Merchandise Mix Optimization

Amazon just rolled out a pretty cool update to Brand Metrics. Here's what you need to know: New features: -Category median benchmarks -Category top benchmarks -Percent change view Why it matters: 1. Compare your brand against category trends in real-time 2. Gauge if your growth is outpacing or lagging the category 3. Get instant insights without exporting data For example, say your beverage brand sees a 20% increase in shoppers. Sounds great, right? But what if the category median is up 25% and top performers are up 30%? This update helps you spot these crucial nuances instantly. The most useful tool is the percent change view. This feature will be huge for understanding your brand's performance in context. You can quickly see how you stack up during events like Prime Day, understand if a dip in numbers is brand-specific or category-wide, and measure the impact of your marketing efforts on awareness, consideration, and purchase metrics. My advice: Make the percent change view your first stop when analyzing performance changes. It'll help you differentiate between market trends and brand-specific issues, giving you the insights you need to make informed decisions.

📊 Sales Performance & Profitability Dashboard | End-to-End Analysis 🚀 Project Overview Developed an interactive Sales Performance Dashboard using a 53K+ row transactional dataset to analyze revenue growth, profitability, discount impact, regional performance, and product efficiency. The project focuses on transforming raw sales data into actionable business insights for strategic decision-making. 🔑 Key Business Insights • Generated $2.30M in total revenue with $286K profit, achieving a 12.47% profit margin • Strong year-end sales growth, but profit growth lags due to high discounting • West (38%) and East (32%) regions lead profitability, while the South region underperforms • Technology category drives the highest revenue, but Tables consistently generate losses • Average discount of 15.62% significantly impacts margins beyond safe thresholds • Standard Class shipping delivers the best revenue-to-cost balance 📈 Dashboard Highlights ✔ Monthly Sales & Profit Trends ✔ Category & Sub-Category Profitability Analysis ✔ Discount vs Profit Impact Analysis ✔ Region-wise Profit Contribution ✔ Shipping Mode Performance ✔ Top & Bottom Performing Products 🛠 Tools & Technologies • Excel – Data cleaning, calculations & dashboard design • SQL – KPI aggregation, trend analysis & business queries • Power BI – Interactive visuals, DAX measures & storytelling 🎯 Business Value This dashboard enables businesses to: • Optimize pricing and discount strategies • Improve regional and product-level profitability • Shift from volume-driven to margin-driven growth • Make data-backed decisions with clarity and confidence 📌 Always open to feedback, collaboration, and data-driven discussions. #DataAnalytics #PowerBI #SQL #Excel #BusinessIntelligence #DashboardDesign #DataStorytelling

✨ How Do You Really Measure Quality Score at Scale—And Why Should You Bother? If you’re managing 5,000+ Google Ads campaigns across categories—think baby shampoo, skincare, haircare—you’ve probably wondered: 🧐 How do I report Quality Score meaningfully, beyond the keyword level? After all, Google Ads only shows QS per keyword. But CMOs and performance leads don’t want 20,000 rows—they want clarity: ✅ Which product categories are dragging down performance? ✅ How do brand vs. generic campaigns stack up? ✅ Are expensive keywords with low QS killing ROI? Why does this matter? Because Quality Score impacts your entire bidding ecosystem: 🎯 Manual CPC? Low QS inflates your CPCs for the same ad rank. 🎯 tCPA? Low QS reduces eligible impressions (hurts conversion volume). 🎯 Max Conversion Value? Low QS makes auctions more expensive, lowering ROAS. In short—even smart bidding can’t fully outrun poor Quality Score. Here’s my structured approach to calculate and roll up Quality Score at ad group and category level: 🔹 1️⃣ Extract Keyword-Level Data Download reports with: ✅ Keyword ✅ Match Type ✅ Campaign ✅ Ad Group ✅ Impressions ✅ Clicks ✅ Quality Score 🔹 2️⃣ Weight Quality Score by Impressions Don’t average blindly—weight by volume: Weighted QS = Σ(QS × Impressions) / Σ(Impressions) Example: Keyword A: QS 7, Impressions 10,000 Keyword B: QS 4, Impressions 500 Weighted QS = 6.86 🔹 3️⃣ Aggregate by Ad Group & Category Group keywords: 📂 Ad Groups 📂 Campaigns (Brand vs. Generic) 📂 Product Categories Apply the weighted calculation to see where issues are concentrated. 🔹 4️⃣ Visualize & Act Use: 📊 Google Looker Studio dashboards 🔥 Heatmaps in Sheets to flag underperforming areas fast. How does this impact overall performance? ✅ Manual CPC: Lower QS = Higher CPC = Lower margin ✅ tCPA: Lower QS = Fewer auctions won = Less volume ✅ Max Conversion Value: Lower QS = Lower impression share In real accounts, I’ve seen a +15–25% CPC reduction by lifting category QS just 1–2 points. Takeaway: Scaling performance means engineering clarity. Quality Score is not just a metric—it’s a lever that multiplies the impact of your bidding strategy. If you’re navigating this complexity—or want to build frameworks to benchmark QS across thousands of campaigns—let’s connect. 👉 How are you measuring Quality Score across bidding strategies? Share your thoughts! #GoogleAds #PerformanceMarketing #EcommerceGrowth #DigitalStrategy #MarTech

