Forecasting Ecommerce Demand

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

One of the most practical AI use cases in eCommerce right now isn’t a chatbot or a fancy personalization layer. It’s predicting a shopper’s future LTV before you spend the budget, and routing spend toward the people most likely to buy again. This is what I learned recently from Pecan AI which is quite interesting to me. And because most teams can’t do that today, they keep allocating budget evenly and running broad promos, hoping it works. 𝐏𝐞𝐜𝐚𝐧 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 changes the workflow: • You define the goal (e.g. “Predict 90-day LTV by channel and creative”) • It builds the predictive model for you • Then outputs ranked audiences and campaigns to scale, cap, or test, pushed directly into the tools you already use (ad platforms, CRM, email) No dashboards. Just actionable predictions. 📚 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐭𝐡𝐞𝐲 𝐬𝐡𝐚𝐫𝐞𝐝: A DTC apparel brand had strong AOV but low repeats from a few ad sets. Pecan flagged those cohorts as low predicted LTV, capped spend, and shifted budget to a lookalike built from high-LTV buyers → ROAS went up and discount costs dropped. This is the kind of AI that actually drives growth, not just adds another layer of complexity. Demo link → https://hubs.la/Q03BJHTF0 #AI #ecommerce #predictiveanalytics #martech

Machine Learning Interview Question with Solution for a Walmart Data Scientist Role Question: Walmart collects data from various sources like sales, inventory, and customer behavior. One of the main goals is to predict product demand to optimize inventory levels. Suppose you are provided with historical sales data, including features like Date, Store_ID, Product_ID, Sales_Quantity, and Promotion. How would you build a machine learning model to forecast sales for the next 30 days? Solution: To solve this problem, we can follow these steps:    ⏩ Understand the Problem: The goal is to predict Sales_Quantity for the next 30 days, which makes this a time series forecasting problem.    ⏩ Data Preprocessing:        - Handling Missing Values: If there are any missing values, we need to fill them appropriately (e.g., using median or forward fill for missing sales quantities).        - Feature Engineering: Create additional features such as:             - Lag features (previous sales quantities)             - Rolling averages (7-day, 30-day)             - Holidays (since Walmart sales may spike during holidays)             - Days of the week (sales patterns may differ between weekdays and weekends)    ⏩ Train-Test Split:        Split the data into a train set (e.g., sales before the last month) and a test set (last month of sales).    ⏩ Model Selection: Some of the models we can consider are:         - Random Forest Regressor: Can handle non-linear relationships and provide feature importance.         - XGBoost or LightGBM: These are tree-based gradient boosting models that work well for structured data like sales forecasting.         - ARIMA (AutoRegressive Integrated Moving Average): A classic time-series forecasting model.    ⏩ Training the Model:        - Use historical data to train the model on sales quantity (Sales_Quantity) as the target variable.        - Ensure to include relevant features like promotions, store IDs, and lagged sales as inputs.    ⏩ Evaluation Metrics:        Use Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) to evaluate the model's performance on the test set.    ⏩ Hyperparameter Tuning:        Perform Grid Search or Random Search for hyperparameter tuning to optimize model performance.    ⏩ Deployment:        Once the model is trained and evaluated, deploy it to forecast future sales and adjust inventory levels accordingly. Can you think of any alternate solution? Please share in comments! #dsinterviewpreparation #ml #walmartinterview

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

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

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

Want to use machine learning for better forecasting? Your models must learn whether seasonality exists in your business and successfully predict it. Here's how. First up, we need a working definition of trend: Patterns that appear at regular intervals (e.g., weekly or monthly). Think of seasonality as a factor that modifies the KPI you are trying to forecast: - Retailers make more sales in November and December. - Customer service receives fewer calls on weekends. - Airlines carry more passengers around holidays. - Website visits are higher in the morning. As always, the key to building a powerful machine learning model is knowledge of the business process. For this post, the business knowledge takes on two forms: 1 - Knowing that seasonality is part of the business process. 2 - Understanding the nature of the seasonality. For this post, we'll assume that seasonality exists and that its nature aligns with the calendar year - for example, the classic seasonality of brick-and-mortar retail (i.e., Black Friday). As with any machine learning model, you must provide the algorithm with enough data so that patterns can be learned. I will cover one aspect of this in a later post, when I discuss lagged features. A powerful way to help ML forecasting models learn seasonality is to provide features that explicitly detail seasonal aspects of the business process. This is a bit abstract, so let's explore the scenario of seasonality manifesting within a calendar year. Let's say you're trying to build an ML forecasting model for a monthly KPI (e.g., sales). Since you are aware that the business process exhibits seasonality within each calendar year, providing the month name as a feature often helps the algorithm learn this seasonality. For example, the resulting ML forecasting model can learn: - Sales are highest in November and December. - Sales are lowest in January and February. - Sales bump in August. However, keep this in mind. Months are categorical data, and you need to handle them correctly in your ML forecasting models. While you can use month numbers instead (e.g., January = 1), I prefer to use month names explicitly. Regardless of whether you use month numbers or month names, be sure to encode the data as needed to ensure that the ML algorithm treats it as categorical. For example, when using Python's scikit-learn library, be sure to use a OneHotEncoder on the month data before training your model. BTW - Millions of professionals now have access to the tools to craft powerful ML forecasting models. Python in Excel is included with M365 subscriptions and provides access to libraries such as scikit-learn and statsmodels. Everything you need to go far beyond Microsoft Excel's forecast worksheet.

Switching from supply to demand planning takes guts. This document shows how to transition from a supply planner to a demand planner for those who want to make the move: ➡️ Shift from Internal to External Mindset ↳ Move beyond production constraints; focus on market & consumer trends ↳ Meet customers to understand their pain points; visit the retail stores to see how products perform and are positioned ➡️ Learn the Language of Forecasting ↳ Get fluent in forecast accuracy terms like MAPE, WMAPE, bias ↳ Get also familiar with consensus forecast, baseline vs. uplift, forecast lag ➡️ Master Forecasting Tools & Models ↳ Explore statistical methods in Excel like moving averages and exponential smoothing ↳ Understand when to use each forecasting technique; pros & cons ➡️ Get Close to Sales and Marketing ↳ Ask questions like 'What's driving the change?', 'How confident are you in this number?' ↳ Get comfortable with challenging sales forecast numbers ➡️ Start Owning a Small Forecast Segment ↳ Ask to take on one region, customer, or category’s forecast as a “stretch task”  ↳ Build, improve, and defend it in review meetings. That hands-on work builds fast credibility. ➡️ Embrace Uncertainty as Way of Working ↳ Forecasts are predictions. Learn to do scenario planning. ↳ Communicate and document assumptions clearly ➡️ Bridge with S&OP ↳ Use your supply planning expertise to connect demand, supply, and finance ↳ Embrace the learning curve and enjoy the journey Any others to add?

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