Forecasting Ecommerce Demand

How to Build a Killer Time Series Forecasting Project for Your Data Science Portfolio If you want to showcase serious machine learning skills in your portfolio, a time series forecasting project is a great way to stand out. It touches multiple advanced areas, from feature engineering to model tuning and error analysis. A perfect playground for this? The M5 Forecasting Accuracy Competition on Kaggle https://lnkd.in/gggecJrQ Let’s break down how to approach it like a pro 👇 Step 1: Choosing and Understanding the Dataset Start with exploratory data analysis (EDA). In the M5 dataset, you’ll find daily sales for thousands of products across multiple stores and categories. Ask - Are there seasonal or holiday trends? How does demand vary across states or categories? Are there anomalies or missing data? Then, do feature engineering - Create calendar features (day of week, month, holiday flags), Add lag features and rolling means to capture trends, Aggregate demand at different levels (store, department, product) Good features often beat fancy models. Step 2: Model Selection and Evaluation You can go two routes - (a) Classical models: ARIMA, SARIMAX, ETS (b)ML/DL models: LightGBM, XGBoost, Prophet, LSTM, Temporal Fusion Transformer. Pick the model based on your data scale and interpretability needs. Then define the right metrics - RMSE or MAE for general accuracy, WRMSSE (Weighted Root Mean Squared Scaled Error) - used in M5 for multi-level forecasting. Choosing the right metric shows you understand the business impact of forecasting. Step 3: Hyperparameter Tuning and Optimization Use GridSearchCV or Optuna for automated hyperparameter tuning. Test how different lags, window sizes, or regularization terms affect performance Step 4: Error Analysis and Model Improvement Don’t stop at scores - investigate why your model fails. Which products or stores are consistently over/under-forecasted? Are errors higher during holidays or promotions? Does the model react poorly to new product launches? Use that insight to - Add better exogenous features (promotions, prices, events), Segment models by category or region, Ensemble multiple models for stability Final Step: Tell the Story Your notebook should read like a case study, not just code. Explain - the business context (“Why forecasting matters”), the modeling journey (“What you tried, what worked, what didn’t”), the insights (“What patterns the model uncovered”) That’s what turns a project into a portfolio highlight that recruiters remember. The M5 dataset is challenging - but that’s the point. If you can handle it, you demonstrate real-world readiness in forecasting, feature engineering, and model optimization - skills every company values. --- 🚶➡️ To land your next Data Science role, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence!

𝗬𝗼𝘂𝗿 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗶𝘀 𝗱𝗲𝘀𝘁𝗿𝗼𝘆𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲. For some FMCG brands, no price cuts, no problem. The brands growing 3-5X faster than competitors have stopped competing on price entirely. This is the framework of how top CPGs win online. The data is clear; 1️⃣ Digital-first brands like L'Oréal, Nestlé and Procter & Gamble are achieving 3–5X higher unit growth 2️⃣ Their edge: Value communication, optimized digital shelf, and content that converts 3️⃣ They’re using pack strategy and personalization, not blanket discounts, to drive volume ++ 𝟰 𝗧𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗺𝗼𝘃𝗲𝘀 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝗖𝗣𝗚 𝗖𝗠𝗢𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆 𝗶𝗻 𝗛𝟮 ++ 1. I strongly recommend, stop leading with "20% off" and start with "Here's why this matters to your life." This way you can master value communication over price communication. - Create content that educates, inspires, and justifies your price point - Use storytelling that connects product benefits to real consumer moments - Build trust through transparent ingredient stories and sustainability narratives 2. Your Amazon listing is your new Times Square storefront. Is your digital shelf better then your flagship store by the way? - Invest in premium product imagery and A+ content - Use data-driven SEO to dominate category searches - Leverage customer reviews as social proof, not just feedback 3. Create value through innovation, not desperation. And it happens faster when you deploy strategic assortment & smart pack architecture. - Develop premium formats and limited editions that command higher prices - Use pack sizes strategically to hit different price points without discounting - Test subscription models and bundles that increase customer lifetime value 4. Use technology to deliver the right message to the right consumer. Is there anybody left not leveraging AI for personalization at scale? I didn't think so. :) - Implement dynamic pricing based on demand signals, not competitor panic - Create personalized product recommendations across all digital touchpoints - Use predictive analytics to anticipate consumer needs before they discount-shop 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Brands that compete on value creation, not price destruction, are the ones dominating market share growth. If you’re still defaulting to promotions, this is your wake-up call. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟲𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁 : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿👇 About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. #CPG #FMCG #ecommerce #AI #retailmedia PepsiCo Mondelēz International Mars The HEINEKEN Company Colgate-Palmolive Reckitt Henkel Kenvue Unilever adidas Nike The Coca-Cola Company

