Demand forecasting is the process of predicting future revenues and products that shoppers will buy using quantitative and qualitative data.
What is Demand Forecasting?
Demand forecasting allows you to estimate your store’s sales and revenue for a specific period in the future.
Historical sales data plays a significant role in demand forecasting, but you can also take into account other factors such as customer feedback, expert analyses, economic trends, and your sales team’s opinions and forecasts.
The accurate data you will use depends on the demand forecasting method you choose, but it should provide you with answers to questions such as: how many employees do I need in the store during a certain season? What amount of inventory should I have for each product? How often should I restock specific products? How can cash flow cover salaries in different seasons?
Why Does Demand Forecasting Matter?
Demand forecasting is critical for the future of your store as it helps you reduce risks and make the right decisions in many areas. Here are some key benefits of demand forecasting that you should consider:
Having the Right Products in Stock
Effective inventory management ensures that you have the right products at the right time precisely when your customers need them, but not in a quantity that leads to spoilage or becomes irrelevant.
Without demand forecasting, it’s easy to run out of stock (and end up with disappointed customers leaving empty-handed) or overstock products (wasting money on items you can’t sell).
By forecasting demand, you can prepare your supply chain and inventory for any anticipated peaks, as well as avoid excess inventory that affects cash flow and increases storage costs.
Assessing Risks of New Product Launches
Some demand forecasting methods – those that include market research and input from store employees and customers – can reveal demand for products you haven’t yet offered.
You should consider the costs of manufacturers, suppliers, storage, and marketing for a new product. This can be a huge gamble. For this reason, it’s worthwhile to use what you’ve learned through demand forecasting to mitigate the risks associated with providing a product you haven’t sold before.
Making Smart Hiring Decisions
Having the right number of employees to support customers during shopping is a win for everyone. But just like errors in stocking products, mistakes in hiring can be costly.
If you do not have enough employees during peak demand, you will force your customers to wait for service, which can affect the customer experience and lead them to shop elsewhere.
And if you have too many employees when visitor numbers drop, you will waste money on salaries.
You can use historical sales data to plan your staffing schedule, and feel free to adjust it throughout the day and reduce shifts if you realize you’ve scheduled more employees than necessary.
Types of Demand Forecasting
Demand forecasting and the data used varies based on your objectives. Here are some common methods of demand forecasting for use cases you’re likely to encounter:
External Market Demand Forecasting
External demand forecasting looks at the overall economy and how major trends can affect your store and objectives.
This includes economic conditions (such as inflation, GDP, and unemployment levels), your competitors, emerging trends, and changes in consumer behavior.
By understanding what is happening outside your store (and beyond your control), you will be better prepared to face challenges such as raw material shortages or supply chain issues and find solutions to them.
Internal Demand Forecasting
Internal demand forecasting explores how your internal resources relate to external demand forecasting.
This includes forecasts related to trade finance, supply chain management, cash flow, personnel, and other elements of your internal operations.
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For example, if external demand forecasting reveals that the demand for products at your store will double in the next two years, can your store adapt to that? Will you have the staff, space, and inventory needed to serve more customers?
Internal demand forecasting identifies any constraints and challenges you may face in your store.
Short-term Demand Forecasting
In this case, the short term refers to consumer demand for a period of up to one year.
Retailers can use short-term demand forecasting to prepare their inventory for seasonal demand peaks (such as summer, Black Friday, and winter holidays) and to act swiftly to changes in market behavior.
If your store uses a Just-In-Time (JIT) purchasing system, this type of forecasting helps you meet demand efficiently despite having low inventory.
Long-term Demand Forecasting
Long-term demand forecasting considers periods longer than one year and up to four years. This way, you can understand the annual and seasonal patterns of product demand.
This is useful for planning your inventory based on seasonal patterns, as well as your marketing, product launches, and store expansions. Similar to short-term demand forecasting, use historical sales data from your store to plan around major sales trends such as seasonality.
Passive Demand Forecasting
Passive demand forecasting uses historical sales data to predict future data. It does not require statistical analysis or consideration of broader economic trends.
The passive approach requires that your store (or the product or category you are studying) has prior sales data, so this approach is not applicable for forecasting demand for new products or initiatives.
Active Demand Forecasting
Active demand forecasting is more appropriate for new stores and those planning rapid growth. 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 frequently change their product offerings and introduce new collections, explore new marketing initiatives or experimental shopping experiences, and open more stores to expand their brand and reach new customers and markets.
