Demand Forecasting: The Complete Guide - Everything You Need to Know
What is Demand Forecasting?
Demand forecasting is the process of using data and analytics to predict the future customer demand for a product or service – which is typically done using a variety of methods, including market research, consumer surveys, and by ingesting third-party data for statistical analysis.
Why is Demand Forecasting important?
Demand forecasting is important for businesses of all sizes across industries because it powers informed decisions about production and inventory levels, pricing, staffing, and marketing. By predicting future demand, businesses can ensure that they have the right products or services available at the right time – and in the right quantities to meet the fluctuating levels of demand. This is especially crucial during periods of demand hypersensitivity to avoid stock outs and overstocking, which often results in lost sales and increased costs.
Demand forecasting helps businesses improve their performance and better meet the needs of their customers such as by:
Setting prices designed to maximize profits while still meeting the needs of their customers
Focusing targeted marketing efforts more effectively, such as occasion-based marketing
More efficiently planning their production and supply chain for tighter inventory control
Optimizing staff schedules to best serve customers and avoid expenses such as unnecessary overtime pay
How companies across industries leverage demand forecasting to make data-driven decisions
There are several reasons why demand forecasting is an essential element of planning and success for industries across the economy. Let's take a look at a few examples of how companies in the hotel and accommodation, airline companies, CPG and FMCGs, and more use demand forecasting to boost profitability:
Demand forecasting helps hotels, resorts, and other types of accommodation providers plan for future demand and make informed decisions about their operations and their pricing. By analyzing data to predict future demand, hotel operators can make sure they have enough rooms available at the right time – and at the right prices – to best meet guest needs, and make a profit while doing so.
Without integrating demand forecasting into operation plans, it's easy to run into issues such as overbooking and customer complaints, which can lead to lost revenue in the form of refunds, and damage to the brand's reputation. We all know how brutal (and permanent!) online reviews can be when a guest leaves your establishment unhappy.
Dynamic pricing is one-way hotels, online travel agencies, home-sharing apps, and more use demand forecasting to drive higher profits. For example – if a hotel revenue manager is expecting an influx in demand for a range of dates in the future, they can preemptively raise room and package rates to maximize the additional revenue. On the other hand, if demand is expected to decrease, accommodations may need to slash prices to secure more bookings than the local competition.
Consumer packaged goods (CPG) and fast-moving consumer goods (FMCG)
CPGs and FMCGs use demand forecasting to better understand fluctuations in consumer demand for their products and make informed decisions about production and supply chain management. With accurate demand forecasting, these companies can proactively work with retail partners to make sure they're stocked with enough inventory on hand to align with expected levels of consumer demand in different retail locations.
Demand forecasting also helps CPGs and FMCGs make sound decisions regarding pricing, promotions, and other strategic marketing efforts.
Grocery & Supermarkets
Grocers and supermarkets use demand forecasting to better plan their inventory and stock the products customers are on the market for, at the right time. This is crucial to avoid costly issues such as overstocking, which can lead to spoilage and lost profits, as well as out of stocks – which can lead to lost sales and unhappy customers.
Grocery stores and supermarkets leverage demand forecasting to improve their overall operations, increase their profitability, and make sure to consistently provide the products shoppers want, when they want them.
Human Capital Management (HCM)
Demand forecasting is also an important aspect of human capital management, as it powers the ability for a wide range of organizations to anticipate the future needs of their workforce, and plan accordingly. These plans may include actions from hiring and training new employees to managing current employees to ensure that they have the skills and knowledge necessary to meet the organization's future needs.
Quick Service Restaurants (QSR)
Demand forecasting is important for the quick serve restaurant industry because similarly to the other industries we've covered so far, it helps these businesses plan and prepare for spikes and dips in customer demand. Companies in the fast-casual space use demand forecasting to optimize operations and ensure that they have the necessary resources on hand during high periods of demand – such as food, staff, and equipment, in place to meet customer needs.
Take out providers such as pizza and coffee shops, sandwiches, burritos, wings and more operate in a volatile and highly competitive environment, which is particularly sensitive to changes in demand. By accurately forecasting these changes, fast food restaurants avoid over- or under-stocking, which can lead to spoilage, food wastage, and lost profits.
Demand forecasting also helps these businesses stay competitive and attract new customers by making informed decisions about staffing, pricing, seasonal and event-based menu changes, and more.
Demand forecasting unlocks the ability for restaurants to plan their operations more effectively. For example, by forecasting demand, a restaurant can ensure that it has enough ingredients on hand to meet the expected level of customer demand. This can help the restaurant avoid running out of popular menu items, which can lead to customer dissatisfaction and lost sales.
