Engineering Sales Forecasting Models

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 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

THE BOARD SABOTAGED SALES. A CEO just declared: “We’re gonna hit $20M in revenue this year… because the board decided so.” No bottom-up reality check. No clear conversion math. No forecasting framework. Meanwhile, 69% of sales reps miss their quota in B2B Tech. The harsh truth? If your revenue goal isn’t tied to pipeline, win rates, and deal velocity, it’s not a goal. It’s a shot in the dark. We live in a data-fueled GTM era, and you just can’t cheat anymore. HERE’S 3 WAYS TO TEST TOP-DOWN TARGETS AGAINST BOTTOM-UP REALITY. 1. Full-Year Predictive Sales Forecast A real forecast isn’t just about projecting short-term revenue from your existing pipeline and hoping the rest falls into place. It’s about understanding how your sales engine actually works - tracking pipeline generation, win rates, and sales cycle length - to calculate a realistic full-year projection. And it’s not about averages. Start with each country, product line, and team individually, then sum them up to get a forecast that truly reflects how revenue is generated across the business. 2. Reverse-Engineered Growth Plan Start with your revenue goal, then apply your target growth percentages to last year’s conversion funnel broken down by country, product line, and team. How many new opportunities, proposals, and closed deals does that require? What level of activity needs to happen to support it? The numbers need to match both market reality and operational capacity. 3. Sales Velocity Lever Check Revenue growth comes down to four levers: deal size, win rate, sales cycle length, and pipeline volume. The key is knowing which of these actually drive growth and how they interact. Look at your 12-month trend for each by country, product line, and team. Where are improvements happening? Where are things stalling? Which shifts will have the biggest impact on hitting your goal? If your growth plan relies on improving performance this year, the trends should already be moving in the right direction. TAKEAWAY Win rates have dropped by 20 percentage points over the past years, sales cycles keep getting longer, and deal sizes are shrinking. Hoping for a sudden turnaround without real evidence won’t cut it. You can’t expect your board to be sales target experts, but you can give them the data to keep goals grounded in reality. No more BS targets just to please the board. No more CRO shoulder shrugs when it’s time to hit them. How do you balance ambition with reality in goal setting?

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