How to Forecast Demand for Your Retail Store (and Why You Should)

How to forecast demand for your retail store

Demand forecasting is the process of projecting future revenue and which products shoppers will buy using quantitative and qualitative data.

It helps you make smart decisions about your product offering, inventory, staffing, and marketing. Without demand forecasting, you’re at the risk of making costly mistakes.

You might have questions that are hard to answer without demand forecasting, such as:

  • Would opening a new brick-and-mortar location be a huge gamble?
  • I want to invest in offering a new product line; how can I check if customers want it?
  • How well will my store operate three years down the line?
  • How many employees do I need to hit my sales and revenue goals? Will I be able to afford them?

We’re diving into everything you need to know to forecast demand for your store, including why demand forecasting matters, the types and methods you can use, factors that play a role in customer demand, and examples to get you started.

What is demand forecasting?

Demand forecasting allows you to estimate your store’s sales and revenue for a specific period in the future.

Historic sales data plays a massive role in demand forecasting, but you can also take into account other factors like customer feedback, insights from experts, economic trends, and the opinions and predictions of your sales force.

The exact data you’ll use depends on the demand forecasting method you choose (which we cover in detail later), but it should give you answers to questions like:

  • How much staff do I need on the shop floor in a specific season?
  • How much inventory of each product should I have?
  • How often should I restock specific products?
  • How much payroll can my cash flow cover in different seasons?

Why is demand forecasting important?

Demand forecasting is critical to the future of your store as it helps you reduce risks and make the right call on many fronts. Here are the key benefits of demand forecasting to consider.

Have the right products in stock

Efficient inventory management ensures you have the right products on hand exactly when your customers want them, but not so much that they go bad or become irrelevant.

Without demand forecasting, it’s easy to run out of stock (and end up with disappointed customers who leave empty-handed) or overstock a product (and waste money on products you can’t sell).

By forecasting demand, you can prepare your supply chain and inventory for any expected peaks, as well as prevent excessive inventory that hurts your cash flow and increases storage costs. 

With Shopify POS, merchants can get recommendations on which products to restock based on both their profitability and sales rate, and get notified whenever a product’s inventory levels reach a certain threshold so they can restock before they run out of stock. 

Assess the risk of launching new products

Some demand forecasting methods—those that include market research and the input of your store associates and customers—can reveal demand for products you don’t yet offer.

You have to account for the costs of manufacturers, suppliers, storage, and marketing of a new product. This can be a massive gamble. That’s why it pays off to use what you’ve learned through demand forecasting and minimize the risk that comes with sourcing a product you’ve never sold before. 

Make smart staffing decisions

Having the right number of employees staffed to support customers shopping is a win for everyone. But just like missing the mark with product inventory, staffing mistakes can be costly.

If you don’t have enough employees at peak demand, you’ll force your customers to wait for service, which can deter from your customer experience and cause them to shop elsewhere. 

And if you have too many employees when in-store foot traffic dips, you’ll waste money on payroll.

Consider using historical sales data to plan your staff schedule, and never be afraid to adjust throughout the day and cut shifts short if you realize you’ve scheduled more staff than necessary. 

💡PRO TIP: One way to assure you have enough store associates scheduled is to use historical sales data to pinpoint peak sales times. Head to your Shopify Admin retail sales reports to see your stores peak sales times.

Run better marketing campaigns

Understanding customer demand makes your marketing efforts more intentional and cost-effective.

For example, when you know a drop in sales is coming, you can run limited-time discounts or double down on your loyalty program and encourage repeat purchases. That’s also a great time to explore low-cost digital marketing ideas or social media contests.

And leading up to peak sales periods, you can partner with local influencers or experiment with paid ads on social media and Google to capitalize on the high demand.

Budget accurately and maintain positive cash flow

All the benefits of demand forecasting boil down to your budget and cash flow. Can you always ensure you have the means to cover your fixed and variable expenses like cost of goods sold, your commercial space lease and payroll? 

Your chances of answering ‘yes’ go up when you anticipate fluctuations in customer demand and the expenses that come with it (along with calculating your break-even point). You’ll end up with projections for your revenue and profit margin.

With data-backed sales forecasts, you can shift your budget as needed instead of jumping into panic mode because your revenue dramatically dipped.

