Retail Space Planning Software

#Amazon just killed the old e-commerce algorithm. Rufus now has memory & it changes the game more than Prime ever did. For 20 years, #ecommerce placements ran on two engines: ▪️Product-based logic → “You bought a phone, here’s a case.” ▪️Crowd-based logic → “People who bought X also bought Y.” That era is over. Now, with Rufus AI memory, a third engine arrives: ▪️Contextual logic → “Yesteday you asked for trail shoes. Today you’re back - here’s a water-resistant jacket that completes your kit.” This is bigger than chat. Rufus memory will fuel every surface on Amazon: Sponsored Display, PDP recos, offsite retargeting. One memory, everywhere. A full-funnel intelligence system that learns once and sells everywhere. Why it matters: 1️⃣ Smarter cross-sell → Rufus won’t waste placements on what was just bought. It will anticipate the next logical purchase 2️⃣ Full-funnel impact → Memory won’t stay in chat. Expect it to power every algorithmic slot across Amazon. 3️⃣ Journey > click → Performance is no longer about CTR. The real metric: How often does Rufus recall and re-recommend your brand across the funnel? 4️⃣ Content = algorithm fuel → If your PDP doesn’t spell out connections (pairs with, next in routine, complementary use cases), Rufus won’t link you into the journey. What brands must do now: ▪️Design ecosystems, not SKUs → Build routines, bundles, and adjacencies. Memory rewards portfolios that tell a story. ▪️Engineer cross-sell signals → Use content to “teach” Rufus where your product fits in the customer journey. ▪️Hit hygiene benchmarks → Near-200 character titles, 7+ visuals, A+ content, 4.3★+, Prime/FBA - still a non-negotiable fundamental priority ▪️Adopt new KPIs → Share of voice in Rufus answers, attach rate, and repeat recommendation frequency. Business impact This is the algorithmic pivot of the decade. Contextual AI shifts Amazon from a #marketplace with recommendations into a shopping brain that curates, recalls, and predicts. Every surface, every placement, every touchpoint is now personalized by a history of interactions. Day 1 for the industry - we will see other #online #OMNIchannel giants follow. Retailers with strong loyalty programs are sitting on a goldmine once they connect life context with shopping intent. If you’re not training contextual algorithms to remember your brand, you’re training them to forget you.

Post 8: Deciphering Brick-and-Mortar Metrics - Essential KPIs for Fashion Store! 📊🏢 Hello everyone! 😊 Continuing our #RetailAnalyticsJourney, let's navigate the metrics labyrinth for brick-and-mortar fashion stores. We'll shed light on Key Performance Indicators (KPIs) that are vital for assessing performance, strategizing operations, and ensuring customer satisfaction in physical stores. ✏ Foot Traffic/Customer Entry👣 This KPI reflects the number of customers visiting the store within a certain period. It provides insight into the store's appeal and can help identify successful marketing efforts or store layouts that attract customers. For example, if 1000 people entered in store last month, the foot traffic/customer entry is 1000. ✏ Conversion Rate 🔁 While foot traffic measures attraction, the conversion rate quantifies action, indicating the percentage of store visitors who make a purchase. It can highlight the effectiveness of sales staff, pricing strategies, and the overall shopping experience. If 300 out of those 1000 visitors made a purchase, the conversion rate would be (300/1000)*100 = 30%. ✏ Sales per Square Foot 🏢 This KPI measures the average revenue a retailer generates for every square foot of sales space. It's a crucial metric for physical stores, demonstrating the productivity of the retail space and helping to optimize store layout and merchandise display. Suppose the store made 500,000 in sales last month and it's 2,000 square feet, sales per square foot would be 500,000/2,000 = 250. ✏ Shrinkage Rate 📉 Shrinkage is the percentage of inventory lost to theft, damage, or administrative errors. High shrinkage can significantly impact profitability. Tracking this rate can help identify operational areas needing attention to minimize losses. If store started with 500 items and only sold 400, but ended up with 80 at the end, the shrinkage is (500-400-80)/500 * 100 = 4%. ✏ Gross Margin Return on Investment (GMROI) 💰 GMROI offers a snapshot of the profitability on inventory investments. It's the gross profit made from selling merchandise compared to the cost of the inventory. High GMROI indicates a successful return on inventory investments, while low GMROI can signal pricing or purchasing strategy adjustments. Suppose company made 50000 in gross profit from an inventory costing you 20000, GMROI would be 50000/20000 = 2.5. Mastering these KPIs will help fashion retailers operate successful brick-and-mortar stores in an increasingly digital world. Remember, it's about harmonizing data and intuition, creativity and strategy. 🎭 Don't forget to share, like, and comment below! Let's keep growing together on this exciting #RetailAnalyticsJourney! 🌟 #retail #fashionretail  #analytics #DataDrivenDecisions