Most FMCG brands think their hero product is simply their biggest-selling SKU. That’s not how retailers like Walmart think. Walmart merchants don’t just evaluate products based on: * volume * margin * velocity They evaluate products based on how products influence: * shopper perception * basket size * store traffic * category growth * supply chain efficiency * digital conversion * overall value image This is where “hero products” become strategically critical in Revenue Growth Management (RGM) and Price Pack Architecture (PPA). A hero SKU is the product that disproportionately shapes shopper behavior. It becomes: * the price benchmark * the basket anchor * the retailer traffic driver * the mental reference point for value Think about categories like: * eggs * detergent * diapers * bottled water * soda * paper towels Shoppers remember these prices. If Walmart wins on those hero SKUs, shoppers often assume: “Everything else is competitively priced.” That perception drives billions in retail behavior. What’s fascinating is that the hero SKU is not always the highest-margin item. Sometimes Walmart intentionally protects pricing on certain hero products because the total basket economics matter more than the individual SKU margin. And today, hero products are evolving rapidly. The in-store hero SKU may not be the digital hero SKU. On Walmart.com, hero products are increasingly influenced by: * search ranking * ratings & reviews * subscription behavior * delivery speed * digital shelf visibility * image quality * conversion performance This creates massive new challenges for FMCG RGM teams. The winning assortment strategy is no longer: “What sells the most?” It’s: “What product best drives the total ecosystem?” That includes: * category growth * retailer profitability * shopper loyalty * basket attachment * omnichannel conversion * premiumization opportunities This is where conjoint research becomes incredibly powerful. Historical scanner data explains what happened yesterday. Conjoint helps teams simulate: * future shopper behavior * price elasticity shifts * pack architecture changes * assortment optimization * promotional mechanics * retailer-specific reactions * digital shelf trade-offs * premiumization opportunities Most importantly, conjoint helps identify: * which SKU SHOULD become the hero product * which SKU recruits new shoppers * which SKU improves retailer economics * which SKU drives larger baskets * which SKU protects value perception * which SKU unlocks profitable growth Because the future hero SKU is not always obvious from historical data alone. A smaller pack may recruit more households. A premium pack may increase margin without hurting penetration. A retailer-exclusive bundle may dramatically improve conversion. A digitally optimized SKU may outperform the traditional shelf leader. The future winners in RGM will be the teams that understand: The hero product is no longer just a product. It’s a strategic growth lever powered by shopper understanding, retailer strategy, and predictive conjoint insights.

📦 SKU Proliferation **More options ≠ more sales.** As Category Managers, we’ve all felt the pressure to expand assortments—more flavors, colors, sizes. But this pursuit of choice often backfires: 🎯 Demand gets diluted across too many products 📉 Forecasting accuracy drops 🏭 Production complexity increases 🛑 Higher chances of stockouts AND overstocks 💸 Inventory holding costs shoot up ⚠️ The Reality Check: Imagine scaling from **3 chopping boards to 12**. Suddenly: • 12x forecasts • 12x shelf spaces • 12x packaging/logistics setups …while 80% of sales come from just 2 core SKUs. Result: Excess inventory, wasted capital, confused shoppers. 🚀 The Fix? **Strategic SKU Rationalization:** ✔️ Ruthlessly cut low performers (ABC analysis is key) ✔️ Collaborate with Supply Chain & Sales teams ✔️ Empower store feedback—they know what sells ✔️ Optimize shelf space for clarity, not clutter ✔️ Focus on *quality* of assortment, not quantity #### 📌 Remember: **Winning supply chains aren’t defined by SKU count—but by knowing what *not* to carry.** #CategoryManagement #SKURationalization #RetailStrategy #SupplyChainOptimization #InventoryManagement