Types of demand forecasting

Demand forecasting and the data you use varies based on your goals. Here are several popular ways to forecast demand for use cases you’re likely to encounter: 

External macro-level demand forecasting

External demand forecasting looks at the broader economy and how macro trends may impact your store and your goals..

This includes economic conditions (like inflation, GDP, and unemployment levels), your competitors, emerging trends, and shifts in consumer behavior.

By understanding what’s happening outside of your store (and your control), you’re better prepared to face challenges like material shortages or supply chain issues and find solutions

Internal demand forecasting

Internal demand forecasting explores how your internal resources relate to external demand forecasting.

These include business financing, supply chain management, cash flow, personnel, and other elements of your internal operations.

For example, if the external demand forecast reveals customer demand will double at your store’s location over the next two years, can your store adapt to it? Will you have the staff, space, and inventory to serve more customers? 

Internal demand forecasting identifies any restraints and challenges you could run into in your store.

Short-term demand forecasting

In this case, short-term refers to consumer demand for a period of up to a year.

Retailers can use short-term demand forecasting to prepare their inventory for a seasonal peak in demand (think summertime, Black Friday, winter holidays) and to act quickly to shifts in market behavior.

If your store uses just-in-time (JIT) purchasing, this type of forecasting helps you efficiently meet demand despite holding low product stock. Use historical sales data, typically accessible via your point-of-sale (POS) system, to plan for short-term demand. 

Long-term demand forecasting

Long-term demand forecasting takes into account periods longer than a year and up to four years. This way, you can understand annual patterns and seasonality of product demand.

This is useful for planning your inventory on a seasonal basis, as well as your marketing, launches, and store expansions. Similar to with short-term demand forecasting, use your store’s historical sales data to plan around macro sales trends such as seasonality.

We use up to three years of data, averaged at product level, along with lead time as the basis of the demand forecasting formula. When you have this data, you can then add factors like plans to increase sales, or the input from your marketing team, to understand how demand will change so you can prepare your supply on time.

Passive demand forecasting

Passive demand forecasting uses historical sales data to predict future data. It doesn’t require statistical analysis or looking at the broader economic trends.

The nature of the passive approach requires your store (or the product or category you’re exploring) to have past sales data, so this approach likely isn’t viable to predict demand for new products or initiatives. 

Passive demand forecasting is based on the assumption you’ll keep your current product and marketing strategy and that you won’t change your course of action, which is likely to cause results to no longer align with projected results.

Active demand forecasting

Active demand forecasting is better suited for new stores and those that plan to grow rapidly. It takes into account growth plans, including product development and marketing, as well as economic conditions and market trends.

This approach is crucial for store owners who plan to change their product offering often and introduce new collections, explore activating new or experimental marketing initiatives, and open more storefronts to widen their brand footprint, reach new customers and markets. 

Quantitative vs qualitative demand forecasting

Demand forecasting methods can be categorized as either quantitative or qualitative.

Quantitative demand forecasting focuses on the existing hard data for your store or industry. This can include sales and revenue figures, marketing analytics, and economic indicators.

Trend projection method, barometric technique, and econometric method are quantitative forecasting methods.

Qualitative demand forecasting uses expert opinions, market research data, and estimates to predict consumer demand. This approach works best for new stores and product types that don’t already have quantitative data to work with.

Delphi method, customer surveys, and sales force composite method are qualitative forecasting methods.

Demand forecasting methods you can use

Once you know the type of data you have access to (or that you plan to collect) with your POS software, online sales data, or both), you can choose from the following demand forecasting methods.

Trend projection method

The trend projection method is often used in business forecasting. It works well for stores with lots of historical sales data (two years or more).

In this method, you use your past sales and revenue data to project future sales. You assume that the factors that made your past sales possible will continue to play the same role in the future, including customer needs and competitors in your location.

If your store is new, you can use the trend projection method based on sales data from stores in your area if it’s available to you.

It’s important to account for any anomalies in your past data that might be difficult to replicate on the same scale, like a one-off PR campaign that brought unprecedented foot traffic to your store for weeks.

