Five Ways to Improve Your Demand Forecasts

Published on March 07, 2024

The cost of inaccurate forecasts

According to The Institute of Business Forecasting, the average one-year forecast for businesses specializing in consumer products can be off by up to 20%. If a company generates $1B+ in revenue, this discrepancy translates to as much as $225M+ in losses annually on costs associated with preventable issues such as overstocks.

Without contextual data feeding your forecasting models, unexpected demand volatility can disrupt supply chain management, workforce planning, and overall operational efficiency — ultimately leading to lost revenue opportunities and increased operational costs.

By incorporating external forecast-grade data into your models, businesses across industries are significantly reducing error rates and driving better business outcomes. Today, we'll explore five ways to leverage predictive analytics to improve forecasting accuracy and take control of demand volatility at scale.

What is demand intelligence?

Improve demand forecasting in 5 simple steps

While you can’t control the external forces that impact sales, you can factor them into your forecasts for a more accurate prediction.

Here’s how to integrate contextual, real-world data to build more resilient and adaptable demand forecasting models that navigate the complexities of the modern market landscape, ultimately leading to better-informed decisions and improved business outcomes. 

1. Hone your data modeling expertise

Before you can start improving forecasts and models, you need to ensure that your own internal data is clean and accurate. This includes keeping large repositories of data on your own product sales and performance, so you have an accurate idea of what’s going on internally for your business. While the priority is to optimize your business operations for the near term, it's also business critical to be aware of long term consumer behavior trends and consumption patterns of your target market to ensure your models are resilient.

Analyze your own data to pinpoint exactly when and where your business experienced previously unexplainable demand shifts that you weren’t able to plan for. Here, you should remove the seasonal, monthly, or weekly trends your business experiences to truly identify your baseline demand. This is a great starting point to help you understand what kind of external factors might have influenced these spikes or dips.

2. Leverage external data sources

The more information you can feed your forecasting models, the smarter they will get. Supplying your models with diverse, relevant, and comprehensive data from various sources, such as point-of-sale data, consumer research, and social listening is essential to maximize their effectiveness.

Event impact analysis, for example, uses hundreds of sources to provide a complete picture of event-driven demand. You can use the data to track 19 categories of events worldwide that impact demand differently depending on factors such as population density, business size, and proximity to event venues.

Forecasting demand with PredictHQ Event Trends data showing event-based spending in Sydney, Australia in May 2024.

Use this type of third-party data to power higher forecasting accuracy, provide context and explainability, and confidently connect the dots on what demand drivers are most impactful for your business.

3. Correlate historical demand fluctuations with external events

While historical sales data won't tell you what's happening right now – the key to unlocking higher forecasting accuracy lies in layering external factors on top of this information, providing missing context and explainability.

By combining your historical sales data with years of historical event data, you can uncover the hidden relationships between your demand and real-world events. This type of demand analysis sheds invaluable light on the types of events that have influenced your demand in the past, so you can factor them into your future forecasts.

This approach empowers you to understand exactly how specific events, like marathons or expos, impact your customers' buying behavior, and where to make adjustments to address these demand shifts. Once you establish correlations between your demand and events such as marathons, expos, and more — you’ll know which event types to look out for in the future.

4. Utilize machine learning techniques

Advanced forecasting techniques powered by machine learning and artificial intelligence excel at handling complex datasets and uncovering hidden relationships and patterns. Invest in infrastructure and talent to support advanced forecasting initiatives, which typically involves acquiring new tools, data storage solutions, and expertise in handling these technologies.

Compared to simpler methods, there are a wide variety of powerful techniques that leading businesses use to better predict trends, identify patterns, and optimize their forecasting models for improved results, such as:

  • Time series analysis: This method analyzes historical data patterns to identify trends and seasonality, allowing for more accurate future projections.

  • Deep learning models: These powerful algorithms can learn complex relationships from vast amounts of data, leading to highly accurate forecasts, especially when dealing with non-linear data sets.

  • Predictive data analytics: This advanced analysis uses different inputs (point-of-sale data, primary consumer research, social listening, online search trends, and more) to predict evolving demand trends on a granular level.

By embracing these cutting-edge techniques and building the necessary infrastructure and expertise, organizations can gain a competitive edge and drive growth in today's data-driven economy.

5. Predict demand surges at scale

One of the best elements of forecast-grade data is realizing how many events have been skewing your demand. Major events, such as a Taylor Swift concert, significantly impact local economies — and so do clusters of smaller events when they overlap. 

When we take a closer look at how events cluster to increase demand, we begin to see a more nuanced picture of their collective impact. For example, 

  • a cluster of concerts caused a 60% increase in parking demand in a single day

  • A combination of overlapping holidays caused an 80% drop in parking demand

Most cities will have highly active venues, with events driving millions in consumer spending for restaurants and other local businesses. Understanding the compounding effects of these events is crucial for businesses to anticipate and plan for the fluctuations in demand that they bring. This is why it's important to understand how events impact each city you operate in, and your locations within.

Make your demand forecasting real-world aware

Data is food for AI. In other words, your algorithms are only as accurate as your data sources, so supplying them with dynamic, contextual data trains your models to better predict future demand patterns.

These are just a few examples of steps you can take to build more accurate, resilient demand forecasting models. To better understand how external factors like events affect your demand, claim your free PredictHQ account today to get started.