Retail Technology Tools

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

Every coffee served now comes with a data point. This coffee shop isn’t just brewing lattes. It’s tracking customer dwell time, staff efficiency, and movement patterns using AI video analytics. The NeuroSpot Barista Staff Control and Monitoring Module may sound like something from a sci-fi startup deck, but it’s real, and it’s changing how we measure frontline productivity. Here’s the kicker:   ↳ It’s not about replacing humans.   ↳ It’s about rethinking how we observe, learn, and improve the in-store experience in real-time. From a CMO’s perspective, this is where physical meets digital. ↳ Imagine personalizing offers based on how long a customer stayed.   ↳ Or improving staff allocation by understanding peak idle windows.   ↳ Or tracking which menu board design kept customers lingering longer. This is what the next era of retail looks like. AI acts as a silent observer, while marketing functions as a live operator.  Video credit: @cheatdaydesign  #AIinRetail #SmartMarketing #CMOThoughts #StoreAnalytics #DTC #RetailInnovation #CustomerExperience #MarketingOps #AIForGrowth

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 

Convenience retail: where every penny counts Convenience stores operate on some of the tightest margins in retail. Rising energy costs, wage increases, and theft make cost management a daily battle. Yet, across the UK, independent retailers are showing how smart technology, process optimisation, and discipline can unlock significant savings. Several approaches stand out: • Staff productivity: Automating stock checks and order forecasting with advanced EPoS systems can save up to 12 staff hours per week – hours that can be redirected to customer service and sales. • Promotion cycles: Moving away from rigid four-week cycles towards staggered promotions avoids costly staff surges. One Stop Stores Ltd achieved ~£600 weekly savings with this approach. • Apps for operations: Low-cost tools like Connecteam simplify compliance, shift management, and reporting – reducing admin costs and preventing the need for extra hires. • Security discipline & smart locking: With UK shoplifting at a 20-year high, retailers like Costcutter ’s Peter Patel limit evening facings of high-value products. But there’s another evolution: grab-and-go cabinets that act as a “high value shop in the shop”, released only after credit card tap (or app) and potentially age verification. —> A leading example is Reckon.ai, a Portuguese startup whose AI and computer vision modules transform existing cabinets, fridges, shelves into autonomous smart units. —> Customers unlock the cabinet (via payment or authorized app), pick what they need, and simply close the door — all tracked in real time, with inventory updates and automatic checkout. —> This combines the convenience of self-service with the protection of a controlled environment. • Energy management: Smart plugs, timers, and recovery systems optimise usage. For heavy users, suppliers like SmartNest Energy, British Gas and EDF offer tailored contracts – but the key is short-term flexibility. • Cash handling automation: Smart safes digitise deposits, reduce errors, and free up staff from manual counting. The UK convenience retail market exceeds £47 billion annually, with over 46,000 stores serving millions. Efficiency at the execution level is not optional — it is a survival imperative. #retail #convenienceretail #fmcg #grocery #storeoperations #epos #retailtechnology #efficiency #staffproductivity #promotionstrategy #retailsolutions #energymanagement #sustainableretail #smartretail #security #cashhandling #lossprevention #retailsavings #omnichannel #automation #retailapps #ukretail #europeanretail #retailsecurity #retailinnovation #smallbusiness #ukbusiness #europebusiness #retailtrends #retaitech #foodtech

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

Retailers face two major challenges – high employee turnover and operational inefficiencies. One solution? Mobile devices for frontline workers. * 34% of US retail employees still don’t have mobile devices for their exclusive use – 7.7 million “unserved hands” * 42% of store operations and 37% of merchandising are still underserved by mobile tech Businesses that equip employees with mobile devices see better employee satisfaction, improved efficiency, and enhanced customer experience. How do employees use mobile devices? ➝ Process sales and accept various payment methods ➝ Check real-time inventory and pricing information ➝ Communicate with team members more efficiently ➝ Assist customers anywhere on the sales floor [Insights from Coresight Research] E-commerce brands are also embracing this shift. #Shopify has launched its mobile POS app in select countries, enabling staff to sell, check inventory, and assist customers on the go – making sales and service smoother than ever. Retail success starts with empowered employees. Are you leveraging mobile tech for your business?

Fraud wasn’t supposed to be a core product challenge. But for most businesses operating online today, it has staunchly become one. In 2024, Indian businesses lost ₹22,842 crore to cybercrime. That’s a 206% increase over the previous year. The first few months of 2025 have already added another ₹7,000 crore in losses. This isn't just a compliance or security concern anymore. It shows up as frozen accounts, locked working capital, rising chargebacks, and misuse through stolen cards, fake UPI payments, and promo abuse. What surprised us most was how quickly chargebacks became part of the everyday reality for merchants: 1. More than half involve deliberate abuse 2. Smaller businesses aren’t spared - around 30 percent of Indian SMEs now report direct losses from fraud, with revenue hits of up to 5 percent. The nature of fraud has changed. Attacks are faster, more coordinated, and more sophisticated. The usual playbook of reacting after the damage doesn't hold up anymore. We decided to rebuild our approach from first principles. RiskShield is what came out of it. It’s a fraud detection engine that runs within the payment flow. It scores every transaction in real time using machine learning, detects fraud rings using graph intelligence, syncs with government risk data like I4C, DoT blacklist, NCRB, and blocks bad actors mid-transaction. It also flags early signs of promo abuse, card testing, and UPI manipulation. So far, RiskShield has helped block over ₹1,700 crore in fraud attempts. It has flagged 2 crore high-risk signals and protected more than 6,600 merchants. The system operates quietly in the background, with an F1 score of 87 percent which is a measure that balances precision (how often fraud alerts are correct) and recall (how much fraud we actually catch) and recall close to 95 percent. Most issues are prevented before anyone files a complaint. There’s still more work to do, but one thing is clear to us now: Fraud cannot be treated as an after-effect. It has to be designed against from the beginning. PS. Here's the flow we have built ⬇️

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