Retail Demand Forecasting | 3 mins read

Guide to Retail Demand Forecasting For Beginners

guide to retail demand forecasting for beginners
Jin Hyun

By Jin Hyun

For a business to be financially abundant, it must be strategic in its use of capital. When it comes to managing inventory, businesses need a clear plan, as the cost of holding goods for too long without the product demand to match will result in wasted capital.

Conversely, if a company does not have the stock to meet customer demand, it will lose out on potential sales. By utilizing retail demand forecasting strategies, businesses can effectively prevent instances of over or under-ordering inventory.

What is Demand Forecasting?

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Demand forecasting is an essential business resource management technique that estimates the future demand for goods and services for particular products over a defined time period. Companies can use different data sources depending on the demand forecasting model used, in an attempt to create the most accurate future outlook.

Demand forecasting should take into account-

  • Historical sales data
  • Consumer research
  • Seasonality
  • Potential political, economic, and social factors
  • Market research

Effective demand forecasting will provide a valuable roadmap for businesses to follow when making decisions about new product lines, staff scheduling, pricing strategies, and inventory management. These data insights will allow management to ensure that there is no wasted capital tied up in holding too much or too little stock.

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The growth potential of a company is heavily dependent on the organization's ability to utilize capital in the most optimal way, Therefore, by maximizing the profitability of a business through forecasting insight, executives will be able to invest the remaining resources toward expansion efforts.

Additional benefits of retail demand forecasting include-

  • Directs business planning, goal setting, and budgeting to create an optimal procurement strategy.
  • Optimizes inventory to reduce the cost of holding goods and increase inventory turnover rates.
  • Gives insight into future cash flow, improving budgeting for supplier payments and operational expenses.
  • Highlights areas of inefficiency within the supply chain and production process.
  • Informs resource planning, allowing businesses to alter labor schedules for busy or quiet periods.

Of the top 50 U.S retailers, it has been reported that 34% have poor forecasting accuracy. This significant statistic highlights the competitive advantage technique that exists for businesses that can produce reliable projections.

It also stresses the need for accurate data-driven predictions as a demand forecast deviation of just 1% in either direction (over or under projecting) could potentially cost a company up to millions of dollars. This is precisely why demand forecasting is essential for any business looking to maximize their growth potential.

3 Demand Forecasting Models

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There are three main methods of product demand forecasting - casual, qualitative, and time series.

Causal Demand Forecasting

This technique assumes that there is a cause-effect relationship between the variable to be forecast and other independent variables. The variables considered in this model are external regulations, price, and seasonality, along with an additional qualitative method to understand inventory requirements for the future.

The dependent variable can be affected by many different factors in this calculation, and the casual method takes this into consideration. Data used for causal methods include sales data, product features, surveys, and macroeconomic conditions.

These methods are recommended for retailers in volatile markets, multi-channel businesses, and data-driven retailers with many metrics. It may also be useful when forecasting by specific products or categories, alongside marketing campaigns and promotions.

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Qualitative Forecasting

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This forecasting technique is used when businesses have limited access to historical data, particularly in the case of new product launches or startups.

These techniques are often referred to as judgemental methods, which rely on the opinions of experts, such as sales, marketing, and supplier professionals. Qualitative forecasting can also involve non-quantifiable data and responses collected from focus groups, customers, or experienced employees.

The different types of qualitative demand forecasting methods include surveys, focus groups, expert opinions, market research, Delphi Methods, historical analogy, and panel consensus.

Time Series Model

The time series model relies on quantifiable information by using historical data to identify sales trends. These methods work best for short to medium-term forecasts (less than a year).

The raw data used in this model is ordered and assessed chronologically, with the assumption that trends, cycles, and seasonality fluctuations will repeat themselves. The most commonly used time series forecasting techniques include econometric modeling, decomposition, moving average, indicator approach, graphical methods, life cycle modeling, and seasonal adjustment.

The Effect of Seasonality on Forecasting

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It's essential that businesses consider which products are most affected by seasonality, compared to those that will naturally be more steady due to their function.

Forecasting for holiday decorations, or certain clothing lines, will be very different from forecasting personal cosmetics, for example. If seasonality isn't considered when assessing sales trends, the business will miss key information pertaining to their estimated transactions. This means that instead of holding more inventory leading up to peak seasons, the business would miss out on a valuable opportunity for increased revenue.

Forecasting software is an intelligent solution for businesses to streamline the complex process of predicting demand, ensuring the highest accuracy possible with a low risk of human error.

This demand planning software provides businesses with the tools to plan and manage their inventory, production, and labor by predicting the customer's long-term needs using predictive analysis based on historical data and other external factors.

The investment in demand planning software is an investment to save companies money, time, and energy while freeing up capital for business growth.

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