Customer demand forecasting methods & models: a comprehensive guide

In today’s fast-paced business world, staying ahead of the curve is essential for survival. Whether you’re a small startup or a large corporation, accurately predicting customer demand for your products and services can mean the difference between thriving and merely surviving. That’s where demand forecasting comes in.

In this article, we’ll dive into the world of demand forecasting. We’ll explore what it is, why it matters, and how different methods and technologies can help you and your business stay one step ahead of the competition!

What is a demand forecast, and why is it important?

A demand forecast is essentially a crystal ball for your business. It’s a prediction of how much customer demand you will see for your product or service in the near and distant future. Depending on how far ahead you want to predict demand, this forecast often covers a specific time period, such as the next month, quarter, or full year ahead.

The importance of accurate demand forecasting in inventory management, supply chain, and sales

Whether it’s a short or a long-term focus, the real objective is to drive efficiency and optimization. Having a view of upcoming demand helps businesses ensure they’re operating at the right level, whether that’s holding the correct stock, managing future supply chain orders, or using sales data to predict revenue and profits.

Having the best demand forecasting helps businesses go on to make the right decisions about spending, resources, and strategies, giving them the best chance of beating out their competition and hitting their strategic objectives.

3 real-world examples of demand forecasting

But how can you actually use this sort of data to predict demand for a product? To find out, let’s take a look at three real-life use cases of consumer demand forecasting in action.

• Seasonal demand. Many organizations use seasonal data sets to accurately predict the short-term demand of customers. Temperature and weather are two of the most common seasonal factors that influence demand, with hot weather calling for products like sunscreen and bottled water, whereas rainy seasons driving sales of umbrellas and raincoats.
• Societal trends. Long-term demand, such as future fashion lines or technological ranges, are often influenced by broader societal trends, with events such as political changes or pop culture wielding the power to affect demand over time.
• Past behaviors. Historic sales data provides the backbone of many e-commerce demand forecasts. This type of quantitative demand forecasting uses customer’s buying history to predict what they’ll buy next, even prompting customers to re-purchase perishables on a regular basis.

The 8 types of demand forecasting methods to estimate demand

Demand forecasting is the process of using data to predict future behavior, but not all demand forecasting methods are built the same. To help you understand the best way to meet customer demand, let’s look at 8 variables that all contribute to the different types of forecasting methods on offer.

 Passive forecast demand vs. active forecast demand

The first point of comparison is to look at active demand forecasting versus passive demand forecasting.

With passive demand forecasting, organizations set up a “fire and forget” forecasting process, that’s usually completely automated. The forecast brings together historical data sets and makes projections based on patterns and trends that have built up over time. The assumption here is that the future will replicate the past, so it’s not always best for fast, unstable companies. Purely or mostly passive forecasting is best for companies with stable sales and consistent growth in non-volatile markets.

Active demand forecasting is a lot more hands on. In active demand forecasting, information is drawn not from non-standard data sources, use specialized knowledge and use a range of different statistical methods to predict future behavior. Of course, this active management requires more resources and is often more costly, but for fast-growing companies in dynamic industries, active forecasting helps to accurately forecast future behavior far better than the passive equivalent.

Short-term demand forecasting vs. long-term demand forecasting

The second lens to look through when comparing demand forecasting models is short-term demand forecasting versus long-term demand forecasting.

While every company will define ‘short-term’ differently, this use of demand forecasting typically looks anywhere up to the next quarter or the next full year. In some selected circumstances, it may be even more tactical, used to predict upcoming weekend sales or fortnightly inventory management requirements.

Some companies use demand forecasting for longer-term projections, measured in years, to help them plan more strategic investments. Like everything long-term, predicting this sort of demand for products requires some assumptions, which in many instances may turn out to be untrue. That said, this sort of forecasting system is still valuable, helping to simulate different scenarios and compare the likelihood of different macro-environment changes.

Internal (micro) demand vs. external (macro) demand

To predict future demand, companies often create a mic of their own internal data versus while also leveraging external data sets.

In economics terms, microeconomics focuses on the behavior of a company’s customers – essentially their internal data sets. For demand forecasting, this means using historical sales data, stock volumes, and past financial performance metrics to predict future demand for a product. While this is a good starting point, if used in isolation, it can create a bit of a black hole in terms of prediction.

External, macro-level, data helps predict the demand for products and services across the wider industry or economy. This helps better understand broader societal trends, industry innovations, and economic movements, which may be big factors in driving a change in demand. Used alongside internal data, this gives a nice blend of the inside and outside view, but this data often needs to be purchased from specialist insight agencies.

Quantitative methods vs. qualitative methods

Lastly, when it comes to choosing the right types of data, companies often utilize a mix of quantitative and qualitative demand forecasting methods.

When large quantitative data sets have been produced, organizations need to choose the right analysis methods to determine how they should work to meet demand. Popular methods include moving averages, trend projection, or econometric modeling, with many organizations using advanced AI and Machine Learning models to boost their analysis and produce more accurate forecasting (more on that later!)

