How to Set Realistic Demand Forecasting Goals

Instead of deciding on a revenue goal and working backwards from there in a spreadsheet to set pipeline and revenue targets, try this instead: 1. Get finance, sales, marketing and customer success in the same room 2. Have each functional leader share their perspective on what a “bottoms up” forecast would look like based on historical performance, reality and specific changes that we can make to improve historical performance 3. Go ahead and model the “top down” scenario on what would be an ideal revenue target and progression towards that goal over the 12 month timeframe 4. Compare the “bottoms up” and “top down” scenario and get clear on the gap 5. Identify what would have to be true to achieve your ideal top down forecast 6. Honestly assess the feasibility of being able to achieve those things based on historical performance and reality  - wanting and hoping for something is not enough to achieve meaningfully different results  7. Take your “bottoms up” model and identify what the GTM teams can do together to make some improvements, place those bets and adjust the forecast accordingly After this exercise, you’ll probably land closer to your “bottoms up” forecast instead of your ideal “top down” model -  but this is likely much closer to what will really happen vs what you want to happen This isn’t about settling for lower goals, it’s about how do we forecast accurately, take reality into account, identify our best bets, place those bets and try to improve results over time in a realistic and sustainable way Bonus - when all GTM teams and finance are all involved in this process together it creates way more alignment, empathy and clarity on what each team is accountable for and how they need to work together to achieve company targets #marketing #b2b #demandgeneration

Your forecasting team is solving the wrong problem. Here's what I hear in ops meetings every week: "Our forecast was off by 15%. We need better algorithms." "If we just had more historical data, we could predict demand perfectly." "Let's hire a data scientist to improve our forecasting accuracy." That’s the wrong problem. The problem isn't prediction accuracy. Yes, forecasting matters. But chasing 100% accuracy is a trap. No model—no matter how advanced—can perfectly predict demand. There are too many unknowns: trends shift, influencers post, competitors stockout, platforms change overnight. The goal isn’t to guess whether you’ll sell 427 or 445 units next month. The goal is to act when the signals show you something’s changing—and act fast. Here’s what modern inventory teams do instead: ❌  "Our model predicts 15% growth in Q2 based on last year's trends." ✅  "Amazon reviews dropped 30% this week while our Shopify conversion jumped 12% - customers are shifting channels. Moving 500 units to our DTC warehouse today." ❌ "We need 90% forecast accuracy before we can trust our buying decisions." ✅  "Competitor just stocked out on their bestseller. Our search traffic for similar products is up 40%. Expediting our next shipment." ❌  "Let's wait for more data to confirm the trend." ✅  "Three different signals point to the same outcome. Acting now with 70% confidence." This is the real competitive advantage: → Real-time visibility → Signal-driven decisions → Fast reactions that get ahead of the market Chasing perfect forecasts just slows you down. And you still won’t get them perfect. Modern commerce is about signal hunting. Not chasing perfection.

Because wrong service levels and inventory targets kill the supply chain... This infographic shows how to set them up in 7 steps: ✅ 1️⃣ Understand Historical Demand Patterns & Segment the Portfolio 👉 use historical demand data and calculate demand variability. Segment SKUs based on their value and demand variability. ✅ 2️⃣ Define the Required Service Levels 👉 decide the service level targets that the business needs. The higher the service level, more is the inventory needed. ✅ 3️⃣ Determine Lead Times 👉 understand inbound, production and outbound lead times. This will impact how much safety stock the company needs to maintain service levels. ✅ 4️⃣ Apply Seasonal Indexing 👉 Use the formula to calculate safety stock: Z×σd×L ❓ Where: Z is the Z-score corresponding to the service level (e.g., Z=1.65 for 95% service level); σ_d is the standard deviation of demand; L is the lead time in periods. ✅ 5️⃣ Set Reorder Points 👉 calculate Average Lead Time X Average Daily Demand + Safety Stock Calculate reorder points (ROP) to determine when to place an order ✅ 6️⃣ Balance Inventory Targets with Working Capital 👉 use the inventory turnover ratio and days of inventory on hand (DOH) to monitor and set reasonable inventory targets without overstocking. ✅ 7️⃣ Create Feedback Mechanisms & Monitor Performance 👉 track service levels and inventory performance weekly. Identify areas where the targets are not met and safety stock levels, lead times, and demand patterns need adjustments. Any others to add?