Retail Demand Forecasting: How to Maximize Sales and Minimize Risks

This article is for educational purposes and does not constitute legal, financial, or tax advice. For specific advice applicable to your business, please contact a professional.

The ability to predict what customers want and are willing to buy at specific times throughout the year will make or break your retail business, but it’s also a bit like predicting the ever-changing weather. Many factors are at play, and market conditions are constantly changing. If you overestimate demand, you may end up with excess inventory and tie up valuable resources. On the flip side, underestimating demand could result in lost sales and disappointed customers — imagine hosting a pop-up lemonade stand on the hottest day of the year and running out of drinks just as the line starts to wrap around the block! The key to finding the sweet spot is accurate demand forecasting.

What is demand forecasting?

Demand forecasting is the process of estimating upcoming sales and predicting the specific products that customers will buy. It involves analyzing historical sales data, market trends, and other relevant factors to understand the likely demand for specific products or services during a particular period.

Demand forecasting has several benefits that cut across inventory management, marketing, staffing, and finance. It helps you determine the right amount of stock to have on hand, reducing the risk of overstocking or stockouts. You can tailor marketing and promotional activities based on forecasted demand, ensuring targeted efforts to boost sales. By understanding peak periods and slower seasons, you can plan your workforce more efficiently to align with varying levels of demand, ensuring that there are enough employees during busy times and avoiding overstaffing during slower periods.

If done well, demand forecasting can help you accurately manage your order quantities and avoid unnecessary discounts.

Demand forecasting methods to use

The method of demand forecasting you choose depends on your business goals and the historical data you have access to. These methods are often categorized as either quantitative or qualitative.

Quantitative demand forecasting

Quantitative methods rely on data to identify patterns, trends, and relationships that can be used to make sales predictions. Naturally, quantitative methods work best for businesses that already have historical data, usually of up to two years or more. The goal is to estimate future demand based on past performance.

While there are many quantitative demand forecasting methods you can use, time series analysis (also known as trend projection or trend analysis) is often the easiest starting point.

Time series analysis involves looking at your business’s past performance and using that knowledge to make informed decisions for the future.

For example, consider a small clothing store that tracks daily sales for different types of clothing items over a couple of years. This data is like a time series, showing how the store’s sales change over days, weeks, and months. By looking at the sales data, you may notice patterns, such as the types of clothing that sell more during specific seasons or events. You may also see if there’s a general direction in which sales are moving over the years. Is the store selling more clothes yearly, or is it about the same? This gives insights into trends in the business. You can also recognize if there are regular changes that happen around the same time each year. In the clothing store, this could be a consistent boost in winter jacket sales during the colder months, showing seasonality.

Accurately predicting demand using quantitative methods requires collecting reliable sales data for your business over time. While that might seem daunting, you can easily do this with the right tools. For example, if you use a Square POS system, you can automatically collect and track important retail metrics and performance indicators that are crucial for demand forecasting.

Here are some of the metrics you can track using Square reporting and analytics features:

  • Historical sales data: You can access actual sales data over a specific period, broken down by time intervals (e.g., daily, weekly, monthly). * Historical sales data serves as the foundation for various quantitative forecasting methods, helping identify patterns, trends, and seasonality.
  • Sales per category: This helps you understand the total revenue generated for specific product categories, providing insights into which categories are performing well and which may require adjustments in pricing, marketing, or inventory management.
  • Sell-through rates: This helps you track the percentage of products sold compared to the initial stock levels. Higher sell-through rates generally indicate efficient inventory management, while lower rates may suggest overstocking or slower sales.
  • Inventory turnover: This is the number of times inventory is sold and replaced within a specific period. Inventory turnover reflects how quickly products are moving off the shelves. A high turnover rate suggests efficient inventory management.
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Qualitative demand forecasting

This method relies on your best judgment to predict future demand. It works best for new businesses with little or no historical data or when available data is unreliable. Here are some qualitative demand forecasting methods to consider.

  • Market research involves conducting surveys, focus groups, and interviews to gather opinions and preferences from potential customers. Square POS includes a customer feedback feature that allows you to collect private feedback that may be useful for qualitative forecasting.

  • Scenario planning: This involves developing different scenarios or stories about situations that could occur in the future and assessing their impact on demand. This technique can help you prepare for a range of possible outcomes.

  • Expert opinions: Industry experts, experienced professionals, or individuals with deep knowledge of your market can provide qualitative insights and predictions based on their understanding of the industry and its dynamics.

  • Delphi method: This involves collecting opinions from a group of experts anonymously, collating the responses, and then seeking further input based on the collected feedback. This process continues until a consensus is reached. The Delphi method is particularly valuable when dealing with uncertainties, complex problems, or future-oriented decision-making.

Combining quantitative and qualitative approaches is often best for more robust and accurate demand forecasts.

How to forecast demand: Important considerations for maximizing sales and minimizing risk

1. Define your objective and scope.
Why are you performing a demand forecast? What would you like to achieve? Clearly outline the objectives of the demand forecasting process. Understand whether the goal is short-term or long-term forecasting, and define the scope in terms of product categories, markets, or specific timeframes.

2. Assess data availability.
Evaluate the availability and quality of historical data. Some methods, like quantitative approaches, rely heavily on historical data, so assessing the completeness and reliability of the available information is crucial. Using automated data collection tools like Square POS ensures you have accurate records about your business.

3. Consider product characteristics.
Examine the specific characteristics of the product, such as whether it’s a seasonal item, a staple, or subject to sudden changes in consumer preferences. The product’s demand pattern influences the choice of forecasting method.

4. Consider market dynamics.
Understand the market environment, including factors like competition, technological changes, economic conditions, and regulatory influences. External market dynamics can impact demand and should be considered in the forecasting approach.

5. Balance quantitative and qualitative approaches.
Depending on the situation, you may want to combine quantitative and qualitative methods. Some scenarios may benefit from integrating expert opinions, market research, or subjective insights alongside numerical data.
For example, consider a consumer electronics store that wants to forecast demand for a new smartphone. External factors significantly influence the demand for new smartphones, and these factors are often difficult to quantify using purely historical data or quantitative methods. So, in addition to studying its historical sales data, the store may seek input from industry experts, technology analysts, and product reviewers who can provide insights into the anticipated market reception. The store may also analyze social media trends and sentiment analysis related to discussions about the new phone.

6. Conduct continuous monitoring and adjustment.
Demand forecasting is an ongoing process. Regularly monitor the forecasting performance and be ready to adjust methods as market conditions, product characteristics, or other factors evolve.

Effective retail demand forecasting can help your business reduce excess inventory costs, improve cash flow, boost customer satisfaction, and enhance overall operational efficiency. The key is to choose the right method by carefully considering your objectives, product characteristics, and market conditions.