Where organizations want a more tactile view of demand for products or services, qualitative methods such as Delphi, surveys, or focus groups are used to get a deeper, more personal understanding of market demand. While these sorts of data sets have far less volume, they are typically a lot richer, uncovering factors that impact demand in a way that numbers and algorithms can’t.

What factors can impact demand forecasting accuracy?

But even the best, most accurate forecasts can go awry. Here are some factors that can cause demand volatility and impact demand trends over time.

1. Economic changes. Recessions, booms, and everything in between can dramatically impact consumer behavior. While many people associate economic changes with negative consequences, periods of economic growth can lead to increased demand too.
2. Competitor actions. A new product launch or aggressive pricing strategy from a competitor can shake up the market. These are actions that quantitative forecasting won’t pick up, so it’s useful to use some qualitative forecasting techniques too.
3. Technological advancements. New tech can make your product obsolete or suddenly put it in very high demand. Including this data helps understand how forecasting affects your supply chain, ensuring you have the orders in place to get your hands on the latest tech if needed.
4. Seasonal variations. As we saw earlier on, weather is one of the contributors to demand forecasting challenges. It’s famously difficult to predict the weather, so many businesses plan for the inevitable ups and downs brought on by changes in the seasons.
5. Marketing and promotions. While you can use data to project future demand, you can take actions into your own hands by boosting your marketing and promotion activity. After all, nothing drives demand for a new product like a well-crafted advertisement campaign.
6. Supply chain disruptions. One of the biggest challenges of demand forecasting is factoring in unseen events such as supply chain disruptions. While you can forecast demand perfectly, if supply chains are held up, it can be impossible to meet demand now, meaning it’s difficult to accurately predict demand for the future.
7. Political and regulatory changes. One of the common factors to impact demand forecasting is new laws or political events. Unforeseen changes to policies may drive customers towards or away from your products at a moment’s notice, so try to use qualitative methods to factor in political uncertainty.
8. Data Quality. Demand forecasting is essential for many businesses, but it’s only as good as the data you put into the system. If your data quality is poor, the best way to improve the accuracy of demand forecasts is to invest in higher-quality, more consistent data to help you and your team make more informed decisions.

How to choose the right demand forecasting software?

Demand forecasting is important for businesses in any sector, but it can’t be done without robust processes, procedures, and techniques to bring it all together in one place. That’s where demand forecasting software, such as Microsoft Dynamics 365, IBM Planning Analytics, GMDH Streamline comes into play, helping to align data sets, automate forecasting, and instantly create business-ready intelligence.

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What are the key features to look for in demand forecasting software?

Ensure any software package includes:

• All of the data sets you need to meet demand forecasting targets
• Interfaces to other data sets which you can use to predict demand
• Reporting and dashboarding so the forecasting model can help you and your business make the right decisions
• Has scalability to be used for short and long-term demand forecasting
• Is easy to use – both for data-literate and non-data-literate colleagues

How do they tackle the challenges of demand forecasting?

As we’ve already seen, there are many challenges to overcome with demand forecasting. To help, make sure your software can:

• Spot and overcome factors impact demand forecasting such as poor data quality or tracking competitor action
• Work with different types of demand data, such as qualitative and qualitative data sets
• Tap into broader macroeconomic data sets to help you smooth out spikes and dips in demand
• Sync with your marketing platforms to assess how renewed efforts may impact demand

Can they show you demand forecasting examples?

Any good software supplier should be to demonstrate practical value-adding features. Make sure any provider can:

• Show you how demand forecasting is used in practice by other clients
• Show you how it can flex to different niches – e.g., demand forecasting in supply chain or sales environments
• Show you how forecasting enables their client to deliver tangible results and a solid return on investment.

How can partners such as Inetum help you use Machine Learning & AI to improve the demand forecasting process?

The demand forecasting process is a hard one to master. But, with demand planning so crucial to business strategy, decision-making, and cost optimization, what else can be done to improve the accuracy and speed of your forecasts?

Like many things in the technology world right now, the rise of Machine Learning and AI is turbocharging the accuracy, completeness, and power of data when forecasting demand. Unlike traditional methods, which use basic assumptions and algorithms, Machine Learning analyzes vast amounts of historical data to identify complex patterns previously unseen to the human eye. These algorithms also have the power to overcome challenging external factors, such as economic and seasonal events, which may also affect demand, to give a truly revolutionary view of future demand.

To leverage all of the benefits of demand forecasting, we recommend teaming up with an expert AI partner who can guide you on the road to next-level analytics. At Inetum, we’re passionate about generative AI, helping companies across the globe enhance efficiency, foster growth, and drive innovation. If you want to learn more about how we can help you, check out our Generative AI page, or reach out to our experts:

Elevate Your Data Strategy Our customized Data solutions align with your business objectives. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance. Schedule a meeting