Demand forecasting and sales forecasting are two distinct but related concepts in business planning.

Demand forecasting involves predicting the number of customers who will desire and potentially purchase a particular product or service in the future, helping businesses plan for production, inventory, and marketing.

Sales forecasting, on the other hand, focuses on estimating the revenue a business will generate from selling its products or services, taking into account factors like pricing, competition, and marketing efforts.

While both types of forecasting aim to anticipate future market conditions, demand forecasting focuses on consumer interest, while sales forecasting predicts financial outcomes.

Let’s dive deep into each and understand how to choose the right one for your business.

What Is Demand Forecasting?

Demand forecasting is the process of predicting how much of a certain product or service people want to purchase in the future.

In other words, you’re trying to figure out how many people are interested in what you’re selling.

This information can help a business plan for production, inventory management, and marketing strategies.

Let’s use pizza sales as an example.

Suppose you own a pizza shop, and you want to know how many people will want pizza on a given day.

You might look at data like the day of the week, the weather, or even local sporting events to predict how many people will be interested in buying pizza.

This is demand forecasting.

Now that we understand it, let’s explore sales forecasting to see how it differs.

What Is Sales Forecasting?

Sales forecasting, on the other hand, is the process of estimating how much revenue a business will generate by selling its products or services.

This involves not only understanding how many people are interested in what you’re selling (demand), but also taking into account factors like pricing, competition, and marketing efforts.

Sales forecasting helps a business make decisions about things like hiring, budgeting, and overall growth.

Using the pizza shop example again, let’s say you already have a good idea of how many people will want pizza on a particular day (demand forecasting).

Now, you want to know how much money you’ll make from those sales.

You’d need to think about things like the price of each pizza, any promotions you’re running, and how close your competitors are to your shop.

This is sales forecasting.

Although sales and demand data are connected, sales data is not always an accurate representation of true demand. Let’s discuss why this is the case.

Why Sales Data is Not Always the Same as Demand Data?

Even though sales data and demand data are closely related, they do not always accurately represent the same information.

There are several factors that can cause discrepancies between the two, leading to situations where sales data may not provide a complete picture of real customer demand.

Here are some reasons explaining why sales data is not always the same as demand data:


A stockout occurs when a business runs out of inventory for a particular product, so it can no longer meet the demand from customers.

In this case, sales data will reflect a lower number of sales than actual demand, as customers are unable to purchase the out-of-stock product.

This discrepancy can lead to underestimating the true demand for such a product when relying solely on sales data.

Price Sensitivity

Customers’ purchasing behavior can be influenced by price changes.

For example, if a product’s price is reduced, more customers may decide to purchase it, leading to higher sales.

However, this increase in sales does not necessarily mean that more people wanted the product; rather, the lower price simply made it more attractive to buy.

Thus, sales data in this situation does not directly represent customer demand at different price levels.

Conversely, if a product’s price increases, there may be fewer sales as people may not find the product worth the higher cost.

Again, this does not mean that demand has reduced, but rather that customers’ willingness to pay has changed.

Promotional Activities

Marketing promotions, such as discounts, seasonal offers, or bundled deals, can have a significant impact on sales.

These promotions attract more customers, increasing sales temporarily.

However, this boost in sales may not accurately reflect the typical demand for that product, as customers may simply be taking advantage of the promotion rather than showing an inherent desire for the product.

Competitor Actions

A competitor’s actions can also impact sales without necessarily affecting the actual demand for a product.

For instance, a competitor might temporarily run out of stock, leading customers to buy from your business instead.

In this case, your sales data may show a temporarily increased number of sales, but the underlying customer demand has not changed.

External Factors

Various external factors can influence sales data without providing an accurate reflection of customer demand.

Some examples include seasonality, weather, economic conditions, and global events.

These factors may cause a temporary increase or decrease in sales, even though the underlying demand remains consistent.

Why Use Sales Forecasting Instead of Demand Forecasting?

From the perspective of concretely building and using forecasting models, there are several advantages to using sales forecasting instead of demand forecasting due to the challenges in predicting demand.

Data Availability

Sales data is more readily available and easier to collect for most businesses.

This data is generated through actual transactions, allowing businesses to build accurate and structured models based on historical performance.

In contrast, demand data is more challenging to collect and quantify, as it represents potential customer interest, without the influence of factors like stockouts or pricing.

Incorporating Market Factors

Sales forecasting models can incorporate different market factors, such as pricing, competition, and promotional activities, enabling businesses to better understand and react to market conditions.

Demand forecasting models focus primarily on customer interest, which might overlook the impact of these additional factors on actual sales.

Financial Planning

Sales forecasting allows businesses to generate detailed revenue projections, which are essential for budgeting, financial planning, and setting sales targets.

This level of financial detail is not as easily extracted from demand forecasting models, as they focus on consumer interest without incorporating the specific revenue implications.

Forecasting Model Complexity

Building demand forecasting models can be more complex, as they require the incorporation of various external factors and assumptions.

For example, accurately capturing demand data could involve analyzing customer surveys, web analytics, or social media trends, which are often challenging to incorporate into forecasting models.

In contrast, sales forecasting models use historical sales data, which is usually more structured, easier to analyze, and more directly tied to revenue outcomes.

Easier Model Validation

Given that sales data is based on actual transactions, the accuracy of sales forecasting models can be more straightforward to validate by comparing predictions to historical sales performance.

However, validating demand forecasting models can be more challenging due to the difficulty of obtaining ground truth data for demand, as it is inherently more abstract and harder to quantify.

Considering the challenges of demand forecasting, you might prefer using sales forecasting.

However, it’s important to be aware of potential drawbacks associated with sales forecasting as well.

What Are The Potential Drawbacks of Sales Forecasting?

While sales forecasting provides valuable insights into revenue generation and helps businesses make informed decisions, it does have several potential drawbacks that should be considered:

Lack of Demand Information

Sales forecasting primarily focuses on revenue generation and can underestimate or overlook actual customer interest, as it does not directly measure demand.

Solely relying on sales forecasts may cause businesses to miss opportunities to grow market share or identify unsatisfied customer needs.

Reactive to Market Conditions

Sales forecasting is highly influenced by market conditions, price changes, promotional activities, and competitive factors.

As a result, models may prove to be reactive in nature, adjusting to past events and struggling to identify new trends or shifts in customer behavior.


Sales forecasting often relies on assumptions about future market conditions, which may not always be accurate. Economic shifts, unforeseen market developments, or sudden changes in consumer preferences can make the forecasting models less reliable.

Short-term Focus

Sales forecasting can sometimes focus too much on short-term projections and can be less effective for longer-term planning.

Predicting future sales beyond a certain time frame becomes increasingly uncertain, which may hinder accurate long-term strategic planning and investment decisions.

Data Quality Issues

Sales forecasting models depend heavily on the quality and availability of historical sales data.

Inaccurate, incomplete, or outdated data can lead to unreliable forecasts, which could impact business decision-making and resource allocation.