By accurately forecasting demand, a restaurant can also right-size staffing levels to schedule enough employees on shift to serve customers without overstaffing, which helps save on labor expenses by avoiding costly overtime pay. Demand forecasting can help a restaurant run more efficiently and effectively, ultimately leading to increased profitability.
Transportation providers such as taxis, ride hailing and ridesharing services, bike, and scooter rentals, and more use demand forecasting to decide how to best allocate of their resources. By accurately forecasting future demand for their services, these companies can prepare by getting more drivers on the road, or running a hiring promotion to attract new drivers to the company in preparation for high-demand seasons.
Demand forecasting is an essential tool for businesses in the on-demand delivery space as it helps these companies to optimize their operations, ensuring that they have the right number of drivers and vehicles available to meet customer demand.
On-demand delivery apps, as well as the delivery arm of retailers such as convenience stores and fast food restaurants, leverage demand forecasting to:
Improve their pricing and inventory management to boost profits and customer satisfaction.
Identify trends in consumer behavior and make strategic decisions to adapt to changing market conditions.
Demand forecasting is an essential tool for parking operators to predict future demand for parking, and make informed decisions about how to manage their supply of parking spaces. Demand forecasting also helps parking operators improve their customer service by:
Ensure there's always enough parking available for their customers, and even
Provide customers with real-time information about parking space availability
Optimize dynamic pricing strategies, and improve the overall efficiency of their operations.
Accurately predicting customer demand helps retailers make informed decisions about their inventory, pricing, and overall business strategy – and ensure that they have the right products in stock at the right time, and at the right price. This can help them to avoid stock-outs, which can lead to lost sales, and it can also help them to avoid excess inventory, which can tie up capital and result in markdowns and other forms of financial loss.
Technology platforms that service retail, accommodation, and other industries leverage demand forecasting to provide end users with powerful event insights that drive better business outcomes. Demand forecasting helps these platforms to deliver more accurate predictions about how much demand there will be for their customers’ products or services in the future.
Benefits of demand forecasting
Overall, demand forecasting helps businesses make better, more informed decisions about their operations and improve their performance. Additional key benefits of demand forecasting for businesses include:
Improved inventory management: Stock just the right products or services available, at the right time, and in the right quantities to meet the needs of their customers.
Better pricing decisions: Set prices that maximize profits while still meeting the needs of their customers. For example, if demand is expected to increase in the future, businesses can raise their prices to capture some of the additional revenue. On the other hand, if demand is expected to decrease, businesses may need to lower their prices to remain competitive.
More effective marketing: Focus advertising and promotions on the products or services that are expected to be in high demand in the future.
Increased supply chain efficiency: Plan your production and supply chain more effectively to reduce waste. For example, you can optimize production schedules to cut down on idle time and minimize the amount of inventory you need to hold.
Optimized labor management: If a business has a demand forecast indicating an upcoming peak in customer demand, they can use this information to plan ahead and hire additional staff to meet that demand. This can help them to avoid delays or customer dissatisfaction due to understaffing. On the other hand, if a business has a demand forecast that indicates a future downturn in customer demand, they can use this information to plan ahead and reduce their staffing levels accordingly. This can help them to avoid overstaffing and the associated costs and inefficiencies.
Benefits of Demand Forecasting with Event Data
Demand forecasting on its own is a powerful tool as it helps companies to anticipate and meet customer demand more effectively. But to generate accurate predictions, it requires external data, machine learning, and feature prioritization. One specific type of third-party data that can be used for demand forecasting is event data – which refers to information about events that may impact customer demand.
Factoring events into demand forecasting further improves forecast accuracy while painting a complete picture of local demand. It helps companies gain visibility into the external factors impacting demand fluctuations, train your models with relevant, accurate data, reduce MAPE, and improve forecasting accuracy.
Other benefits of using event data for demand forecasting include:
Improved accuracy: As mentioned, by including information about specific events that may impact demand, businesses can create more accurate demand forecasts – and avoid making decisions based on incomplete, incorrect, or outdated information.
Enhanced responsiveness: By regularly monitoring event data, businesses can quickly identify changes in customer demand and adjust their operations accordingly. This can help them to avoid overproduction or stockouts and to better meet the needs of their customers.
Enhanced planning: By using event data to create long-term demand forecasts, businesses can plan ahead and make strategic decisions about their operations. This can help them to avoid potential disruptions and to optimize their resources for maximum efficiency.
Competitive advantage: By using event data to create accurate, timely demand forecasts, businesses can gain a competitive edge over their rivals by better anticipating and meeting customer demand. This can help them to win new business and to maintain a loyal customer base.
