What Is Demand Forecasting? A Comprehensive Guide
by Jaidyn Farar
One ecommerce brand orders thousands of new products, expecting them to leap off the virtual shelves—but after months, most of the items are still sitting unsold in the warehouse. Another brand realizes too late that a popular product is out of stock, with droves of customers turning to competitor sites to purchase an alternative.
These businesses have a common problem: they haven’t accurately forecasted customer demand. As a result, they now face inventory challenges that increase costs, decrease revenue, and hurt the customer experience.
Demand forecasting is a critical component of your inventory management strategy—but how exactly does it work, and how can you get started? Read on to find out.
Introduction to demand forecasting
Many factors influence customer demand, and accurate demand forecasting requires you to understand each one.
What is demand forecasting?
Demand forecasting is the process of using historical sales data and other information to predict future demand for products. It involves predicting how many units of a product you’ll sell in a given time.
Accurate forecasting is essential for avoiding two of the most common inventory issues: overstocking and understocking. By analyzing past sales patterns, keeping a close eye on market conditions, and predicting future trends, businesses can accurately predict demand and order the right amount of stock at the right time.
But forecasting goes beyond predicting sales and ordering inventory. Understanding exactly what your customers want (and when they want it) can guide big-picture decisions about product development, pricing strategies, and the rate at which your business scales.
Forecasting demand is important for every business, but it’s especially vital for smaller organizations. When your business is just getting off the ground, even seemingly small demand forecasting errors can lead to major financial repercussions. In contrast, larger businesses often have the resources and flexibility to overcome these errors.
What factors influence demand?
Many factors influence demand, and demand doesn’t necessarily remain steady throughout the year. While your business can control some of the following factors, others depend on outside influences.
- Seasonality. Demand fluctuates based on the time of year, weather, and holidays, creating predictable spikes and troughs. For example, winter clothing sees high demand in colder months, while summer gear peaks during warmer seasons. Most businesses frequently adjust their operations, scaling back during slow periods and ramping up for the busy season.
- Geography. Customer location plays an important role in demand, as preferences and needs vary by region. For example, those in wet climates are likely to purchase more umbrellas and raincoats than those living in deserts.
- Competition. New competitors or the launch of innovative products can disrupt demand for your offerings. If a rival introduces a more affordable or feature-rich product, you’ll probably need to adapt your strategy to stay competitive.
- Consumer preferences. Changes in consumer preferences can change the demand for certain products; you should stay attuned to these trends and adjust your product offerings accordingly.
- Marketing. Your marketing efforts have the power to shape demand for current customers and potential buyers alike. The most successful businesses use advertisements, email campaigns, and promotions to boost demand by raising awareness or creating urgency. This is especially true during key shopping seasons or new product launches.
- Pricing. Demand can fluctuate based on price changes, with lower prices often leading to higher demand and higher prices potentially causing demand to drop.
- Social proof. Positive reviews, testimonials, and referrals from satisfied customers can boost demand by building trust and credibility. On the other side of the coin, negative feedback or bad reviews can harm your reputation and decrease demand for your products.
Types of demand forecasting
Understanding the different types of demand forecasting will help you choose an approach that aligns with your business goals and market conditions. This section highlights key forecasting types, explaining when and why each is effective. We’ll cover the differences between the following six types of demand forecasting:
- Short-term vs. long-term demand forecasting
- Micro-level vs. macro-level demand forecasting
- Passive vs. active demand forecasting
Short-term demand forecasting
Short-term demand forecasting focuses on predicting demand over a relatively short period, usually less than 12 months; when forecasting in the short term, you might look at days, weeks, or quarters.
Short-term forecasting is especially useful for managing inventory levels and staffing during peak seasons or promotional events. But while it provides actionable insights for immediate operational needs, it doesn’t capture broader trends or market shifts. To account for those, you’ll want to use long-term forecasting.
Long-term demand forecasting
Long-term demand forecasting looks further ahead than short-term forecasting, usually covering periods greater than 12 months. It focuses on strategic planning, informing decisions about expanding into new markets, launching product lines, or investing in infrastructure.
A long-term forecasting approach is ideal for businesses aiming to align growth initiatives with demand trends, and it requires a solid understanding of market dynamics and external factors. It’s all about thinking through possibilities about the future of both the company and the world, then preparing for different scenarios.
Micro-level demand forecasting
Micro-level demand forecasting is specific to your business, using data such as historical sales, financial metrics, and internal sales projections. It examines demand at a granular level, taking into account individual products, SKUs, or customer segments. This approach is valuable for optimizing product assortments, tailoring marketing strategies, or managing regional inventory needs.
