Retail Sales Forecasting Methods

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 

Human forecasters augmented by GenAI improve performance by 23% and vastly outperform AI-only predictions. Fascinating new research has uncovered important lessons, not just on Humans + AI forecasting, but more generally AI-augmented thinking. 🔮Human forecasters provided an LLM with a 'Superforecaster' prompt substantially improved their prediction performance. 📊In contrast to studies in other domains, the improvement was consistent across more and less skilled forecasters. 🔄Even the use of biased models improves performance to a similar degree, showing that the value was in providing additional perspectives to be assessed by human judgment. 💬Back-and-forth interaction is critical to value creation. Simple Humans + AI thinking processes such as incorporating predictions is of limited use. Forecasters using the models through their thinking process is high value. 🌈Prediction diversity is not degraded by use fo LLMs, with users not letting the models homogenize their thinking. 🚀Forecasting is an excellent use case and example for AI-augmented thinking. High-level human decision-making is highly complex and cannot be delegated to machines, but LLMs, used well, can substantially improve outcomes. The 'Superforecaster' prompt used in the study and a link to the pre-print paper are in the post. #foresight #forecasting #humansplusai #augmentedintelligence

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

A forecast that’s right half the time isn’t a sales execution problem. It’s a system signal — the model producing the number doesn’t know what it’s producing. At $8M–$30M ARR, the instinct is to tighten stage definitions, add scrutiny to late-stage deals, introduce a second opinion on every forecast call. Those moves produce small improvements. They don’t fix the underlying issue. Because 55% accuracy isn’t noise. It’s signal. Three mechanical causes, in order of prevalence: 1. ICP drift. The accounts converting today aren’t the accounts your conversion rates were calibrated against. Your historical close rates are predicting a buyer profile that’s changed underneath you. 2. Stage definitions that describe activity, not commitment. “Proposal sent” tells you what your team did. It doesn’t tell you what the buyer did. Forecasts built on activity stages will always oscillate. 3. Pipeline carrying its own history. 20–40% of most pipelines at $8M–$30M ARR are deals that should have been disqualified two quarters ago. They’re distorting every ratio the forecast depends on. None of these are sales problems. They’re architecture problems. Which is why adding a second sales review doesn’t fix them — and why tightening your CRM workflow makes the symptom worse by hiding the break deeper in the data. What it means for the board conversation: If you’re reporting forecast accuracy under 70%, the defensible board narrative isn’t “we’re working on sales discipline.” It’s “we’ve identified that our forecast model is calibrated against assumptions that need to be re-validated — here’s the work underway.” The first framing sounds like you don’t control the outcome. The second sounds like you understand the system. One question to bring to your next exec team meeting: When was the last time our conversion rates were recalibrated against the accounts we’re actually closing today — not the ones we were closing 18 months ago? If no one has an answer, the forecast isn’t wrong. The model underneath it is. — Forecast Fridays #01

Sales forecasting is a high-impact use case for predictive analytics! Here's what you need to know about it: 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 𝗳𝗼𝗿 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: • 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: Accurate forecasts help the business to make better decisions regarding budgeting, resource allocation, and general planning.    • 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Helps manage inventory more efficiently by predicting future demand, and avoiding stockouts or overstock situations.    • 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Sales forecasts allow companies to anticipate market trends and adapt their strategies in response to upcoming shifts. 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 1. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Collect historical sales data and external variables influencing sales (like economic indicators, market trends, promotional activities, and weather data).     2. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: Clean the data by handling missing values, outliers, and anomalies to ensure the quality and reliability of your model.     3. 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗘𝗗𝗔): Analyze the data to understand patterns, trends, and seasonal behavior. This step is important for choosing the right forecasting model.     4. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Choose a forecasting model based on the business context and the structure of your data. Common choices include time series models (like ARIMA or Prophet), regression models, or more advanced machine learning models depending on data and business complexity.     5. 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Train your model using historical data and validate it by splitting the data into training and test sets, and using techniques like cross-validation to ensure its predictive power.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Deploy the model to start forecasting and continuously monitor its performance over time, making adjustments as necessary based on feedback and new data.     7. 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Communicate the forecasting results to stakeholders through visualizations and reports on accuracy, changes, and recommendations. By being able to build sales forecasts, you contribute directly to the organization's bottom line. This high-impact work can increase your visibility with management, opening paths to more senior roles. Have you been involved in sales forecasting or plan to work in this field? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #predictiveanalytics #salesforecasting #forecast #careergrowth