Factors that Influence Demand Forecasting
By including information about causal factors in their demand forecasts, businesses can gain a more complete understanding of customer demand and can make more informed decisions about their operations.
For example, if a business is using demand forecasting to predict customer demand for a particular product, they may integrate information about relevant event categories in their forecast such as:
This can help them to anticipate how these events may impact customer demand for their product, and make more informed decisions about production, staffing, and inventory management.
For instance, if a demand forecast indicates that a major sporting event is happening in the near future, the business may anticipate an increase in customer demand for its product and take steps to increase production or stock additional inventory. On the other hand, if the forecast indicates that a severe storm is coming, the business may anticipate a decrease in customer demand and adjust its operations accordingly.
In addition to causal factors like events, demand forecasting can also be influenced by a number of other factors, such as seasonality, competitors, and the overall economic environment. By taking these factors into account, businesses can create more accurate demand forecasts to power data-driven operational decisions.
For example, seasonality is a common factor that can impact customer demand for many products and services. By including information about seasonal trends in their demand forecasts, businesses can anticipate changes in customer demand and plan accordingly. For instance, a business that sells seasonal products, like winter clothing or summer equipment, may anticipate an increase in customer demand during the appropriate season and adjust its operations accordingly.
Competitors can also impact customer demand and, as a result, can influence demand forecasting. By keeping track of their competitors' activities and products, businesses can anticipate how these may impact customer demand for their own products and services. For example, if a competitor introduces a new product that is similar to one that the business offers, the business may anticipate a decrease in customer demand for its product and take steps to adapt its operations accordingly.
Demand Forecasting Examples
Let’s explore examples that show how demand forecasting can be used in different industries and situations to help businesses make better, more informed decisions about their operations:
A clothing retailer uses historical sales data to develop a statistical model that predicts demand for different types of clothing, such as T-shirts, jeans, and dresses, over the next six months. The retailer uses this forecast to plan its production and inventory levels and to set prices for the different clothing items.
A restaurant conducts market research to understand consumer preferences and buying behaviors, and uses this information to develop a forecast for demand for different menu items over the next year. The restaurant uses this forecast to plan its menu offerings, pricing, and marketing efforts.
A technology company consults with industry experts to gather their opinions and predictions about future demand for smartphones, laptops, and other consumer electronics. The company uses this information to develop a forecast for demand over the next few years and uses the forecast to plan its production and supply chain.
Types of Demand Forecasting
Demand forecasting comes in different types including short-term, medium-term, and long-term forecasting. Short-term forecasting is used to predict demand for the next few weeks or months and is typically based on historical sales data and current market trends. Medium-term forecasting is used to predict demand for the next few quarters or years and is often based on market research and consumer surveys. Long-term forecasting is used to predict demand for the next several decades and is typically based on economic trends and industry-specific factors.
Demand Forecasting Methods
There are several different methods that businesses can use for demand forecasting, including:
Statistical analysis: This method involves using mathematical models and algorithms to analyze historical sales data, event data, location data, and other relevant information, such as economic indicators and market trends, to predict future demand. This method is often used for short-term and medium-term forecasting and can be effective for predicting demand for products or services with a stable demand pattern.
Market research: This method involves collecting data from consumers and other market participants, such as competitors and suppliers, through surveys, focus groups, and other research methods. This data can be used to understand consumer preferences and buying behaviors, and to develop forecasts for future demand. This method is often used for medium-term and long-term forecasting and can be effective for predicting demand for products or services with a more complex or unpredictable demand pattern.
Expert opinion: This method involves consulting with experts in a particular field or industry to gather their insights and predictions about future demand. This can include experts within the business, such as sales managers or product managers, as well as external experts, such as industry analysts or consultants. This method is often used for short-term and medium-term forecasting and can be effective for predicting demand for products or services with a high level of uncertainty or complexity.
Overall, the best method for demand forecasting will depend on the specific product or service, the time horizon of the forecast, and the availability of data and other information. Businesses commonly leverage a combination of these methods to develop an accurate and reliable forecast.
How to improve demand forecast accuracy
Demand forecasting has traditionally relied on historical data – but this approach is limited in its ability to anticipate future trends and changes in consumer behavior. Developing accurate forecasting models that incorporate external data is essential for building strategies that can adapt to evolving market conditions and drive profitability.
However, this can be a challenging task, as it requires a deep understanding of the factors that are impacting demand in key locations. To achieve success, it is essential to incorporate external, forecast-grade data into forecasting models, and regularly update these models to reflect changing market conditions.