Quantitative vs. Qualitative Demand Forecasting
Demand forecasting methods can be classified as either quantitative or qualitative.
Quantitative demand forecasting focuses on the hard data available for your store or industry. This can include sales, revenue, marketing analytics, and economic indicators.
Trend forecasting, the barometric method, and economic modeling are examples of quantitative demand 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 types of products that do not already have quantitative data to work with.
Delphi methods, customer surveys, and sales force composite method are examples of qualitative demand forecasting methods.
Demand Forecasting Methods You Can Use
Once you know what type of data you have access to (or plan to collect) using your point of sale software or online sales data, you can choose from the following demand forecasting methods:
Trend Forecasting Method
The trend forecasting method is used to predict the future in business. It works well for stores with a lot of historical sales data (two years or more).
In this method, past sales and historical revenue data are used to forecast future sales. It assumes that the factors that made past sales possible will continue to play the same role in the future, including customer needs and competitors in your area.
If your store is new, you can use the trend forecasting method based on sales data from stores in your area if it is available to you.
Barometric Method
Barometric forecasting predicts trends in general economic activity. These forecasts are based on economic indicators that include:
- Indicators
- Leading indicators, which may predict future events
- Lagging indicators, which are performance indicators reflecting the impact of past events
- Coincident indicators, which measure current events in real-time
An increase in the number of new subscribers to a loyalty program can be a leading indicator as it predicts more repeat purchases in the future. An increase in product returns is a lagging indicator as it shows the success (or lack thereof) of products sold in the past. Employee turnover in the store can be a coincident indicator as it reflects your actual performance as a store in real-time.
Economic Method
The economic method is more complex than other methods as it relies on statistical tools and mathematical formulas to understand the relationship between demand and influential factors.
It combines available sales data with external factors such as economic conditions to uncover relationships and future sales. For example, the economic method can explore whether consumer demand for a commodity like coffee depends on the city’s population.
A real-world example comes from The Australian Financial Review, which revealed that population growth is slowing, which could affect retail space and retail sales growth.
Delphi Method
The Delphi method for forecasting demand relies on a panel of experts to predict future sales. It is a systematic way to gather opinions and forecasts from independent experts through multiple rounds.
After recruiting experts and choosing a facilitator, the focus of the session is determined. What do you want to understand better? What problem do you hope to solve?
Then you focus on building questionnaires for multiple rounds of responses. The process is straightforward: collect responses in the first round, share them with the panel, and move on to the next round. Each round of the questionnaire builds upon the previous one.
The responses are anonymous, allowing each expert to share honest feedback. The Delphi method is not conducted face-to-face, so you can recruit experts from within your city or state or anywhere else relevant.
Market Research and Customer Surveys
Demand forecasting based on market research relies on data from customer surveys. This can be especially useful for new stores just starting out and looking to shape their product offerings and marketing strategies.
Data from customer surveys is also valuable for stores looking to expand into new locations, explore new products, or improve existing products. You can also explore store layouts and signage that could enhance sales based on what you learn.
Sales Force Aggregation Method
The sales force aggregation method takes into account the inputs from the people working in your store.
Your employees are on the front lines of your store. They spend most of their time with customers and have great insights into their questions, needs, behaviors, complaints, and overall experiences.
When your employees share their insights about customer behavior and feedback — and how it relates to future demand — you can aggregate all the responses and develop demand forecasts.
Factors Influencing Demand Forecasting
What does demand forecasting depend on? What makes a difference? Here are five key factors that affect your store’s forecasts:
Seasonality
Seasonality means that there are periods in the year when demand for a product is higher than at other times, and it is a pattern that recurs each year.
Seasonal factors include:
- Holidays (like the Fourth of July or Christmas)
- Weather changes and the activities they bring (like skiing in winter and surfing in summer)
- Events (wedding season or festivals)
Examples of seasonal products include sunscreen, snowboards, weighted blankets, patio furniture, school supplies, and candles.
Competition
If there are new stores meeting your customers’ needs, it’s likely that will affect demand forecasts.
Of course, things are more complex than that as other factors such as pricing strategies, promotions, the population in your location, lifestyles of people, and more also play a role, but it is important to always be aware of your competitors.
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This is not necessarily limited to physical stores like your shop and can include supermarkets, pop-up shops, and online-only stores.
Product Types
The type of products you sell is an important factor in predicting demand. Consider some of these points:
- Do your customers need to restock a product, or is it a one-time purchase?
- What is the average purchase rate from your customers?
- What is the average amount per purchase?
- Are there product combos that customers tend to buy together?