A sales leader told me "Our forecast is always off by 20-30%. I don't know what's real anymore." I looked at his pipeline. Every deal in "proposal stage" had an 80% close probability. I asked him one question: "Has an executive at the buyer's company authorized solving this problem?" He had no idea. Here's the problem: His CRM stages were measuring seller activity. Not buyer commitment. Discovery meant "we had a discovery call." Not "they acknowledged a costly problem." Demo meant "we showed them the product." Not "multiple stakeholders agreed this needs to be solved." Proposal meant "we sent pricing." Not "an executive authorized budget to fix this." So his forecast was always wrong. Because he was tracking the wrong things. Here's what we did: We rebuilt his qualification framework around buyer stages instead of seller activities. The ADVANCED framework: Acknowledged problem Documented issue Validated by team Authorized by executive Narrowed to external Chosen as vendor Established timeline Deal terms finalized These are buyer commitments. Not seller activities. When we ran his pipeline through this framework, reality hit hard. Most of his "80% deals" were actually at 25%. They had acknowledged a problem but nothing was documented. No executive sponsorship. No validation from multiple stakeholders. 𝗪𝗶𝘁𝗵𝗶𝗻 𝗼𝗻𝗲 𝗾𝘂𝗮𝗿𝘁𝗲𝗿, 𝗵𝗶𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘄𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 65% 𝘁𝗼 93%. Not because his team started working harder. Because they started tracking what actually predicts if deals close. BTW: When you can forecast within 3%, you can predict your income. You can plan for your family. You can budget for that house or wedding or kids' school. When your forecast is always off by 20%, you're guessing. Your compensation is unpredictable. Your future is uncertain. This isn't just about making your boss happy. This is about controlling your financial future. Track buyer commitment. Not seller activity. That's how you build forecast accuracy. — Sales Leaders! Your sales team doesn’t need more training. it needs a revenue operating system: https://lnkd.in/ghh8VCaf

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

We just watched a luxury fashion brand flip their merchandising strategy in real time. And it worked. Here’s how this usually goes: Designers design. Teams predict what will sell. Everyone prays. But this team didn’t guess. They tested. Before production, they ran a Heatseek targeting different segments with different product colors. Real behavior, not opinions. The results? Surprising. The “safe” color flopped. The one no one expected? Top performer. They uncovered clear preferences by age group and location, signals their merch team never would have predicted. “This is super helpful, especially the ranking,” their merch lead said, staring at the data like it broke the rules. Now they order inventory based on behavior, not gut feel. Less waste. Higher sell-through. Products people actually want. From guessing to knowing. Have you ever tested a big assumption? What did your customers want that surprised you?

Forecasting is no longer a spreadsheet exercise. It’s an intelligence engine. If I were building a forecasting system from scratch in 2025, here’s what it would look like. 1️⃣ Phase 1: Ditch the backward-looking model. Traditional forecasts rely too heavily on rep inputs and lagging indicators. Instead: Feed the model real behavior data: emails, calls, meetings, time in stage, intent signals. Let AI surface deal velocity, risk factors, ghosted accounts, and false positives. 2️⃣ Phase 2: Build the autonomous pipeline. AI isn’t just for scoring. It’s also for triggering. Create Auto-alerts for stalled deals and agent-driven nudges: “Reach out now, buying signals just spiked.” Build auto-prioritization of deals based on historical conversion patterns and AI sentiment analysis. 3️⃣ Phase 3: Deploy next-best-action agents. This is where it gets fun. SDRs and AEs don’t log in to CRMs, they work out of an AI inbox. Every morning: “Here are your top 5 accounts. Here’s what to say. Here’s the play.” GTM motion becomes reactive → proactive → predictive. 4️⃣ Phase 4: Make forecasting a team sport. Sales leaders aren’t spending hours cleaning rollups, they’re challenging the model: “Why did we lose that deal?” “What changed in this region’s pipeline this week?” And AI answers with data, not guesses. Ok, this wasn’t meant to be a product pitch, but you can do all of this with ZoomInfo’s AI Copilot. If your forecast still starts with a spreadsheet and ends with hope, it’s time to rethink the system. What’s the most useful AI signal you’ve seen in a pipeline? #RevOps