Retail Predictive Modeling

That $129 jacket? It might’ve been $99… if you’d clicked from a different device. Modern retail platforms are recommender-first. On Amazon, 35–40% of sales come via recommendation. On Netflix? Over 80% of viewing is driven by it. There are three major architectures in use: 1. Collaborative filtering = people like you also liked If you and another shopper both bought items A and B, and they also bought item C, you might get recommended item C too. Algorithms like matrix factorization and SVD++ fill in the blanks, predicting what you might like based on purchases but also clicks for example. 2. Content-based models = because you liked this, you might like similar things If you liked a cotton white t-shirt, it might recommend another white t-shirt—because they share similar attributes (called metadata). Algorithms use tools like NLP (natural language processing) to read product descriptions and image embeddings to “see” what the product looks like. Con: can overfit, recommending the same type of thing over and over 3. Deep learning & hybrid architectures These models mix everything: your behavior (clicks, views), product visuals, text, prices, even what’s in stock. They use powerful architectures like Two-Tower Neural Nets (one tower learns about users, the other about products), transformers (used to rank what you’re likely to click) and RNNs (follow your session step-by-step to recommend in real time). Top retailers predict how much inventory to stock using algorithms like… - XGBoost / LightGBM = fast, powerful models that handle messy, tabular data (for things like store sales, dates, and weather) - LSTMs = a type of neural network built to handle sequences These models are trained on inputs like weather, local events, past sales by product and location, social media buzz, and global supply disruptions. This allows the company to order just enough, place inventory smarter (based on demand heatmaps), automate price changes, and route shipments better. Today, pricing can also change in real time, personalized to you. This is called price elasticity mapping = understanding how sensitive you are to price changes. Firms like Amazon, Uber, and airlines use models that track: - Device type - Browsing behavior - Purchase history (do you often wait for sales?) - Local demand AI is also changing what we see when we shop… AI stylists: virtual assistants that recommend clothes based on your body shape, past purchases, search intent… Virtual try-ons using computer vision + GANs (a type of AI that creates fake images) + augmented reality AI now creates product shots that don’t exist → powered by image diffusion models Finally: what’s next in retail AI? Fully AI-curated storefronts. Every homepage will eventually be 1:1 generated per user session. Synthetic SKUs: AI-generated product designs based on detected gaps in the market. 👉 Follow Justine Juillard to spend 30 days learning what AI actually is and where it’s going.

📊 How do you design a retailer-specific promotion optimization study? Promotions are one of the biggest investments brands make — yet too often, they’re designed on averages. The truth? Walmart shoppers don’t behave like Target shoppers. Costco and Amazon each play by their own rules. That’s why retailer-specific research is the only way to truly optimize promotions. Here’s how to approach it 👇 🔹 1. Capture Shopper & Retailer Context Every study starts with segmentation: – Where do you usually buy [category/product]? – How often do you shop this retailer? – What role does price play vs. loyalty or convenience? This lets you tag responses by retailer loyalty group from the start. 🔹 2. Build Retailer-Realistic Scenarios Design promotions in the format shoppers actually see in store or online: – Walmart → 2-for-$12 multipack – Target → Buy 2, Get 3rd 50% off – Costco → $3 instant rebate on club pack – Amazon → 20% off with Subscribe & Save Realistic execution ensures shopper responses reflect real behavior. 🔹 3. Layer in Elasticity & Mechanics Testing – Promotion Elasticity: Would you purchase at 10% off? 20% off? 30% off? – Mechanics Trade-Offs (via conjoint): Discount vs. multi-buy vs. loyalty points. – Stock-Up Multipliers: How many units would you buy? – Switching Dynamics: Would you move from a competitor or private label? 🔹 4. Analyze Retailer by Retailer The outputs are clear, segmented insights: – Best mechanic for Walmart vs. Target vs. Costco vs. Amazon – Optimal discount depth per retailer – Incremental lift vs. cannibalization risk – Shopper switching patterns unique to each channel 🔹 5. Add AI + Machine Learning This is where the study comes alive: – AI simulates virtual shelves in each retailer’s style – ML builds retailer-specific promotion elasticity curves – Predictive models run “what if” scenarios (e.g., What if Walmart runs 20% off while Target runs BOGO?) ✅ The result: A retailer-ready sell-in story for your commercial teams — showing exactly how promotions perform, by retailer, with clear ROI guidance. 💡 Takeaway: Promotions aren’t one-size-fits-all. The winners customize by retailer, by mechanic, and by shopper segment. And with AI, brands of all sizes can now access this level of insight. 👉 Have you ever run a promotion study by retailer? What differences surprised you most? #RGM #Promotions #Retail #Pricing #FMCG #CPG #AI #ShopperInsights