While it can identify localized trends, micro-level forecasting may miss broader market influences.
Macro-level demand forecasting
On the opposite end of the spectrum from micro-level forecasting, macro-level demand forecasting takes a broader view, analyzing demand across entire markets, industries, or regions.
This type of forecasting is often used to understand economic trends, shifts in consumer behavior, or external factors like market competition. For example, macro-level forecasting might reveal that your industry is growing rapidly, or that customer spending power has increased due to a more robust economy. This might lead your company to make major decisions such as releasing a new product—or holding off until demand is stronger.
Though it provides a big-picture outlook, macro-level forecasting lacks the detail of micro-level forecasting. For the most complete picture of demand, businesses often use both methods.
Passive demand forecasting
Passive forecasting extrapolates past sales data to predict future demand without requiring external inputs. Because this approach is fully automated, it’s a low-maintenance forecasting strategy.
Passive forecasting works best for established businesses with consistent sales patterns and steady growth, because it doesn’t account for sudden market changes or external disruptions. When conditions are less predictable, active forecasting is a more adaptable alternative.
Active demand forecasting
Active demand forecasting is dynamic and forward-looking. Whereas passive forecasting relies on historical data that’s automatically collected and analyzed, active forecasting incorporates external variables like marketing campaigns, competitor activity, and industry trends. Because active forecasting data isn’t easily collected by computers, it often requires the expertise of those with knowledge of the business, industry, and market.
This approach is helpful for businesses launching new products, entering new markets, or making significant strategic changes. However, in stable conditions where historical trends are reliable, passive forecasting might be more appropriate.
Benefits of demand planning and forecasting
Accurate demand forecasting allows organizations to improve their inventory management strategies, save money, and make better decisions about the business’s long-term trajectory.
Better inventory management
Demand forecasting plays an important role in inventory management. You’ll see fewer stockouts and overstocking scenarios, lower costs, and greater customer satisfaction.
- Fewer stockouts. Demand forecasting helps you plan production and inventory ordering more effectively, ensuring you don’t run out of products when customers need them. No more lost sales here!
- Reduced overstock. By forecasting demand more accurately, you’ll avoid tying up capital in excess inventory. This helps free up cash flow for other business priorities and reduces the need for discounts to clear out unsold stock.
- Minimized product obsolescence. If you overorder items that have a short shelf life (like perishable food) or are constantly being upgraded (like electronics), you risk waste and financial loss. Demand forecasting helps you avoid this pitfall.
- Lower storage costs. Businesses that accurately predict demand avoid ending up with excess inventory that won’t sell. This saves them money on storage and prevents valuable warehouse space from being tied up with unsellable stock.
- Improved customer satisfaction. When products are always available when they’re needed, customers will make more purchases and be happier with their shopping experience. Out-of-stock notifications, on the other hand, may send consumers scrambling to competitor stores.
Better budgeting
Demand forecasting supports better budgeting by providing a clearer picture of future revenue and expenses, allowing you to allocate resources more effectively.
With accurate predictions of demand, you can plan production, marketing, and inventory investments with greater confidence, making sure funds are directed where they’re most needed. Additionally, you’ll know when the time is right to invest in new employees and warehouse technology to expand your fulfillment operations.
Better business decisions
How fast should your business scale? Are you growing too quickly or too slowly? When should you introduce that new product? Is it time to begin shipping internationally?
These major questions (and many others) can be answered with the help of demand forecasting. Forecasting lets you make more informed business decisions by providing data-driven insights into market trends, customer behavior, and potential challenges. With a clearer understanding of future demand, you can confidently plan for new product launches, adjust pricing strategies, and allocate resources to areas that will drive growth.
Forecasting also helps you anticipate changes in the market, such as shifts in consumer preferences or the impact of external factors, so you can easily adapt to those changes.
Common demand forecasting methods
At first glance, demand forecasting might sound simple. When gauging demand for a specific product, you only need to track how well that product has sold in the past—right? Actually, forecasting goes far beyond simple sales data. In order to forecast demand for broader product categories, understand how external factors will shape demand, or predict how new products will perform, you’ll need to go deeper.
This section will walk through some of the most common demand forecasting methods, including when each is most appropriate and useful. But first, let’s discuss the difference between quantitative and qualitative forecasting.
Quantitative forecasting relies on numerical data and statistical methods to predict demand, so it’s ideal when you have plenty of historical sales data. Examples include trend projection and machine learning.