Barometric technique

The barometric technique forecasts the trend in the overall economic activities. These predictions are based on economic indicators that include:

  • Leading indicators, which are performance indicators that might predict future events
  • Lagging indicators, which are indicators of past performance and the impact of past events
  • Coincidental indicators, which measure current events in real time

An increase in new loyalty program sign ups can be a leading indicator because it projects more recurring purchases into the future. An increase in product returns is a lagging indicator because it shows the success (or the lack thereof) of products you sold in the past. Finally, staff turnover can be a coincidental indicator as it represents your capacity as a store in real time.

Econometric method

The econometric method is more complex than others as it relies on statistical tools and mathematical formulas to understand the relationship between demand and influential factors.

It combines existing sales data with external factors like economic conditions to reveal correlations and future sales. For example, the econometric method can explore whether consumer demand for a commodity like coffee depends on the population of a city.

A real-life example comes from The Australian Financial Review, which revealed that population growth is slowing down, which might have a long-term impact on retail floor space and retail sales growth.

Delphi method

The Delphi method of demand forecasting relies on a panel of experts to project future sales. It’s a systematic way to gain opinions and forecasts from independent experts over multiple rounds.

After you recruit your experts and choose a facilitator, you define the focus of the session. What is it you want to understand better? What’s the problem you hope to solve?

Then you focus on building questionnaires for multiple rounds of responses. The process is straightforward: you gather responses in the first round, share it with the panel, and move onto the next round. Each round of questionnaires builds on the previous one.

Answers are anonymous, which allows every expert to share genuine responses. The Delphi method isn’t held in-person, so you can recruit experts within your store’s city or state, or elsewhere if relevant.

Market research and customer surveys

Market research demand forecasting relies on data from customer surveys. This can be particularly useful to stores that are just getting started and want to shape their product range and marketing strategies.

Data from customer surveys are also valuable to stores that want to expand to new locations, explore new products, or improve their existing ones. You can also explore store layouts and signage that could potentially improve sales based on what you learn.

Sales force composite method

The sales force composite method considers inputs of the people working on your shop floor.

Your employees are on the front lines of your store. They spend the most time with your customers and have a mountain of knowledge about their questions, needs, behaviors, complaints, and overall experiences.

As your employees share their insights on customer behavior and feedback— and how it relates to future customer demand—you aggregate all feedback and develop the demand forecast.

Factors that influence demand forecasting

What does demand forecasting depend on? What makes a difference? Here are the main five factors that influence your store’s forecast.


Seasonality implies there are periods of the year when the demand for a product becomes higher than other times, and that it’s a pattern that repeats every year.

The drivers of seasonality include factors like:

  • Holidays (like 4th of July or Christmas)
  • Weather changes and activities they bring (skiing in winter, surfing in summer)
  • Events (wedding or festival season)

Examples of seasonal products include sunscreen, surfboards, weighted blankets, garden furniture, school supplies, and candles.

Google Trends graph showing the search volume for sunscreen

Google Trends graph for the term ‘sunscreen’


If there are new stores opening that meet the needs of your customers, it will likely affect your demand forecast.

Of course, things are more complex than that as other factors, like pricing strategies, promotions, population at your location, people’s lifestyles, and more, but it’s important to always be aware of who you’re up against.

Keep in mind this isn’t necessarily limited to brick-and-mortar stores like yours and can include supermarkets, pop-up shops, and online-only stores.

Product types

The type of products you sell are a huge factor in forecasting demand. Think about some of these:

  • Do your customers need to replenish a product, or is it a once-off purchase?
  • How frequently does an average customer purchase from you?
  • What is the average spend per purchase?
  • Are there combinations of products customers commonly buy together?

From a single customer’s perspective, the demand for a fridge is dramatically different than for a laundry detergent. Keep this in mind as you consider other internal factors along with the broader economy and external influences.


Where your retail store is located plays a role in demand forecasting.

How many people pass through that street daily? Is there anything nearby that can attract or detract customers? How does that change between seasons and/or weather conditions? Is there a competitor located near you?

Start with these questions to uncover all the ways your location can influence future customer demand.

For example, for their expansion into Croatia, German drugstore, Dm-Drogerie Markt, chose retail locations in popular shopping areas in cities with more than 10,000 residents, including malls, walking zones, shopping streets, and areas near markets and grocery stores.

