Machine Learning for Ecommerce Forecasting

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.

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

Because a wrong demand forecast ruins everything else... This infographic shows statistical forecast vs machine learning (ML) in demand forecasting: ✅ Approach 🧮 Statistical forecast: relies on historical data patterns, with limited capacity for external variables 🤖 Machine learning (ML): uses advanced algorithms to detect complex patterns, incorporating economic indicators, social trends ✅ Best to Use For 🧮 Statistical forecast: stable demand patterns with minimal external variables 🤖 Machine learning (ML): changing demand with diverse external influences (e.g., promotions, weather) ✅ Accuracy 🧮 Statistical forecast: works well for simple, well-defined time-series patterns (e.g., seasonality, trends) 🤖 Machine learning (ML): more accurate for complex, high-dimensional data; forecast accuracy rates are 10-20% higher ✅ Model Type Examples 🧮 Statistical forecast: exponential smoothing, moving averages 🤖 Machine learning (ML): neural networks, random forests, XGBoost ✅ Adaptability 🧮 Statistical forecast: requires manual intervention for changing trends or patterns 🤖 Machine learning (ML): highly adaptable to changing demand patterns with retraining ✅ Scalability 🧮 Statistical forecast: has limited scalability; small datasets or simple SKU portfolios 🤖 Machine learning (ML): scales easily for large datasets and complex SKU portfolios ✅ Team 🧮 Statistical forecast: Supply Chain Team can build most of these models themselves 🤖 Machine learning (ML): Supply Chain Teams with Data Scientists are required Any others to add?

🚀 𝐈𝐧𝐬𝐢𝐝𝐞 𝐏𝐢𝐜𝐧𝐢𝐜’𝐬 𝐀𝐈 𝐉𝐨𝐮𝐫𝐧𝐞𝐲: 5 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧𝐝 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐋 We started from day one with a digital-first, cloud-native foundation. But as AI matured, we made a conscious choice: to weave machine learning into the core of how we operate — from forecasting and supply chain to customer experience and personalization. Fast-forward to today — we’re not just delivering groceries; we’re reshaping how AI drives e-commerce and e-logistics. 🧠 In a new deep-dive by Jelmer Borst, we unpack what it really takes to scale from early-stage forecasting models to becoming a machine learning powerhouse: 🔍 𝐋𝐞𝐬𝐬𝐨𝐧 1: 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐞𝐱𝐢𝐬𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. For us, it was demand forecasting for perishables in a just-in-time supply chain. No room for error, and a perfect AI proving ground. 💬 𝐋𝐞𝐬𝐬𝐨𝐧 2: 𝐔𝐬𝐞 𝐍𝐋𝐏 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐭𝐨𝐮𝐜𝐡𝐩𝐨𝐢𝐧𝐭𝐬. From smarter service to frictionless search, we let AI make the experience feel invisible — and unforgettable. 🎯 𝐋𝐞𝐬𝐬𝐨𝐧 3: 𝐄𝐯𝐨𝐥𝐯𝐞 𝐟𝐫𝐨𝐦 𝐫𝐮𝐥𝐞𝐬 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Recommendation systems moved from static logic to two-tower neural networks that personalize and surprise in equal measure. 🏗 𝐋𝐞𝐬𝐬𝐨𝐧 4: 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐀𝐈 𝐦𝐨𝐚𝐭. We didn’t sprinkle AI on top — we baked it into how we think, organize, and build. Cross-functional literacy. Shared metrics. Constant experimentation. 📈 𝐋𝐞𝐬𝐬𝐨𝐧 5: 𝐂𝐫𝐞𝐚𝐭𝐞 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬 𝐭𝐡𝐚𝐭 𝐥𝐞𝐚𝐫𝐧 𝐝𝐚𝐢𝐥𝐲. The more we grow, the better our models perform — and the smarter our operations become. A true flywheel of AI-driven improvement. AI isn't a department. It’s the way you work. If you're building, scaling, or transforming with AI — this one’s worth the read. 👇 Dive into the full story: https://lnkd.in/eznQw4wQ #AI #MachineLearning #Leadership #Ecommerce #CustomerExperience #RecommendationSystems #Forecasting #TechTransformation #PicnicTech #DigitalInnovation Picnic Technologies