Rather than using historical data, qualitative forecasting is based on expert opinions, market insights, or customer input, which makes it particularly useful in situations with limited historical data. Methods like market research and the Delphi method fall into this category.
Together, quantitative and qualitative forecasting offer complementary ways to predict demand depending on the available resources and context.
1. Trend projection
Trend projection uses historical sales data to identify patterns and predict future demand. It’s straightforward and well-suited for businesses with consistent sales growth or seasonal trends.
To use trend projection, start by gathering your historical sales data and plotting it over time. Look for consistent patterns—like sales increasing during specific seasons or dips after holidays. Tools like Excel or Google Sheets can help you calculate growth rates and project those trends forward.
This method works especially well if your sales patterns are steady and not heavily influenced by external factors. Be sure to revisit your projections regularly and adjust them as needed, especially as new trends emerge.
2. Market research and surveys
When using this qualitative method, you’ll gather insights directly from customers or target markets through surveys, focus groups, or interviews. While you can use market research in a variety of contexts, it’s especially helpful if you’re considering launching a new product or expanding into a new market.
To find out what your customers are thinking, you just need to ask. Try creating surveys and distributing them via email, social media, or even post-purchase thank-you messages. Keep your questions focused on preferences, buying habits, or anticipated future needs. For example, you might ask, “What products would you like to see us add next year?” or “How often do you shop online for [your product type]?”
For deeper insights, consider conducting focus groups or interviews.
3. The Delphi method
The Delphi method is a structured approach that collects and synthesizes expert opinions through multiple rounds of anonymous feedback. It’s useful for making strategic predictions in uncertain or complex markets.
The Delphi method is a great option if you want expert insights but don’t want groupthink to skew your results. Simply reach out to a group of industry experts—this could include team members, suppliers, or even external consultants—and gather their predictions anonymously through a series of questionnaires.
After each round, share the aggregated responses (still anonymous) with the group and ask for updated input. This process continues until a consensus is reached.
4. Econometric models
An econometric model combines historical sales data with external factors, such as economic trends, competitor behavior, or market conditions.
For businesses impacted by external factors, econometric models can connect the dots between demand and variables like pricing, competitor activity, or economic trends.
To get started, you’ll want to identify key factors that influence your sales. For example, do your sales spike when a competitor runs out of stock? Once you know the relationships, you can use advanced forecasting tools to create your model.
While econometric methods are more complex, they’re perfect for businesses looking to fine-tune pricing strategies or adapt to broader market changes.
5. Machine learning and predictive analytics
Machine learning is all about using advanced algorithms to analyze large datasets, uncover hidden trends, and produce highly accurate forecasts.
Machine learning might sound intimidating, but don’t worry—it’s easier to get started than you think. Many platforms now integrate AI-powered analytics that can help you forecast demand without requiring technical expertise.
For accurate results, ensure you’re tracking detailed data, such as customer behavior, website traffic, and purchase trends. The algorithms will process this data to identify patterns and predict future demand. Start small and refine your models over time for even more accurate predictions.
Challenges in demand forecasting
The three challenges below frequently trip businesses up. When you understand the potential hurdles, you’ll be more prepared to overcome them.
- Collecting data. Accurate demand forecasting relies on high-quality, consistent data. However, many businesses struggle to gather complete or reliable data, which can lead to inaccurate predictions, missed opportunities, and inventory issues like shortages or overstocking.
- Accessing data. Even when businesses collect the right data, accessing it in a usable format can be a challenge. Disorganized or siloed data across different systems can delay the forecasting process and obscure crucial insights; this ultimately leads to poor decision-making and inaccurate demand predictions.
- Navigating unpredictable market conditions. The popular advice “expect the unexpected” is easier said than done. Sudden economic shifts or unexpected events (such as a natural disaster or a global pandemic) can make demand forecasting difficult. These factors are often outside your business’s control and can lead to dramatic changes in consumer behavior, making previous forecasts obsolete.
Keep fulfillment running smoothly with EasyPost
Demand forecasting allows you to make the best business decisions for the short and long term, providing customers with the product they want at exactly the right time. But having the right amount of stock in your fulfillment centers won’t do you any good if you can’t ship it quickly and accurately when orders arrive.
That’s where a shipping API like EasyPost comes in. EasyPost makes it easy to connect with parcel carriers of all sizes and specialties, allowing shippers to access discounted rates, generate compliant labels, and offer post-purchase features like tracking.