“Don’t Just Stock It—Make It Count: The MBQ , Availability and Fill Rate Advantage.” In retail, the key to success lies not just in stocking products, but ensuring that the products are adequately stocked is critical for driving sales. Understanding critical inventory metrics—Minimum Base Quantity (MBQ), Availability, Fill Rate—is essential to optimizing sales and meeting customer expectations. Further to truly make an impact, these metrics must be applied at the assortment level. 🔹Minimum Base Quantity (MBQ): Units required on the shelf to maintain product visibility and meet customer demand. It’s not enough for a product to be “available”—it needs to be sufficiently stocked to catch the customer’s eye and drive purchases. It’s a critical measure, its true value is realized when applied to specific assortments that cater to different customer preferences. Ensuring each assortment meets its MBQ helps guarantee that the diverse needs and choices of customers are adequately addressed. 🔹Availability: Is more than just having a product on the shelf—it’s about meeting a set percentage of the MBQ (e.g., 75%) to ensure the product is impactful. If the stock falls below this threshold, the product might as well be considered “unavailable,” as its impact on sales diminishes sharply. Customers expect a range of options within an assortment, and if one part of the assortment is understocked, it can lead to a perception of unavailability even if other products are present. Focusing on availability at the assortment level ensures that all customer needs are met, not just those for the most popular items. 🔹Fill Rate: This measures how well we are meeting customer demand from your current stock, Overall fill rates can be misleading if they don’t reflect the availability of specific products within an assortment that address different customer needs and preferences. By monitoring and optimizing fill rates within each assortment, you can ensure that every customer finds what they’re looking for. 🔸Loss of Sale: When availability falls below the MBQ threshold at the assortment level, you’re not just risking a poor shelf presence—you’re also risking direct financial loss. The loss of sale is calculated by comparing potential sales (based on historical average daily sales) with actual sales on days when availability was low. This analysis can help in minimizing missed revenue opportunities. 🔸Backend Operations: A robust backend—covering vendor fill rates, warehouse stocking, and supply chain speed—is essential for sustaining high MBQ fill, availability, and fill rates at the front end, directly impacting your ability to meet demand and minimize loss of sales. 🔸Benchmarks: Varies between a fashion rand a grocery retailer due to differences in product types, demand patterns, purchase frequency and customer expectations - 1. Fashion Retailer • Availability: 85-90% • Fill Rate: 90-95% 2. Grocery Retailer • Availability: 95-99% • Fill Rate: 98-99%

When creating SEO strategies for ASIN variations, the approach differs slightly from optimizing a single ASIN. 🧩 Why? Because variations open the door to getting more creative with backend keywords and alt text, allowing you to cast the widest possible net for indexing. 🤝 Here’s how: ▪️ 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬: Each ASIN allows up to 250 characters for backend (generic) keywords. For a single ASIN, you’re capped at 250 characters. But with 10 variations, you could use up to 2,500 characters by using different keywords across each variation. ▫️ Include core keywords across all ASINs, but use this opportunity to diversify with related terms, misspellings, and other relevant keywords to maximize character usage. ▪️ 𝐀𝐥𝐭 𝐓𝐞𝐱𝐭: Apply the same concept to alt text in A+ Content. You can assign different alt text sets to each child ASIN, further increasing the total volume of indexed keywords. The goal? 🤔 To index for more keywords, improve rankings, and drive greater relevancy. Over time, this translates into increased organic sales—the most profitable kind of sales on Amazon. This strategy requires thoughtful execution, but the payoff is worth it. Are you using this approach for your ASIN variations? Let me know your thoughts! 🫵 #Amazon #ecommerce #digitaladvertising #SEO #ecommercegrowth

Mastering Assortment Strategy in Retail: 8-Step Approach to Customer Delight The secret to retail success?  It's not just about having products, it's about having the right products for the right customers at the right price.  This is where a robust assortment strategy comes into play. Trying to summarize the assortment strategy through my 20+ years of retail and e-com experience. We will dive deep into how a retailer should tailor assortment to meet the needs of diverse customer cohorts.  This goes beyond simple demographics! 1. KYC (know your customer) - Customer Segmentation (Cohorts) : It can never be a one-size-fits-all strategy.  Analyse data to segment customers based on location (metro vs. tier 2/3 towns), demographics (age, gender, region), lifestyle (high-rise dwellers vs. independent houses), and even pincode-level analysis for e-commerce to deliver personalized experiences. 2. Value for Money (VFM):  Pricing is king! leverage EDLP (Every Day Low Prices), large pack savings, and strategic entry-level priced assortment (think 49/-, 99/- deals) to deliver exceptional value. 3. Seasonality & Festivals:  India's vibrant festivals and seasons are key drivers of demand.  Proactively plan relevant assortment well in advance, ensuring customers find exactly what they need when they need it – building trust and loyalty. 4. Brand Strategy:  A balanced portfolio is crucial.  Strategically incorporate national, challenger, and regional brands to offer diverse choices.  Regional brands, often offering incredible VFM, make an instant connection with the customers. 5. Private Label (PL) Power: In categories where branding is less crucial, private label products offer significant value, driving strong customer stickiness.  This is particularly evident in staples like pulses and dry fruits. 6. Evolving Trends:  The world of retail is constantly changing. monitor emerging trends, innovations & global selection through social media, market research, and supplier insights, ensure to include the latest and most sought-after products into assortment.  Be agile and adapt quickly! 7. Supply Chain Efficiency: Assortment strategy cant be just about marketing; it's about operational efficiency also. Carefully consider supply chain costs, warehousing, and logistics to ensure products are readily available. Availability is the Key 8. Continuous Improvement: Assortment planning is an ongoing process.  conduct quarterly reviews, analyzing velocity, profitability, and emerging trends to continuously optimize offerings.  Consider "assortment modules" for faster scaling and roll-out. This 8 step comprehensive approach will allow to offer a tailored shopping experience that truly resonates with diverse customer base. What are your key strategies for optimizing assortment? Share your insights in the comments! #retail #assortmentstrategy #supplychain #FMCG #ecommerce #merchandising #categorymanagement #omnichannel #indiaretail #valueformoney #privatelabel