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

Because Demand Planning is NOT a copy-paste of Sales numbers... This infographics compares Sales Planning vs Demand Planning: ✅ Focus 👉 Sales Planning: maximize market share, return on investments & profitability 👉 Demand Planning: predict future demand accurately to drive inventory planning ✅ Approach 👉 Sales Planning: top-down based on brands/categories 👉 Demand Planning: bottom-up; at SKU level ✅ Lowest Granularity Level 👉 Sales Planning: customer; sometimes brand 👉 Demand Planning: SKU, item number ✅ Inputs 👉 Sales Planning: historical sales data, salesforce insights, market intelligence, competitive positioning, marketing campaigns, customer behavior 👉 Demand Planning: historical demand data, statistical forecasting models, inputs from marketing and sales, adjustments for lead times, product lifecycles ✅ Unit of Measure 👉 Sales Planning: 1. $ amount and then 2. quantity 👉 Demand Planning: 1. quantity and then 2. $ amount ✅ Main Deliverables 👉 Sales Planning: sales targets, promotional plans, product launches 👉 Demand Planning: demand forecasts; key input for supply planning ✅ Planning Used For 👉 Sales Planning: revenue forecast, AOP (annual operating plan), budget 👉 Demand Planning: supply plan in S&OP (Sales and Operations Planning) ✅ Metrics 👉 Sales Planning: market share, revenue growth, profit margin 👉 Demand Planning: FVA (forecast value added), forecast accuracy ✅ Consensus (unified forecast and operational plan) 👉 Sales Planning: to ensure to meet sales targets 👉 Demand Planning: to meet demand requirements (inventory, lead times) Any others to add?

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Building a Data Analytics Team for a Mid-Sized Fashion & Beauty E-Commerce Brand! Continuing from my previous post on building a data analytics team, I received many DMs asking for real-world examples. So, in this post, I’ll try to wear the hat of a mid-sized fashion & beauty e-commerce brand and build their data team from scratch. -> Challenge? Scaling an analytics team that drives growth, retention, and profitability while solving key business problems First, What Problems Do We Need to Solve? Before hiring, let’s define the top challenges a data team should tackle: 1) Marketing Attribution & ROI – Are our paid ads actually bringing new customers? 2) Customer Segmentation & Retention – Who are our high-value customers? How do we keep them engaged? 3) Demand Forecasting & Inventory Planning – What should we stock, and when, to minimize dead inventory? 4) Personalization & Conversion Optimization – Can we recommend the right products at the right time? 5) Fraud Detection & Order Cancellations – Are we losing money due to fake COD orders or excessive returns? #Year 1: How to Build the Right Data Team & Solve These Problems? A) Phase 1 (0-3 Months) – Laying the Foundation ->Key Hires: 🔹 1 Data Analyst – To track key KPIs, build dashboards, and analyze marketing performance 🔹 1 Data Engineer – To set up ETL pipelines and connect multiple data sources 🔹 1 BI Developer – To automate reporting and create self-serve dashboards -> Quick Wins: ✔️ Centralize data in a data warehouse (Snowflake, BigQuery, or Redshift) ✔️ Automate daily sales & marketing reports for better decision-making ✔️ Implement UTM tracking for paid ads & influencer campaigns B) Phase 2 (3-6 Months) – Scaling Insights & Retention Strategies ->Next Hires: 🔹 1 Data Scientist – To build customer segmentation models & predict churn 🔹 1 CRM Analyst – To optimize retention campaigns, loyalty programs & lifecycle marketing -> Key Initiatives: ✔️ Identify high-value customers vs. those likely to churn ✔️ Optimize ad spend & ROAS – Cut waste, double down on high-performing channels ✔️ A/B test pricing & discounts – Find the sweet spot for conversions C) Phase 3 (6-12 Months) – AI-Driven Decisions & Advanced Analytics -> Final Hires: 🔹 1 Demand Forecasting Analyst – To predict inventory needs & optimize supply chain 🔹 1 AI/ML Engineer – To implement recommendation engines & dynamic pricing -> Big Impact Areas: ✔️ Build AI-powered product recommendations to increase AOV (Average Order Value) ✔️ Implement predictive demand forecasting to reduce stockouts & excess inventory ✔️ Set up fraud detection models to minimize return abuse & fake COD orders What challenges have you faced in scaling data teams for e-commerce? Let’s discuss! #Ecommerce #DataAnalytics #AI #CustomerRetention #FashionTech #MarketingOptimization