From the perspective of an individual customer, the demand for a refrigerator is vastly different from the demand for laundry detergent. Keep this in mind when considering other internal factors alongside the general economy and external influences.
Location
The location of your retail store plays a role in predicting demand.
How many people pass by that street daily? Is there anything nearby that can attract or deter 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 could affect future customer demand.
For example, the German dry goods chain Dm-Drogerie Markt chose retail locations in popular shopping areas in cities with populations over 10,000, including shopping centers, tourist spots, shopping streets, and areas close to markets and grocery stores.
Global Events
In 2020, the COVID-19 pandemic turned the retail and e-commerce sectors upside down. No one could predict the outcomes.
Shopify’s Future of E-commerce report revealed that people’s shopping habits changed in several ways, including how they visit stores and where they go to find new products.
Consumer spending on luxury items declined, but the sportswear market saw significant growth.
The COVID-19 dashboard from Glimpse, a tool that tracks how topics spread online, also indicated increased demand for products such as blue light glasses, resistance bands, nail care tools, green screens, and bread makers.
What about the seasonal products mentioned earlier, like school supplies? There was a smaller peak than before the pandemic, as many schools were forced to operate remotely. However, the volume of searches for another seasonal product, inflatable pools, surged significantly as people could not take their usual beach vacations:
A graph of the term “inflatable pool” in Google Trends
You cannot predict a global pandemic and similar world events, but you can commit to understanding how they affect your customers.
By adjusting your product offerings to provide alternative shopping methods, such as online purchasing with in-store pickup, you can quickly adapt and stay strong.
Examples of Demand Forecasting
Here’s what demand forecasting might look like in a retail store with fictional examples:
Home Goods Store and Post-Pandemic Demand
The home goods store had been in operation for five years and observed a consistent and recurring pattern in demand for its products, growing slightly each year. Every spring and summer, there is a peak in demand for garden furniture, and candles and blankets run out every winter.
There is a new home goods store within a 10-mile radius, which has diluted some sales of seasonal products over the past two years. Instead of growing as usual, sales of these products have stagnated.
However, due to the significant increase in demand for home office furniture because of the pandemic, demand for quality standing desks and office chairs with good lighting rose. The old store quickly adapted by increasing inventory and adding more purchasing options, such as buying online with in-store pickup and in-store shopping with home shipping.
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This store uses competitor data to forecast demand for seasonal products and economic data to predict the length of remote work in the area. Thanks to adaptive marketing strategies and timely stocking of the right products, this store’s growth has returned to the trajectory it was on before the pandemic.
Sportswear Store and Fluctuating Trends
The sportswear store has been around for over a decade and carries well-known brands of athletic clothing. Its sales are steady and predictable.
However, in mid-2019, its sales declined while store traffic remained approximately the same. The store owners could not pinpoint the reason. After a while, they gathered their employees in the store to understand their experiences on the ground.
They discovered that many gyms and training facilities had started hosting new activities, including kickboxing, basketball, volleyball, and tennis, and that the store’s inventory did not offer a wide range of products. Point-of-sale data revealed that the demand for the products being offered had increased, but the inventory was low and sold out quickly.
To solve this problem, the team went into research mode to find out all the sports activities available in the surrounding areas of the store. They used official city data, social media activities, and customer surveys to understand what was growing and waning in popularity.
As a result, they updated their product assortment for all the sports they focused on. They also revamped their store design and promotion, both in-store and online. They brought their sales to levels higher than those before mid-2019.
Demand Forecasting for Your Store
By forecasting demand, you will provide the right products to your customers, employees, and partners. It will allow you to think quickly 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 every physical store location, giving you and your team the quantitative information you need to make informed forecasts.
The key to successful demand forecasting is using your sales data, economic indicators, expert knowledge, and your employees’ experience to read your customers’ minds (almost) and offer them what they need. Start using Shopify POS.
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Frequently Asked Questions About Demand Forecasting
What is demand forecasting method?
Demand forecasting is the process of estimating future demand for a product or service. It helps companies anticipate market changes and plan accordingly. There are several different methods used for demand forecasting, including time series analysis, context analysis, economic modeling, and machine learning. Each method has its own strengths and weaknesses, so companies should consider which approach fits their needs.
What are the types of demand forecasting?
There are two types of demand forecasting: qualitative and quantitative. Qualitative demand forecasting relies on non-numeric data such as surveys, interviews, and expert opinions. Quantitative demand forecasting uses numerical data such as sales data
Source: https://www.shopify.com/retail/demand-forecasting
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