Algorithmic merchandising was our catalyst for a 62% increase in revenue – with the same traffic. Here's our crazy experiment👇 We ran a crazy experiment over the last couple of weeks. While analyzing the data to find the next big growth lever for one of our longest-standing brands I’ve noticed something interesting. Over 32% of the site-wide traffic was hitting collection pages. Also, I identified some outperforming products (hidden champions) that were getting a lot of clicks even though they weren't in prime positions. On the other hand, some products that were getting the most impressions weren't performing as well. People stopped browsing more often when there were a lot of poor performing products in the visible space. So good products didn't even get a chance to be shown to many people. What if we could change the allocation of these products? – Give good products more visibility and bad products less. The challenge now was to find those outliers and position them accordingly. The real breakthrough came when I figured out how to use this data to improve product placement on collection pages. My approach went beyond just tracking clicks. I looked at several key metrics to get a full picture of how each product is doing: → CTR by position → Basket Rate → Purchase Rate: → 90-day Product LTV These 4 indicators were fed into RetentionX's machine learning process to generate a performance indicator that creates a score from 0-100. Products that weren’t performing as well in their current spots were moved to less prominent positions, freeing up space for the real stars — the products that were outperforming expectations. For the first time, our customer had a clear strategy for how to present their products, one that went beyond just gut feelings and good looks. They could now combine our automated insights with their own logic for sorting products—like aligning email campaigns with what customers would see on the site, push new arrivals and demote low stock items. The changes we made had a noticeable impact. Collection pages, which had been somewhat overlooked, suddenly became the go-to place to track what was happening with their customers and how their products were being perceived. The numbers told us we were on the right track, and remember this is a $40M+ brand: → 62% More Profit from the Same Traffic → 27% Additional Increase in Revenue → 23% Higher Conversion Rate → 12% Increase in AOV → 18% Increase in Basket Rates When we saw how well this approach worked, we knew we couldn't keep it to ourselves. So Merchandise Automation is now part of our RetentionX Core product. Read the full case study here: https://lnkd.in/dHh_Sbkp

Stop initialising page objects manually. Playwright fixtures eliminate setup repetition at the architecture level. ❌ The Problem When writing Playwright tests, you end up creating the same page objects over and over in every single test. You type things like "new LoginPage" and "new HomePage" repeatedly. It's tedious, makes your tests longer, and if something needs to change, you have to update it in lots of places. ✅ The Solution: Custom Fixtures Playwright lets you set up page objects once and then use them automatically in any test. Instead of manually creating them each time, you define them in a fixtures file and Playwright provides them ready to use. Think of it like a coffee machine - instead of manually grinding beans and boiling water every morning, you just press a button and get coffee. ❓ How It Works: Step 1: Create Your Fixtures File Make a file called "fixtures.ts" where you tell Playwright about your page objects. You import your page classes, define which ones you want available, and extend Playwright's base test object with them. This is a one-time setup. Step 2: Use in Your Tests Now in your tests, instead of creating page objects, you just list them as parameters. Playwright sees you need "loginPage" and automatically creates it for you. Your test code gets much shorter and focuses on what you're actually testing. The Benefits: ✅ Less typing - No repeated initialization code ✅ Easier to read - Tests are shorter and clearer ✅ One place to update - Change initialization logic once, applies everywhere ✅ Faster writing - Just list what you need as parameters ✅ Auto cleanup - Playwright handles object lifecycle ❗ Why This Matters: Without fixtures, a checkout test might have four or five lines just creating page objects before you even start testing. With fixtures, you skip straight to the actual test steps. The setup happens behind the scenes. You can even add common navigation or setup steps to your fixtures. For example, make the loginPage fixture automatically navigate to the login URL, so every test using it starts on the right page. ❓ When to Use This: Use custom fixtures whenever you're copying the same page object creation code across multiple tests. If you find yourself typing "new SomePage" more than once or twice, it's time to make a fixture. ✅ Getting Started: 1) Create a fixtures file 2) Import your page objects 3) Extend the test object with them 4) Use your custom test object in tests 5) List page objects as test parameters Done. Your tests become cleaner and you save time every time you write a new one. -x-x- To learn and implement a scalable Playwright framework using JavaScript/TypeScript, refer: https://lnkd.in/gHYidnfr #japneetsachdeva