Growth is determined by the market itself - a fact that skews a lot of how we think about the performance of brands and their marketing. A company whose product is part of a fast growing category, like AI tools, will simply grow much faster than one in a declining one, such as legacy CDPs. Of course, identifying where there is growing demand is a major part of good marketing strategy, but it can also be a fog that hides how effectively we're winning our share of it. What if you are growing, but slower than the underlying category growth? That's an indication that you're leaving revenue on the table which could be yours if you successfully held your market share position. Compare that to a brand who is actively over-performing against the category - a path that leads towards increased market share and which carries real long-term company value. Take this example below of branded search data for a cohort of brands in the CDP category, which has seen more than 20% decline this year compared to last. Unsurprisingly, all brands in the cohort are seeing individual brand search decline, but not at the same rates. Once you account for the underlying category growth, you get a much more telling comparison. As Dale W. Harrison put it to me, it provides a "DiD (difference-in-difference) view...a normalized change in brand-aware search volume as a function of overall category movement." Suddenly, even amidst a declining category, there are clear winners who are outpacing the category, ones who are holding their position, and ones who are underperforming. Of course, what we don't know in this view is the relative marketing budget contributions of each brand, but for anyone in the table, it could provide an important data point to say whether the marketing mix is right. For example, if a brand underperforming against the category slashed its marketing budget, it might well be an indicator to reverse that decision to avoid losing long-term share. But if they didn't, it could be an important signal that the competitive set has changed their game and the brand needs to match. With all metrics, if we don't have something to compare it against, we can't truly know whether it's truly good or bad. Factor in the competitive set, and the overall growth of the category, and you gain a perspective that few brands truly have.

The Hidden Cost of Choice: What H&M and UNIQLO Reveal About Assortment Economics An assortment can look like a commercial advantage while becoming an operational burden at the same time. The comparison in this graphic is compelling. An online assortment analysis indicates that H&M lists roughly three times as many active styles as UNIQLO, while their parent companies reported markedly different FY2024 operating margins, approximately 7.4% for H&M Group versus 15.9% for Fast Retailing, the parent company of UNIQLO. That does not mean fewer styles automatically generate higher margins. The businesses differ in geographic exposure, brand portfolios, sourcing models, pricing architecture, store networks, vertical integration, as well as cost structures. Operating margins reflect the interaction of many strategic decisions, not one variable in isolation. How much economic value does every additional style truly create? H&M has built its model around broad choice, rapid trend response, frequent assortment renewal, serving multiple customer preferences across global markets. UNIQLO follows a philosophy centred on functional essentials, longer product lifecycles, consistent demand patterns, concentrating resources around fewer product families. Neither model is inherently superior. They simply optimise different objectives. An unusual way to view this debate is through organisational attention rather than inventory. Every additional style requires forecasting, buying, allocation, photography, content creation, pricing, replenishment, merchandising, quality control, store execution, markdown decisions, analytical monitoring, management attention. Products compete for customer demand, yet they also compete for internal resources. That cost rarely appears in financial statements, although it influences performance every day. Choice creates value until additional options begin redistributing existing demand rather than expanding it. Beyond that point, complexity may grow faster than customer benefit. Conversely, a concentrated assortment carries its own risks. Greater dependence on fewer products means forecasting errors, sourcing disruptions or changing consumer preferences can have a larger commercial impact. It is understanding the level of complexity required to support the brand proposition without creating unnecessary organisational friction. The companies in this comparison illustrate two different philosophies for managing uncertainty. One responds by offering more alternatives. The other reduces uncertainty by focusing demand around fewer products. Neither philosophy guarantees superior financial performance. Yet both demonstrate that retail success depends less on maximising choice than on aligning product strategy with operational capabilities. Data sources: Fast Retailing FY2024 Results, H&M Group Full Year Report 2024, Lectra and FashionABC online assortment analysis. Graphic created by YOOBIC.