Market Forecasting Solutions

Demand forecasting errors silently bleed profits and cash. This document shows 7 red flags in demand forecasting and how to fix them: 1️⃣ Over-reliance on historical data ↳ How to fix: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 2️⃣ Ignoring promotions and discounts ↳ How to fix: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 3️⃣ Forgetting cannibalization effects ↳ How to fix: model cannibalization effects to adjust forecasts for existing products 4️⃣ One-size-fits-all forecasting method ↳ How to fix: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 5️⃣ Not monitoring forecast accuracy ↳ How to Fix: track metrics like MAPE, WMAPE, bias, to improve over time 6️⃣ High forecast error with no accountability ↳ How to fix: tie accountability to S&OP (sales and operations) meetings 7️⃣ Past sales (instead of demand) consideration ↳ How to fix: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations By Marcia D Williams

Your financial forecast is lying to you. (Save this + Repost for others if it's useful ♻️) It's not your fault. It's your method. After leading FP&A teams for over a decade, I see the same mistake kill budgets again and again: Relying on a single source of truth. The secret isn't finding one 𝘱𝘦𝘳𝘧𝘦𝘤𝘵 technique. It's combining the right ones. Here's my go-to "accuracy booster" combo: 1. 𝗗𝗿𝗶𝘃𝗲𝗿-𝗕𝗮𝘀𝗲𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴    You estimate the impact of major planned business changes.        ✅ 𝗧𝗵𝗲 𝗚𝗼𝗼𝗱:    It accounts for real-world strategy (new products, market expansion, etc).        ❌ 𝗧𝗵𝗲 𝗕𝗮𝗱:    It can be heavily influenced by human bias. (Hello, happy ears).     2. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴    You use historical data and algorithms to project trends.        ✅ 𝗧𝗵𝗲 𝗚𝗼𝗼𝗱:    It's pure data. Completely immune to internal politics or bias.        ❌ 𝗧𝗵𝗲 𝗕𝗮𝗱:    It can overreact to recent blips in data and miss the bigger picture.     See the problem? Each one has a blind spot. My solution is brutally simple: Run both methods in parallel. Then take the average of the two. This simple act balances human insight with unbiased data. The result? A forecast you can actually trust. It's how we consistently beat targets. What's the biggest forecasting challenge you face? Let's talk about it in the comments. 👇 -Christian Wattig P.S. This isn't just theory. I've implemented this exact blended approach at several high-growth companies. It just works.

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.

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

Most companies are flying blind when it comes to revenue 📊 "Some months we're closing deals left and right, other months it's crickets. I never know what's coming next month." Every month I meet with business owners who tell me exactly this. Revenue unpredictability kills everything. You can't plan hiring, you can't forecast growth, and you definitely can't sleep well at night wondering where next month's revenue is coming from. Well here's the thing...it doesn't have to be this way. ➡️ THE SOLUTION: PIPELINE DRIVEN FORECASTING Stop guessing at your revenue and start building forecasts based on actual pipeline data. Think about that difference. Instead of hoping deals close, you're working with real data from real prospects. STEP 1️⃣ → STRUCTURE YOUR CRM Track each deal by stage, amount, and expected close date in your CRM system. See every deal needs to move through defined stages that actually reflect how your sales process works. You can't just throw deals in there and hope for the best. STEP 2️⃣ → EXPORT PIPELINE DATA Export your CRM data to Excel for revenue forecasting and analysis. You know what's amazing about this? You get complete control over how you manipulate and model your data. Plus Excel gives you that flexibility that most CRM reporting just can't match. STEP 3️⃣ → FORECAST REVENUE Use weighted pipeline data to predict future revenue with confidence. Apply probability percentages to each stage and calculate realistic monthly projections. That's pretty powerful when you think about it. ➡️ RECOMMENDED CRM TOOLS 🔵 Salesforce → Enterprise grade pipeline management for larger companies 🔴 HubSpot → All in one sales & marketing platform ⚫ Pipedrive → Simple, visual pipeline management for smaller teams Now you may be thinking which one should I choose? Well that depends on your company size and complexity, but any of these will work better than spreadsheets alone. ➡️ BEST PRACTICES FOR PIPELINE MANAGEMENT 📅 Keep data updated weekly 📊 Track conversion rates by stage 📋 Define clear stage criteria 📝 Review forecasts monthly ⚙️ Set up CRM automations 🗓️ Set realistic close dates The key is to export pipeline data monthly to maintain accurate revenue forecasts. This monthly ritual will completely change how you plan and operate your business. === I've seen this transform companies from reactive revenue planning to predictable growth patterns. Instead of crossing your fingers each month, you'll know exactly what's coming and can make strategic decisions accordingly. What's your experience been with pipeline management? Are you still flying blind or do you have a system that works?

