What Is Demand Forecasting in Retail? A Guide for Growing Businesses

Stitch Labs is a retail operations management platform for high-growth brands.

Demand forecasting is a key component to every growing retail business. Without proper demand forecasting processes in place, it can be nearly impossible to have the right amount of stock on hand at any given time.

Too much merchandise in the warehouse means more capital tied up in inventory, and not enough could lead to out-of-stocks — and push customers to seek solutions from your competitors.

So, what is demand forecasting? And how is demand forecasting done in retail? Below, we’ll explain demand forecasting and how you can use it to support your retail business’ sustainable growth.

Table of contents

  • What is demand forecasting?
  • Why demand forecasting is important
  • Uses of demand forecasting
  • How is demand forecasting done?
  • Demand forecasting tips
  • How to calculate demand forecasting accuracy


What is demand forecasting?

Demand forecasting in retail is the act of using data and insights to predict how much of a specific product or service customers will want to purchase during a defined time period. This method of predictive analytics helps retailers understand how much stock to have on hand at a given time.

What is demand forecasting in economics?

Demand forecasting in economics is a bit different than how a retailer might use demand forecasting in business. So what do we mean by demand forecasting in economics, and how does that differ from retail?

In economics, analysts look at demand in the market as a whole, often for a particular industry or product category. In retail, you’ll look at the demand for YOUR products specifically. Demand forecasting in economics can (and should) inform forecasting in retail.

What is demand forecasting in marketing?

Demand forecasting in marketing is another component for retailers to consider. Get your marketing and operations teams on the same page so that they can share calendars, priorities and initiatives and be proactive in planning. Retail ops can’t provide inventory analytics for extra demand from a marketing campaign if they don’t know about it in the first place.

Why demand forecasting is important

When explaining why demand forecasting is important, the answer spans across several areas of a retail business. One Retail Systems Research report found that nearly three-quarters of “winning” retailers rate demand forecasting technologies as “very important” to their business and their success.

How does demand forecasting contribute to growing businesses? It mostly comes down to two things: becoming more cost-efficient and improving the customer experience.

How demand forecasting makes your business more cost-efficient

Almost every retail business is always looking for ways to cut costs. It’s one of the easiest ways to maximize your profits. When you implement a proper demand forecasting process to your business, you’re cutting costs in a few ways.

Firstly, you’re reducing the amount of capital you have tied up in unneeded inventory. And the less stock on hand you have, the lower your holding costs.  

Secondly, you’re making sure you capitalize on every sale opportunity by not disappointing customers with out-of-stocks.

Those are the two most straightforward ways, but you can also use demand forecasting to operate a lean and agile business, only investing money in more stock when you need to. When you’ve forecasted demand, you can easily check in before the period’s over to see if you’re on target to hit your predicted sales. If you’re looking shy of your goal, you can amp up marketing and advertising. If it looks like you’ve underestimated, you could reorder or prep yourself to cross-promote a related product.

How demand forecasting enhances the customer experience

Another quick way to improve profits? Improve the customer experience. Rather than raising prices, focusing on the end user of the product can lead to customer loyalty and referrals.

Let’s go back to the most obvious: avoiding out-of-stocks that disappoint customers and lead them to your competitors. This is one of the most impactful ways to please customers.

Beyond simply having enough product to meet demand, you can also use forecasting to inform staffing decisions. While this is relevant to businesses needing e commerce management, it especially pertains to brick-and-mortar retailers. Customers who come to your store want to speak to an associate. And if no one’s there to help them, this can make a poor impression on shoppers. Even online sellers need to prep staff accordingly, especially during busy selling periods, so as not to delay shipping and fulfillment.

Uses of demand forecasting

As mentioned earlier, demand forecasting impacts many areas of your retail business. Here are just a few use cases of demand forecasting for rapidly growing businesses needing multichannel management:

  • Prepare accurate budgets and financial planning
  • Make informed purchasing decisions
  • Implement purchase order automations to avoid stock issues
  • Gain a thorough, comprehensive understanding of your business
  • Anticipate staffing needs
  • Grow sustainably
  • Measure progress towards business and sales objectives
  • Streamline production process
  • Plan advertising and marketing campaigns and budgets
  • Enhance the customer experience (avoid out-of-stocks, backorders, late shipments, etc.)
  • Resourcing and project management


How is demand forecasting done, accurately?