Leverage machine learning
There are a variety of techniques you can use to leverage machine learning to improve demand forecast accuracy and make better decisions, including:
Use historical data to train a machine learning model. By feeding the model data on past demand for a product, the model can learn to make predictions about future demand.
Incorporate external factors that may impact demand. This could include things like seasonality, economic indicators, and market trends. By taking these factors into account, the model can make more accurate predictions.
Use advanced algorithms to make more accurate predictions. Some machine learning algorithms, such as deep learning and gradient boosting, can make highly accurate predictions when trained on high-quality data.
Regularly evaluate and update the model. As new data becomes available, it's important to retrain the model to ensure that it continues to make accurate predictions. This can be done by splitting the data into training and testing sets and using the testing set to evaluate the model's performance.
Smart event data improves demand forecasting accuracy
Smart event data refers to data that has been processed, enriched, and analyzed using artificial intelligence (AI) or machine learning algorithms, such as the event data provided by PredictHQ. In the context of demand forecasting, this intelligent event data improves accuracy in a few ways:
By using AI algorithms to analyze large amounts of data, it is possible to identify patterns and trends that may not be visible to humans, or by using traditional data sources. This can provide a more accurate view of consumer behavior and demand for products.
Smart event data can also be used to identify external factors that may impact demand, such as market trends, economic indicators, and seasonality. By incorporating these factors into the demand forecasting model, the model can make more accurate predictions.
Intelligent event data can also be used to improve the accuracy of machine learning models. By providing a larger and more diverse dataset for the model to learn from, the model can make more accurate predictions about future demand.
Choosing Your Demand Forecasting Partner & Software
When choosing a demand forecasting partner and software, there are a few key factors to consider to ensure that you get the most value from your investment:
Accuracy and reliability: The demand forecasting software should be able to make accurate predictions about future demand. It's also important to choose a partner with a track record of success in demand forecasting.
Integration with existing systems: The demand forecasting software should be able to easily integrate with your existing systems, such as your inventory management software and sales data. This will make it easier to use the software and get the most value from it.
Scalability: As your business grows, the demand forecasting software should be able to handle larger amounts of data and make more accurate predictions. It's important to choose a partner and software that can scale your business.
Cost: The cost of the demand forecasting software and services should be reasonable and in line with your budget. Be sure to compare prices and features from multiple vendors to find the best value.
Why partner with PredictHQ?
Events drive demand. More than $1.1 trillion per year worth of demand each year, in fact. Discover which event categories impact your demand, so you can turn demand anomalies into a competitive advantage. Combine your historical data with PredictHQ’s seven years of forecast-grade event data, and then use our API in forecasting to reduce costly errors.
PredictHQ uses a combination of machine learning algorithms and curated data sources to power its demand forecasting. The company gathers data from hundreds of sources, including events, weather, public holidays, and school schedules, to create a comprehensive view of demand for a given location and time.
This data is then fed into machine learning models that can accurately predict future demand based on past trends and patterns. Because PredictHQ uses a combination of data sources and advanced algorithms, it provides highly accurate forecasts that help business leaders across industries make data-driven decisions.
PredictHQ provides the external intelligence you need to create better forecasting solutions for your customers. We offer a variety of partner integrations and partner solutions, including:
Access and share smart event data in Snowflake Secure Data Share
Snowflake Secure Data Share enables companies to quickly access and shares PredictHQ intelligent event data in a controlled and efficient way. Being able to share data through Snowflake removes a substantial number of steps required to use PredictHQ data in any meaningful way.
Reduce forecast errors to plan smarter with PredictHQ + BlueYonder
Blue Yonder is the world leader in digital supply chain and omnichannel commerce fulfillment. Power your forecasting with demand intelligence via the Blue Yonder Luminate Demand Edge platform – which includes four of PredictHQ’s demand intelligence categories, and can extend to the full set of 19 categories.
Incorporate intelligent data into antuit.ai to accelerate forecasting
PredictHQ’s demand intelligence has been added into antuit.ai’s predictive models across many industries, including retailers and consumer packaged goods. Antuit.ai’s industry-leading AI Demand Forecasting includes PredictHQ’s verified and intelligent event data, which provides much-needed local context for every store about relevant demand catalysts such as sports (both nearby and televised), school holidays, academic events, severe weather, and 14 other categories of demand causal factors.
Leverage PredictHQ + Amazon Web Services to build scalable data solutions
PredictHQ’s forecast-grade data is verified by Amazon Web Services as a high-quality and reliable data source. As a trusted AWS technology partner, you can leverage Amazon Web Services to build scalable data solutions with PredictHQ data. PredictHQ leverages AWS CloudFront to improve the performance of many of our website endpoints for customers located around the globe.