substantial down payments. This market has been largely inaccessible to younger buyers, who continue to face affordability challenges, limited savings, and reduced opportunities for financial support in the form of lower mortgage rates. With affordability gauges near record lows, first-time homebuyers hold a mere 24% share of the market, down dramatically from the 40% share held in pre-Great Recession years. Rising prices and interest rates have compounded these barriers, leading to a market where nearly three-quarters of all buyers have no children under 18 at home, reflecting an older and more established buyer profile than in decades past. While this report offers a look back, the trends it captures underscore a potential turning point. Recent mortgage application data suggests that prospective buyers who had previously been priced out or sidelined may begin to re-enter the market as interest rates stabilize. If these sidelined buyers do return, particularly younger and more diverse demographics, the profile of the typical buyer could again start to shift, gradually increasing diversity in age, household composition, and race among homebuyers. At Havas Edge, we’re continually analyzing these demographic shifts to support brands in delivering timely, targeted strategies that meet the realities of today’s buyers and the anticipated resurgence of those who’ve been waiting on the sidelines. #RealEstate #Homebuyers #MarketTrends #HousingEconomics #ConsumerInsights

60% of today’s fashion is made from fossil fuels. But these next wave of fabrics is grown, not extruded. From mushrooms to microbes, meet the smartest, most desirable fabrics of the future. 🌱 Mycelium Leather – Mushroom roots reimagined as biodegradable luxury. 🌊 Algae & Seaweed Textiles – Marine-grown fibres turning seaweed into streetwear. 🌾 Hemp & Crop Waste – Regenerative fibres like hemp and banana spun into essentials. 🧫 Bacterial Cellulose – Microbial silk that's plastic-free and planet-friendly. 🍇 Fruit-Based Leathers – Grape, apple, and orange peels turned into sleek, cruelty-free leather. 🌬️ AirCarbon – Pollution-eating microbes creating carbon-negative fashion. 🧪 Lab-Grown Bioleather – No cows, no chemicals, only science-backed, scalable leather. 🕸️ Bio-Spun Silk – Spider-free silk, brewed by microbes for strength and softness. ☁️ CO₂-Derived Fibres – Captured carbon emissions woven into wearable tech. 🧵 Bio-Based Stretch – Stretchy, compostable yarns made from corn and sugar. 🌱 Which material would you wear first? Drop it below. #plantbased #innovation #slowfashion #veganism