World events

The year 2020 and the pandemic flipped retail and ecommerce on their respective heads. No one could have predicted the outcomes.

Shopify’s Future of Ecommerce report uncovered people changed their shopping habits in many ways, including how often they visit stores and where they go to find new products.

Customer spending on luxury items took a dip, but the athleisure market saw massive growth.

The COVID-19 dashboard from Glimpse, a tool that tracks how topics trend across the internet, also uncovered spikes in demand for products like blue light glasses, resistance bands, nail kits, green screens, and bread makers.

The dips? Eyeliner, backpack, perfume, bowtie, heels, alarm clock, and belt.

And some of the seasonal items mentioned earlier, like school supplies, saw a smaller peak than before the pandemic as many schools had to operate remotely. But search volume for another seasonal product, inflatable pools, skyrocketed as people couldn’t take their usual beach holidays:

Google Trends graph showing search volume for inflatable pools

Google Trends graph for the term ‘inflatable pool’

You can’t predict a global pandemic and similar world events, but you can commit to understanding how they impact your customers.

From tweaking your product range to offering alternative ways to shop, like buy online, pick-up in store, you can adapt quickly and stay resilient.

Demand forecasting examples

Here’s how brick-and-mortar demand forecasting might look like with hypothetical examples.

1. Homeware store and the post-pandemic demand

A homeware store has been in business for five years and sees a steady, recurring pattern in demand for their products, growing slightly every year. Every spring and summer, there’s a peak in demand for garden furniture, and candles, blankets, and rugs sell out every winter.

There’s a new homeware store within the 10-mile radius, which diluted some of the seasonal product sales over the recent two years. Instead of growing as usual, sales for these products stagnated.

However, because of the spike in need for home office furniture due to the pandemic, the demand for standing desks, quality office chairs, and good lighting spiked. The older store adapted quickly with increased inventory and more buying options, like buy online, pick-up in store, and buy in-store, ship to home.

This store used competitor data to predict demand for their seasonal products and economic data to predict the longevity of remote work in this area. Thanks to the adapted marketing strategies and stocking the right products at the right time, this store’s growth returned to its pre-pandemic trajectory.

2. Sports apparel store and fluctuating trends

A sportswear store has been around for over a decade and stocks well-known sports apparel brands. Its sales are consistent and predictable.

However, in mid-2019, its sales declined while the foot traffic stayed almost the same. The owners couldn’t pinpoint the cause. After a while, they brought their store associates together to learn about their experiences on the floor.

They found that several gyms and training facilities started hosting new activities, including kickboxing, basketball, volleyball, and tennis, and their store’s inventory didn’t offer a wide enough range of products. POS data revealed that for products they did offer, demand spiked but stock was low and they sold out quickly.

To fix this, the team went into research mode to learn all sports activities available in the areas surrounding their store. They used official city data, social media activities, and customer surveys to understand what’s growing and diminishing in popularity.

As a result, they refreshed their product range for all sports they focused on. They also revamped their store layout and their promotions, both in-store and online. This new approach brought their sales numbers above the levels before mid-2019.

Forecasting demand for your store

By forecasting demand, you’ll do right by your customers, staff, and partners. It will allow you to think on your feet and adapt to opportunities and challenges with minimal risk.

With Shopify POS, you can access real-time and historical sales data for both your online store and each of your store locations, equipping you and your teams with the quantitative information you need to make informed forecasts.

The key to a successful demand forecast is using your sales data, economic indicators, expert knowledge, and your employees’ collective experience to (almost) read your customers’ minds and give them what they need.

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Demand forecasting FAQ

What is demand forecasting method?

Demand forecasting is the process of estimating the future demand for a product or service. It helps companies anticipate changes in the marketplace and plan accordingly. There are several different methods used to forecast demand, including time-series analysis, contextual analysis, econometric modeling, and machine learning. Each method has its own strengths and weaknesses, so companies should consider which approach best suits their needs.

What are the two types of demand forecasting?

The two types of demand forecasting are qualitative and quantitative. Qualitative forecasting relies on non-numerical data such as surveys, interviews, and expert opinions. Quantitative forecasting uses numerical data such as historical sales data, economic indicators, and consumer trends.