Analyzing $5 Trillion of Daily Demand Reveals the Fastest Path to 5-15% Revenue Growth Through Better Forecasting
Analyzing $5 Trillion of Daily Demand Reveals the Fastest Path to 5-15% Revenue Growth Through Better Forecasting
Forecasting errors are inevitable, but they can be minimized with the right tools. External API features, like those from PredictHQ, are specifically designed to enhance accuracy and transform demand forecasting into a strategic revenue driver.
As a business, you know you need to be where your customers are. Demand forecasting involves the use of both traditional statistical methods and modern approaches, such as machine learning and AI-driven techniques, to analyze data from diverse sources—including market research, historical trends, and third-party datasets—to predict future demand for products or services. By leveraging machine learning models, strategically minded businesses can use demand forecasts to develop actionable insights into their customers; optimising pricing, inventory, marketing and more.
However while demand forecasting is now a cornerstone of efficient business operations, it remains one of the most challenging aspects for companies to perfect. Inaccurate demand forecasts can leave businesses unprepared—leading to overstocked inventory, staffing inefficiencies, and, ultimately, financial losses. For businesses in the United States, a 5-15% uplift in revenue opportunity exists from improvements in demand forecasting. That’s $47 - $143B (for QSR’s, Hotels, & Parking alone) on the table for those able to solve the puzzle.
The error gap in demand forecasting
One of the biggest challenges in demand forecasting is accounting for anomalies—demand that deviates from standard demand patterns. Traditional statistical models tend to focus on historical sales data without considering external, real-world factors such as weather changes, local concerts or sporting events. For example in 2024, one concert in College Station, Texas, generated over $7.8m of revenue for local businesses, in only one night. This introduction of an external demand anomaly, when not accounted for in a forecast, creates what’s known as the forecasting error gap, and failure to prepare for such an opportunity can cost a business hundreds of thousands of dollars in missed revenue from poor resource planning.
Across a year, this gap can be significant: our research from $5.3T of daily demand data shows that up to 10-24%, of annual customer demand is ‘non-standard demand’. To measure this gap, businesses often rely on the Mean Absolute Percentage Error (MAPE) - a metric where the goal is simple: the lower the MAPE, the better your forecasting accuracy. The average MAPE across US businesses is 15-49% depending on industry.
Why is the error rate higher than non-standard demand?
"Non-standard demand, such as spikes or dips, not only causes forecast errors on the specific days in question but also propagates inaccuracies into future forecasts as these anomalies become part of the training data, compounding the overall error rate” says Dr Xuxu Wang, Chief Data Officer at PredictHQ.
However, reducing the MAPE is easier said than done, especially when datasets lack the necessary breadth, depth, or timeliness to capture these anomalies effectively. The result? Forecasts that are far less accurate than they need to be, leaving businesses unprepared and money on the table.
Reducing the error margin aka building a better forecast
While some models may smooth over the anomalies, a better approach is to incorporate these anomalies as additional data to reinforce prediction models. By correlating spikes or dips in retrospective demand data with real-world events, you can recontextualize past anomalies and use this data to improve confidence in your next forecast. For a business, this can translate into improved resource planning, stock management, or staff scheduling for example.
The key to reducing the error margin in demand forecasting lies in the quality and breadth of this event data. While no forecasting model can account for every single anomaly, companies that use more complete, diverse, and real-time datasets can significantly improve their forecasting accuracy.
PredictHQ—a demand intelligence company—is leading the way in providing businesses with the data they need to reduce forecasting errors. By incorporating PredictHQ’s external intelligence into their models, a business can lower their error margin within 1 month; a game-changing improvement for any business relying on accurate demand forecasts.
At PredictHQ we have three recommendations for leveraging event data to reduce your error margin in your demand forecasting:
Completeness: The more data points your model has access to, the more accurate its predictions will be. This includes integrating data from external factors like weather, events, and local trends.
Up-to-date information: Demand forecasting is highly time-sensitive. Using real-time data ensures your forecasts account for the latest developments that could impact customer behavior.
Diverse sources: Events in your area don’t occur in isolation - complex combinations of events of all types will impact your customer behavior in different ways, and subsequently your business. Pulling in data of different types from multiple sources can help you capture a broader range of demand-impacting factors.
Why choose PredictHQ?
For businesses in the United States, just a small increase in forecasting accuracy can result in tens of millions of dollars in additional revenue from enhanced business strategies. One retail customer using PredictHQ saw a ROI of $20M by optimizing their front of store merchandising in association with local activities around the relevant store locations, boosting sales during those events. PredictHQ is designed to work seamlessly within your team to boost your forecasting accuracy with a comprehensive API for on-demand access to our event datasets 24/7. This easy-to-use API provides access to more demand-impacting data than any other provider in the world. From local concerts to major sports events, PredictHQ captures it all.
For example, a leading fast-food casual restaurant has leveraged PredictHQ’s Sports and Live TV Event Data to transform their predictive demand intelligence. By integrating over 200 features from PredictHQ’s Features API, the restaurant built an advanced forecasting model to guide kitchen staff on how many burgers, pizzas, wings, and fries to prepare—well before orders come in, minimising waste and wait time for customers.
Integrating PredictHQ’s industry-recognized demand intelligence data into your own existing forecasting models is straightforward and highly effective. Here are a few key ways to transform your forecasting with PredictHQ:
Retroactive demand analysis: By analyzing past demand data with PredictHQ’s enriched datasets, you can identify previously unseen patterns and adjust your future forecasts accordingly.
Real-time adjustments: Use PredictHQ APIs to make live updates to your forecasts based on the latest data. This is particularly valuable for industries like retail and hospitality, where demand can change rapidly.
Scenario planning: PredictHQ’s data allows you to run more robust “what-if” scenarios, helping your business prepare for a wide range of potential outcomes.
Manual review: Not all events impact demand equally. For added control over your forecasts PredictHQ also allows manual review, ensuring that your data can be curated for relevance, and enabling more targeted and effective forecasting adjustments.
Demand forecasting will never be perfect, but tools like PredictHQ can help you get as close to accuracy as possible, turning the data from a challenge into a strategic advantage. By reducing the error gap in your forecasts, you’ll not only save time and money but also gain a competitive edge in your industry. See what this impact could look like for you with our calculator here.
You can try PredictHQ for free today, or speak to a member of our team to learn more about the impact PredictHQ can make in your business.