𝗦𝘁𝗲𝗽 𝗻𝘂𝗺𝗯𝗲𝗿 𝟭 in any good projection: calculate future Revenue. As accurate as possible. That's mandatory!! 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 ✔️Historical Trend Analysis - Leveraging past performance to predict future trends. ✔️Market Analysis - Understanding market segments and potential impacts on revenue. ✔️Customer Segmentation - Analyzing different customer groups to tailor marketing and sales strategies. ✔️Sales Funnel Analysis - Monitoring progression through the sales funnel to anticipate revenue generation. ✔️Product Lifecycle Analysis - Assessing the stages of a product's life to forecast sales and revenue. ✔️Econometric Models - Using statistical methods to forecast revenue based on economic and market variables. 𝗢𝘁𝗵𝗲𝗿 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 ➡️ Driver-Based Forecasting: Focusing on key business drivers like unit sales, market share, or operational efficiency, this method provides a granular view of forecasted revenue, allowing for more targeted strategy adjustments. ➡️ Rolling Forecasts: Instead of static annual forecasts, rolling forecasts update throughout the year to reflect real-time market conditions and business outcomes, providing a more dynamic financial outlook. Curious to know how you all manage forecasting? What methods do you find most useful?

How Accurate is Your Sales Forecasting? Traditional Sales Forecasting = Guesswork For decades, organizations have relied on intuition, spreadsheets, and outdated manually inputted CRM reports to forecast sales. This manual approach is riddled with bias, delays, and blind spots... WHY? - Sales reps overinflate pipelines to hit quotas. - Managers base projections on anecdotal evidence or past trends - By the time leadership sees the data, it’s often too late to pivot. The result? Missed targets, wasted resources, and strategic stumbles. AI-Driven Sales Forecasting = Accurate, Real-Time, Real Smart, Real Profitable RESULTS... AI-powered forecasting changes everything. No more manual guesswork or worse... It ingests vast streams of real-time data...deal velocity, rep behavior, buying signals, market shifts and detects patterns invisible otherwise. It predicts with far greater accuracy what will close, when, and why. Key Advantages: - Granular Insights: Understands buyer intent, risk signals, and deal health at the individual opportunity level. - Dynamic Forecasts: Adjusts in real-time as new data comes in...no more waiting for end-of-month cleanups. - BS-free Predictions: Removes emotional subjectivity and provides facts. - Actionable Recommendations, some of which can be automated. McKinsey reports that companies using AI in sales forecasting improve forecasting accuracy by 20–50%, leading to significantly higher close rates and shorter sales cycles. Traditional sales forecasting belongs in a museum. #forecasting #ceo #sales #salesquota #AI

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

Everyone is asking the same question right now: What’s next? Real-time forecasting has become essential. When market volatility spikes, brands face pressure from every angle. This includes price increases, inventory delays, and retailer challenges. In these moments, clarity is key. That’s why we released March CPI numbers early, providing the insights needed to navigate instability. Forecasts aren’t just about looking back. They’re about anticipating what’s coming. Brands use forecasts to: • Adjust pricing before margins are impacted • Provide clear, actionable data to retailers • Reforecast monthly as market conditions evolve • Stay ahead of category-specific inflation This approach helps brands make informed decisions and stay proactive, rather than reacting when it’s too late. When the focus shifts from “What happened?” to “What will happen?”, having reliable forecasts makes all the difference. If you're looking to stay ahead of changing market conditions, it starts with having the right data and insights in real time.