I’ve worked with some of the world’s largest companies to build annual plans. Tell me if this sounds familiar… It is month 1 or 2 of the new fiscal year – and you are already in trouble. You are in danger of missing the annual plan and aren’t entirely sure why. The next Executive Leadership or Board Meeting is already on your worry list. You’re unsure how to change the dynamic because your perspective on the business is limited. You are focused on the current month and quarter. Your plan has devolved into, “If I can make 1Q, I will figure out the rest later.” Here is a better idea. Invest in predictive models that highlight likely gaps-to-goal 12+ months into the future. Ensure the models span awareness-to-close; qualified-to-close isn’t broad enough. Devote at least 15% of leadership meetings to FUTURE quarters and leverage predictive models to drive the discussion. As you sit in month 1 or 2, use predictive models to evaluate the most likely outcomes in months 5, 6, 7, and beyond. Then, you can effectively evaluate the sales and marketing (S&M) investments that address predicted gaps. Make S&M investment decisions based on predictive models even the FP&A loves. YES, the FP&A team. I am talking about using real math! Let the math tell you where to spend–not the loudest voice in the room. The old way of closing gaps was to ask the CRO to do more. To find more. To sell more with existing resources. “Go get us a $1M deal.” The new way is to be methodical and leverage predictive modeling to drive decision-making. Try it. Looking farther into the future and taking action is way more fun than sticking your head into the ground.

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

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

𝗪𝗵𝘆 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗔𝗿𝗲 𝗦𝗼 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 A common challenge in data science is dealing with #heterogeneous data, because different regions, customer segments, or product categories may have vastly different amounts of data. Traditional approaches either 𝗺𝗼𝗱𝗲𝗹 𝗲𝗮𝗰𝗵 𝗴𝗿𝗼𝘂𝗽 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝗹𝘆, leading to noisy estimates when data is scarce, or force a 𝘀𝗶𝗻𝗴𝗹𝗲 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗿𝗼𝘀𝘀 𝗮𝗹𝗹 𝗴𝗿𝗼𝘂𝗽𝘀, ignoring real differences. 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 offer a different solution. They allow parameters to vary at 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗹𝗲𝘃𝗲𝗹𝘀 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀, letting us incorporate not just the data itself but also its underlying structure, #metadata, and the way it was collected. They capture shared #patterns while accounting for group-specific differences. This flexibility makes them ideal for data that’s nested or structured across multiple dimensions. In 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗰𝗶𝗲𝗻𝗰𝗲, Bayesian hierarchical models are widely used because they allow scientists to measure effects at different locations, over time, or at different latitudes, all while capturing broader trends. You can read about such one example here: https://lnkd.in/d6ERwa7q In a business use case, such as 𝗿𝗲𝘁𝗮𝗶𝗹 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴, Bayesian hierarchical models provide: • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗰𝗿𝗼𝘀𝘀 𝗿𝗲𝗴𝗶𝗼𝗻𝘀, 𝘀𝘁𝗼𝗿𝗲𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀, capturing both global trends and local variations. • 𝗦𝗲𝗮𝘀𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, assuming common patterns across regions but also allowing for regional differences. • 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 #sparse 𝗱𝗮𝘁𝗮, borrowing information from related datasets to improve #accuracy. You can read more about this application: https://lnkd.in/dnkcKi4b In both cases, I used #PyMC for Bayesian modeling. By allowing flexibility and borrowing strength from related data, Bayesian hierarchical models offer a robust approach to #forecasting, 𝗲𝘃𝗲𝗻 𝘄𝗶𝘁𝗵 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗼𝗿 𝘂𝗻𝗲𝘃𝗲𝗻 𝗱𝗮𝘁𝗮. Let me know if you've used Bayesian hierarchical models, I'd love to hear about other use cases. #BayesianInference #HierarchicalModels #DataScience #MachineLearning #Forecasting #RetailAnalytics #PyMC #EnvironmentalScience #DemandForecasting #StatisticalModeling #BusinessAnalytics #GeospatialModeling #PredictiveModeling #DataAnalysis 