𝗬𝗼𝘂 𝘁𝘂𝗻𝗲 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗹𝘆 – 𝗯𝘂𝘁 𝗶𝘁 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗲𝘀 𝘄𝗵𝗲𝗻 𝗕𝗹𝗮𝗰𝗸 𝗙𝗿𝗶𝗱𝗮𝘆 𝗵𝗶𝘁𝘀.🧙♂️ “Demand forecasting” sounds like one problem. But it’s at least two – and they need different solutions. For example: 1. Daily demand forecasting for the complete product range. Thousands of items, every day, across all locations. We often use algorithms like gradient boosting, deep learning – and yes, even “standard” regressions. The challenge: include everything – price, seasonality, trends, stock levels – and keep it stable without overfitting. The risk? These models tend to learn the average. Peaks often get smoothed out or missed entirely. 2. Then there’s peak event forecasting for holidays, promos, or major events. Totally different game. We need models built to target the spikes – that recognize events and adjust dynamically. They might not be the best at modeling the average though! But they’re better at capturing outliers and extremes. Sometimes lightweight time series models do better here. Or quantile regressions combined with external signals. The goal: anticipate sales behavior when it breaks the usual patterns. My word of caution? Assuming the same model can handle both. This is a great reminder to check early what your business actually needs forecasting for. #ALDITechfluencer #DataScience #DemandForecasting

What happens when you align product performance with sessions, conversion rate, advertising spend, stock on hand and sell-through date? You stop guessing and start making commercial decisions with real clarity. The best merchandise planners and marketers already know this: no metric in isolation tells the full story. The strongest teams are combining traditional planning metrics with ecommerce performance data to understand not just what is happening, but why. For DTC brands, bringing these data points together turns a messy performance picture into a simple set of actions: 🔍 1. Decide what to advertise more When a product has strong conversion, healthy margins and enough stock to support demand, but low sessions, it’s usually a sign that it needs more visibility. This is the sweet spot for scaling paid spend: the product already proves it can sell — it just needs more traffic. 💸 2. Identify what to mark down If you’re holding too much stock and the sell-through date is creeping up, yet conversion is weak even with steady sessions, discounting becomes a strategic lever. Markdowns help clear inventory without wasting ad spend on products the customer clearly isn’t choosing at full price. ✋ 3. Know when to pull back advertising High ad spend + plenty of sessions but poor conversion = a red flag. This is where you pause or reduce spend, diagnose the issue (price, positioning, creative, customer reviews), and redirect budget to products with stronger unit economics. Sometimes the best ROI comes from simply stopping the leak. When metrics live in silos, teams argue. When metrics connect, teams act. This is how modern DTC brands protect margin, improve cash flow and scale the right products at the right time.

🌐 Behind Every Click is a Story I Let the Data Tell It. 📊✨ In a world where e-commerce brands pour thousands into campaigns and still struggle with cart abandonment, product returns, and low retention, the real question isn’t “What happened?” , it’s “Why did it happen?” and “How do we fix it?” 🔎 That’s where data comes in. 📈 And this is where Power BI becomes more than just a dashboard, it becomes a lens for clarity. Over the past few weeks, I built a full-scale, interactive e-commerce performance dashboard, touching every point from marketing campaigns to customer satisfaction. The goal? Make sense of the chaos. Turn complexity into simplicity. Drive action. 🧠 Here’s What I Discovered: ✅ Marketing Channels Instagram drove the most engagement, but Email had the best ROI. Billboard Ads, though expensive, performed poorly — proof that visibility ≠ value. ✅ Cart Abandonment Patterns Over 15% of carts were abandoned. The biggest culprit? Cash on Delivery (COD) users. Fashion orders also had the highest failure and return rates — a clear sign to revisit fulfillment strategies. ✅ Customer Insights That Matter Females aged 35–44 were power buyers across categories Credit Card and PayPal users had smoother journeys. ✅ Returns & Dissatisfaction Top reasons for returns: 📦 “Item Not As Described” 💔 “Arrived Damaged” These aren’t just logistics issues — they’re missed chances to improve product listings and supply chain quality. 🚀 What This Dashboard Achieved: Instead of just dropping charts, I focused on building a narrative: 📌 A story of behavioral trends 📌 A story of missed revenue opportunities 📌 A story that guides business decisions with confidence Power BI didn’t just help me visualize — it helped me strategize. 💡 Final Takeaway Your data is always talking. But without the right tools and the right mindset, it just looks like noise. 📣 This project reminded me why I love data analysis — not just for the numbers, but for the stories they unlock and the decisions they inspire. Let’s connect if you’re building something cool in the analytics space — I’m always open to swapping insights and perspectives. Thanks to Jude Raji for your Help #Datafam #PowerBI #EcommerceAnalytics #MarketingROI #CustomerExperience #DataStorytelling #BusinessIntelligence #DashboardDesign #DataDrivenDecisions #DataStrategy #DataVIZ