Rather than asking “how is demand forecasting done?”, retailers should ask “how is demand forecasting done most accurately?” There are many flaws to every approach to estimating demand and forecasting. Even though we can’t predict the future perfectly, using established methods can help you be more successful in your forecasting practices.

Demand forecasting is done most accurately when a business considers both internal and external data. Internal metrics may include historical sales numbers, ad spend, and website or foot traffic. Externally speaking, you’re looking at factors like industry or consumer trends, the weather, and even your competitors.

To best explain demand forecasting, it’s helpful to look at the different methods. Some of the most common demand forecasting techniques include:

  • Qualitative forecasting
  • Time series analysis
  • Causal model


Qualitative forecasting

This type of forecasting is when a business anticipates demand based on qualitative data. Qualitative data sources could include industry experts and/or consultants, employees, focus groups, and competitive analysis, to name a few. Often, this data is subjective and based on intuition rather than hard numbers or facts.

  • Market research
  • Delphi Method
  • Expert opinion
  • Focus groups
  • Historical analogy
  • Panel consensus
  • Surveys


Recommended for: businesses that have limited historical data; new product launches (especially if there’s no other product like it on the market); instances where the previous period is believed to differ drastically from the planned period (for example, the Tickle Me Elmo frenzy during the 1996 holiday season)

Time series analysis

The time series analysis is a more quantitative approach to demand and forecasting. Rather than expert opinions and “soft” data inputs, a time series analysis uses exact numbers as the basis for forecasting demand. It’s a more mathematical approach to forecasting which uses numerical inputs and trends.

Other quantitative forecasting methods include:

  • The indicator approach
  • Econometric modeling
  • Trend analysis
  • Seasonal adjustment
  • Decomposition
  • Graphical methods
  • Life cycle modeling


Recommended for: retailers that have plenty of past sales data (especially if this data reveals year-over-year trends); seasonal items; seasonal selling periods; identifying cyclical sales trends

Causal model

The causal model accounts for demand forecasting factors that may change predicted demand. Demand forecasting factors are both controllable and uncontrollable:

Controllable demand factors Uncontrollable demand factors
Marketing, sales and promotions Weather
Price Politics
Visual merchandising Trends
Location Competitors
Economic and socioeconomic conditions


Because the causal method of forecasting accounts for so many variables, it’s also a more complex approach. Some of the factors, like the weather, can’t be predicted as accurately as you might like. This includes a part guesswork, part data-driven approach to forecasting — and a lot of trust in your intuition.

Recommended for: data-driven retailers with lots of metrics; forecasting by specific product, category or SKU; retailers in volatile markets; multi-channel businesses with a diverse customer base; forecasting in association with marketing/advertising campaigns and promotions

Demand forecasting tips

Demand forecasting is half art, half science. The best approach is to account for qualitative and quantitative data, internal and external variables, and controllable and uncontrollable factors. Many assumptions must be made, as well as “guesstimations” based off your experiences.

That being said, there are a few tips for demand forecasting that you can apply to ensure you’re doing it properly:

  • Establish a baseline: This should be the first task on your list, aside from establishing a goal or hypothesis that you’ll want to achieve or answer with your forecast. Without having a baseline of data, you’re solely going off of third-party information.
  • Preserve your data: Because using your own data is so valuable in demand forecasting, you’ll also need to ensure the data is clean and accurate. Centralize your inventory information so that everything is synced and in a single location, and you’ll mitigate discrepancies.
  • Invest in the right tools: Without the right tools, demand forecasting can be a tedious, manual process. Find the right inventory management software that integrates with your accounting, point-of-sale and other tools for the most comprehensive look at your business.

    It’s not always clear what to look for in an inventory system, so we created a guide to help.


How to calculate demand forecasting accuracy

It’d be remiss to explain demand forecasting without also describing how to calculate demand forecasting accuracy. After all, demand forecasting can be done by almost anyone — but it’s not always done accurately. And if your forecast is inaccurate, then you risk making majorly impactful business decisions based off the wrong information.

To calculate demand forecasting accuracy, many retailers look at the Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE).


MAD is the average difference between the actual demand and forecasted demand. To calculate MAD, you’ll subtract the forecasted demand from the actual demand. You can then average this number over several time periods to find out your overall MAD.

  Mad Equation



MAPE measures the rate of accuracy of your forecast and is calculated by subtracting the forecasted demand from the actual demand, and then dividing that number by the actual demand. To get the percentage, multiply by 100. Again, you’ll calculate this for multiple time periods and determine the average to find out your MAPE.

 Mape Equation


More Resources

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