I’ll give you an insider truth that nobody talks about in retail fixtures: Cross-selling isn’t a sales tactic, it’s a layout tactic. And fixtures decide whether it works or not. Think about it: You walk into a store to buy denim. You’re open to picking up a shirt if it looks right. But the denim and shirt sections don’t flow into each other. The fixtures create a hard stop. You pick your jeans and walk straight to billing. That’s an entire basket gone. I’ve seen this mistake in both luxury and fast fashion: → Isolated fixtures: Sections look pretty in silos but don’t “talk” to each other. → Rigid shelving: No space for cross-merchandising (jeans + belts + wallets). Staff can’t experiment, customers can’t discover. → Wrong sightlines: Customers can’t even see the next category from where they stand. Out of sight = out of cart. Here’s the psychology: Good fixtures guide the eye, create flow, and make it effortless for a customer to move from one product to another. They don’t just “display,” they connect. The brands that get this right don’t just sell more, they sell smarter. One pair of jeans turns into a shirt, a belt, and sometimes even a jacket. But the brands that don’t? They lose revenue they never even knew they had. So next time you think fixtures are just about storage, remember this: Every shelf, rail, and gondola is either killing or multiplying your basket size.

𝗠𝗼𝘀𝘁 𝗳𝗶𝘅𝘁𝘂𝗿𝗲 𝗶𝘀𝘀𝘂𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗰𝗮𝘂𝘀𝗲𝗱 𝗯𝘆 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻. They come from tolerance accumulation. In fixture design, it’s common to validate individual dimensions and assume the system will behave correctly. But in reality, every locator, surface, and interface contributes to a tolerance chain. And that chain defines your repeatability. The common mistake: Evaluating features in isolation instead of as a system. A fixture can meet all nominal dimensions and still: • Misalign parts • Generate variation between cycles • Affect downstream assembly Why it matters Repeatability is not defined by one feature. It is defined by: • Datum reference strategy • Contact conditions • Stack-up across locators and part geometry If the tolerance chain is not controlled, the fixture becomes unstable in real production. How to evaluate it before fabrication • Define a clear datum reference frame (not just nominal datums) • Map the tolerance chain from part to fixture to assembly • Identify critical interfaces affecting positioning • Evaluate worst-case or statistical stack-up depending on process A fixture that “fits” in CAD can still fail in production. Because variation does not appear in isolation. It appears as accumulation. Analyzing tolerances before fabrication avoids costly rework later. #FixtureDesign #ToleranceStackUp #ManufacturingEngineering #DFM #ToolingEngineering

Everyone talks about “data-driven retail.” Offline stores don’t fail because of lack of data. They fail because no one translates learning into store math. Here’s how modern learning (AI, behavioral science, analytics) actually works on a footwear shop floor 👇 1️⃣ Visibility beats variety Research: Choice overload reduces conversion. Showing 18 sandals on a bay drops conversion vs showing 8 curated options. Math: Conversion ≈ f(1 / Options) If conversion drops from 32% → 26% due to clutter: Sales loss = Footfall × 6% × ASP 👉 Hide depth. Sell clarity. 2️⃣ Elasticity decides where shoes should live Learning: Price Elasticity of Demand (PED) Fashion heels PED ≈ -2.0 (highly elastic) Core black pumps PED ≈ -0.5 (inelastic) Store translation: Elastic styles = eye-level, high-traffic zones Inelastic styles = destination zones 👉 Move eyes, not discounts. 3️⃣ Heatmaps become walk paths Digital insight: Click heatmaps show attention zones Offline equivalent: First-stop walk path If 65% of customers hit Zone A first and your hero SKU isn’t there: Lost opportunity = Footfall × 65% × Conversion × ASP 👉 If it’s not seen in 5 seconds, it’s dead stock. 4️⃣ Recommendation engines become staff scripts Digital: “Customers also bought…” Footwear execution: Each core sneaker gets 2 add-on prompts (care + socks / insoles) UPT from 1.2 → 1.35 Revenue lift ≈ 12.5% without adding footfall 👉 The best AI in store still wears a name badge. 5️⃣ Depth planning beats replenishment panic Learning: Fast selling ≠ replenish more Footwear math: Wrong size curve = sell-out illusion Right size curve = full-price longevity 👉 Chasing sales creates markdowns. Planning depth creates margin. 6️⃣ Pricing perception matters more than pricing itself Behavioral science: Customers anchor prices visually Footwear example: Showing AED 499 next to AED 699 increases full-price acceptance of AED 599. Perceived value ≠ Price It’s relative comparison elasticity. 👉 Price ladders sell better than price cuts. 7️⃣ Measure less. Act more. Replace dashboards with 5 daily metrics: Conversion % Full-price sell-through Bills with 2+ items Top-20 SKU availability Time-to-first sale If a metric doesn’t change tomorrow’s behavior, delete it. Final thought Offline retail doesn’t need more data. It needs better translation into sightlines, scripts, depth and math. When learning meets store reality, margin goes up before discounts go live. #RetailMath #FootwearRetail #CategoryManagement #OfflineRetail #BehavioralEconomics #Merchandising