If you’re feeding raw sales data into your forecasts, you’re making a big mistake. Retail is full of noise: stockouts, listing suspensions, influencer campaigns, promotions — all of it distorts the true demand signal. The worst thing you can do is use that raw data to feed your forecasting algorithms — you'll just forecast a new stockout or end up overstocking again. As my friend Nicolas Vandeput often says (and I agree), the best forecasting models are the ones that can consume all the context data: sales units, revenue, price history, ad spend, influencer activity, inventory levels… With that context, models can actually make sense of the past and project the future. The problem? Most brands don’t have that data organized, accessible, or reliable. So what’s the next best thing? Algorithms that automatically detect and adjust for outliers — cleaning the past before predicting the future. That’s exactly what Flieber does. The moment you connect your data, we run it through anomaly detection and correction before it ever reaches the forecasting engine. And we feed our algorithms the adjusted sales, instead of the actual sales. That step alone improves forecast accuracy by up to 40%. For planners, that’s not just a nice-to-have — it’s life-changing.

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

Ever stocked up on a product that turned into a dust-gathering flop? Or worse, missed out on a sales surge because your shelves were empty? That's the pain of bad demand forecasting, and it's felt across the manufacturing world. Get this: businesses with accurate demand forecasts enjoy a whopping 70%-90% reduction in inventory holding costs AND a 98% service-level rate.  Those numbers aren't magic; they're the result of ditching guesswork and embracing data analytics. Why Demand Forecasting Matters? 👉 Optimized Production: Produce what you'll actually sell. No more overstocking or frustrating shortages. 👉 Smoother Operations: Match your resources to real demand. Plan staffing, material procurement, and production schedules with confidence. 👉 Happy Customers = Happy Bottom Line: Have the right products available at the right time. Boost customer satisfaction and sales. Accurate demand forecasting has a ripple effect: 👉 Reduced Waste: Overproduction leads to wastage at every level. Forecast accurately, and minimize your environmental impact. 💪 Better Pricing Strategy: Understand demand peaks and valleys to make smarter, data-backed pricing choices. 👊 Boost in Competitiveness: Stay ahead of the game by anticipating market trends before your competitors even see them coming. Demand forecasting isn't about staring into a crystal ball. It's about using data analytics to uncover hidden patterns and build smart predictive models: 👁️🗨️ Historical Sales Data: The foundation of any good forecast. 👀 Market Trends: Watch for economic indicators, competitor moves, and changes in consumer preferences. 🙌 External Factors: Seasonality, promotions, even the weather can influence demand. 💥 Advanced Analytics: Machine learning algorithms can spot patterns humans miss, leading to supercharged forecasting accuracy. Here's what to analyze to up your demand forecasting game: 👉 Product-Level Specificity: Don't forecast in broad strokes. Break it down by SKU, location, and timeframe for granular insights. 👉 Time Horizons: Need both short-term (production planning) and long-term (strategic decisions) forecasts. 👉 Forecast Accuracy Tracking: Measure how your predictions stack up against reality, and keep refining those models. Wrangling complex demand data and building those super-smart forecasts can be tough. That's where Google's magic comes in. We can help you make sense of the numbers and get the insights you need to make confident, profit-driving decisions. Ready to conquer your demand forecasting challenges? Let's chat! Follow Omkar Sawant for more information! #demandforecasting #dataanalytics #manufacturing #supplychain #AI