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Demand Forecasting: Methods, Models, and Examples

Demand forecasting uses historical sales data to project sales based on external and internal factors. This allows businesses to isolate the variables that influence demand and implement strategies that take advantage of opportunities presented by the numbers.

At the same time, demand forecasts can reveal flaws in a business’s supply chain, marketing, pricing strategy, and more. Demand forecasting can predict the effects of consumer demand and market conditions on the decisions that your business makes. This improves every aspect of an eCommerce business’s operations, from product launches to capital investments.

This guide includes the methods of demand forecasting that businesses need to know, the models for how they work, and the examples that visualize how they change a business’s decision-making strategies.

The Benefits of Demand Forecasting

Demand forecasting uses historical data to predict consumer demand. With more information on the factors that influence demand, businesses can make more informed supply decisions. These benefits trickle down to practically all business operations for eCommerce stores, including:

  • Efficient Inventory Management – A firm grasp of historical sales data helps eCommerce stores refine their inventory. Observing seasonal changes for periods of high and low demand helps a business maintain a more effective supply chain. For example, observing a sale decrease in the same month each year tells your marketing team to focus on engagement during that time, such as with discounts, email offers, and other customer service initiatives.
  • Optimized Production Schedule – A clear demand forecasting methodology can help your eCommerce store maintain a consistent cash flow and keep track of the cash you have on hand. This not only helps optimize inventory purchases but also helps you plan a more efficient supply chain. Businesses that use the trends observed by demand forecasting do not need to put as many items on backorder. They already know what will sell.
  • Adaptability – Demand forecasting models visualize the outside factors that shape demand for your products and services. These could include industry trends, the public’s confidence in the economy, and other changes in your market. Observing these trends helps eCommerce stores adapt to them.
  • Improved Customer Experience – With a more efficient production schedule and inventory plan, eCommerce businesses can offer a better customer experience. Demand forecasting enables this improvement because it addresses all areas of your business’s supply chain.

In eCommerce, understanding demand allows business leaders to make informed decisions on production, marketing, and even staffing. The data analyzed by demand forecasts is a means to a better decision-making process in every operational aspect of the typical eCommerce store.

What Influences a Demand Forecast?

Businesses should consider their internal processes as guiding factors in demand. However, not all the variables in a demand forecast are within a company’s power to change. Often, acknowledging the effect of external forces and planning accordingly is as much a part of demand forecasting as shifting internal procedures to produce results.

The external forces that can influence a demand forecast include:


Changes in competition directly affect demand. Demand forecasting models need to adapt to new competitors or changing behaviors that influence your market.


The location of your retail supply can make or break your customers’ experience. A strategic partnership with a fulfillment company in the areas where most of your customers reside can jumpstart your fulfillment process, reduce shipping times, and create a better lead process. This is especially true of companies whose headquarters is not in the same location as its customers.

The State of the Economy

Economic conditions influence your customers’ buying power, willingness, and needs, all of which change demand. A country slipping into a recession or into a spike of unemployment will trend naturally towards valuing cheaper products over luxury ones.

The Time of Year

Seasonality impacts a demand forecast differently depending on your store’s products by asking, what drives demand for the products you sell? For example, demand for pool equipment spikes in the summer, and demand for coats spikes in the fall.

This seems obvious but demand forecasting allows you to take that fact and push it to a practical result. By observing your peak season, you can change your inventory schedule, staffing, and supply chain initiatives proactively.

How to Create a Demand Forecast

Creating an effective demand forecast requires understanding the methods that demand forecasting can use. There is no ideal demand forecasting strategy since markets, customers, and demand are not static. To reflect changes in how demand is measured for your business, demand forecasting should use and combine multiple methods.

Here are the five main methods of demand forecasting, with examples:

Trend projection

Trend projections observe your historical sales data to plan your future sales strategies. This demand forecasting methodology is straightforward and intuitive. You must observe both when sales changed and why.

For example, consider an eCommerce store that sells kitchen supplies. Using demand forecasting, this store can observe that its months of high activity precede holidays or change with the season. They can use this forecast to strategize their wholesale orders, shipping methods, and promotions.

Additionally, they can observe one-time spikes in demand to dismiss them from their methodology. For instance, if demand increases due to a celebrity endorsement, they cannot count on that again.

Sales force composite

A sales force composite is a short-term demand forecasting method that uses the intuition of the sales team to interpret customer feedback and take advantage of market trends.

For instance, businesses during the COVID pandemic that sell office supplies have had to observe changes in their demand structure and their consumer base. Rather than sell in bulk to offices, they have been remarketing to customers who need supplies for their home offices.

Market research

Market research is an active demand forecasting method that takes advantage of customer demographics to create a marketing plan. For instance, a new eCommerce site can use market research in the form of interactive consumer assessments and surveys to get a feel for their customers’ pain points. Finding out, for instance, that home office workers of a certain age love a company’s chairs can reshape that business’s marketing strategy within their demographic, including income, location, and employment status.


Econometric demand forecasts observe market changes and other external forces to create an organic picture of a company’s product demand. For example, a business that sells luxury beauty products may observe that due to changing economic conditions, the personal debt level is increasing among their customers, making their products less valuable. Conversely, a home repair supplier may discover an increase in demand for their services in the same period.

Delphi method

The Delphi method uses the assessments of experts to facilitate a business’s demand forecast. By assembling these experts, summarizing their assessments, and sharing with the group over a series of repetitions, the goal is to come to a consensus about the forecast. Participants in the Delphi method are kept anonymous to increase their candidness.

For example, an eCommerce business can send a questionnaire to experts all over the world to assess their demand forecast. They can collate the responses and present the questions again until they observe a consensus, with which they can form a marketing strategy.

Obstacles to Effective Demand Forecasting

Demand forecasters face several obstacles that can impede the data from showing optimum results. Demand forecasting’s methodology itself is not the problem, but rather how certain external factors influence the effectiveness of the forecast.

Supply Chain Inefficiencies

Without a well-planned, operational supply chain, demand forecasting cannot predict consumer behavior. By creating an efficient fulfillment process, you can remove variables from the forecast’s equation. This allows you or your experts to troubleshoot specific problems without dealing with distracting extra data.

Inventory Control Issues

Similarly, a lack of efficient inventory management leads to poor demand forecasting. Companies that frequently over or undersupply their warehouses should consider a third-party fulfillment service that handles inventory efficiently.

Lack of Accurate Reporting

Demand forecasting relies on historical sales data. Therefore, established businesses may have so much data with so many uncertain variables that their forecast is imprecise. Organizing data in a usable form is one of the essential building blocks of a useful demand forecast.

Additional Tips for Effective Demand Forecasting

Track results to gauge success

Demand forecasting cannot remain static. A business’s strategy should change with seasonal, economic, and industry demand.

Diversify supply sources

Redundant sourcing can shield your supply chain from relying too much on a single supplier. Backlogs decrease demand. Therefore, a diverse supply chain can improve your forecast.

Share your data

Your supply chain is a network of systems, all of which should be on the same page in terms of your objectives. By communicating with your supply chain and sharing your forecasts, you ensure that data improves your processes at every level.

Avoid dead stock

Items that never sell are a revenue drain for any eCommerce business. Fulfillment companies can help you manage this stock using effective demand forecasting.

The Takeaway

Demand forecasting allows eCommerce businesses to predict consumer needs and optimize supply chains. There is no one methodology to maximize the effectiveness of a demand forecast. Instead, forecasts that use real sales and market data in the context of the economic decisions that shape demand can be combined to help your team identify pitfalls in your business model while playing to your strengths.