70% of your storage lockers are empty. Your rooftop generates $0. Your common areas cost you money. There's revenue hiding inside your building, but you're just not collecting it: Most operators can tell you their unit count down to the decimal. Ask them how many storage lockers they own, and you'll get…a blank stare. That gap is where ancillary revenue is hiding. Parking and pets have been the go-to playbook for years. They’re proven, but they’re also mature. And when rent growth slows, the operators pulling ahead on NOI aren't pushing harder on leasing. They're looking inward and asking: What's already inside the building that could be generating revenue? Example: the average multifamily storage locker sits at about 30% occupancy. That remaining 70% is dead space. But operators are now refitting and renting that space to non-residents in the surrounding community for recurring income. Almost zero operational lift. And that's just storage. Rooftop infrastructure leases (cell towers, 5G, ISP equipment) can generate $1,000 to $5,000+ per month per installation on contracts spanning 10-20 years. Residents don't know they're there. Common areas that cost money to maintain are being turned into revenue lines through: • Co-working pods • Ticketed resident experiences • Sponsor-funded programming that borrows directly from the hospitality playbook • Digital signage in high-traffic areas (package rooms, elevator lobbies, fitness centers) The pattern is the same: Operators are finding ways to monetize space, access, and infrastructure they already own. Without: • More headcount • Resident friction • Major capital investment The best ancillary strategies share three traits: • Recurring or contract-based revenue • Little to no ongoing staff involvement • Feels like it's solving a problem, not nickel-and-diming tenants The ones that fall apart? Constant oversight, unrealistic utilization, and new friction. That's three examples. The full report covers more, including strategies most operators haven't considered, with input from our Advisory Council on what's working and what's not worth the effort. No, Parking and pets aren't going anywhere. But the operators who figure out the next layer now will have a meaningful NOI advantage. Full report on Insights by Blueprint. Link in comments. What's the most creative ancillary revenue play you've seen?

Looking at this fascinating research from Alibaba Group researchers, I'm impressed by their innovative approach to solving one of e-commerce's biggest challenges: recommending long-tail items effectively. The Problem: Traditional Item-to-Item (I2I) recommendation systems struggle with data sparsity, especially for products with limited user interactions. Nearly 20% of items in major e-commerce platforms have fewer than 5 purchases, making them nearly invisible to conventional algorithms. The Solution - LLM-I2I: Instead of rebuilding recommendation models, the researchers took a data-centric approach using Large Language Models to enhance training data quality. How it works under the hood: LLM-based Data Generator: Uses supervised fine-tuning on LLaMA2-7B-Chat to synthesize realistic user-item interactions. The generator analyzes user behavioral patterns and creates plausible future interactions, with special focus on long-tail items through a weighted loss function. LLM-based Data Discriminator: Acts as a quality filter, evaluating synthetic interactions against real user behavior patterns. Only high-confidence synthetic data (scored as "Yes" with 1.0 confidence) gets added to training sets. Smart Integration: The refined synthetic data combines with original behavioral data to train existing I2I algorithms like BM25, BPR, YoutubeDNN, and Swing without architectural changes. Real-world Impact: When deployed on AliExpress, this approach delivered 6.02% improvement in recall numbers and 1.22% boost in gross merchandise value. More importantly, long-tail items saw dramatic improvements - up to 85.71% better performance in industrial datasets. The beauty lies in preserving existing system infrastructure while dramatically improving recommendation quality through intelligent data augmentation. This demonstrates how LLMs can enhance traditional recommendation systems without the computational overhead of real-time inference.

Planning in retail is where judgement meets maths. Two questions shape most outcomes: 1. How much to buy (OTB) 2. How to spread it across width and depth Example: You have 100 stores and an OTB of 100000 units. Now the real trade-offs begin. 1. Do you play more on depth or more on options? 100 options × 1000 depth across the network or 200 options × 500 depth? 2. What % of those 1000 units goes into the first allocation? 3. What % should be held back for replenishment? 4. How many weeks will 1000 units survive before most stores hit cut sizes? 5. At what point does increasing width dilute availability, or increasing depth slow sell-through and freshness? 6. How do you phase monthly inwards to maintain newness? 7. How do options and depth align with the display capacity of each store? Now add real-world constraints - If the network is already carrying old-season merchandise and pullbacks aren’t possible: 8. How do you solve for OTB without blocking fresh inwards? 9. How do you ensure minimum freshness, even at bottom-tier stores? 10. How much newness is required just to keep the system healthy? These aren’t gut calls alone. What helps is structuring the problem into clear equations using basic maths: - Averages & weighted averages - Standard deviation (store dispersion) - Simple regression (sales vs time, depth vs sell-through) When judgement is backed by maths, width, depth and phasing stop being debates and start becoming decisions.