Traditional Methods or Machine Learning: Which is Ideal for Retail Demand Forecasting?
Retail Tech Insights | Thursday, February 01, 2024
This article examines the differences between traditional methods and machine learning in retail stores and provides guidance on which approach best suits different types of customers.
Fremont, CA: The technique of estimating the level of demand for your products over a given time frame using both historical and current data is known as demand forecasting. It assists in making the best supply and procurement choices for the company's clients.
Demand forecasting must be second nature to you as a retailer, regardless of how many SKUs you sell—one million or 1,000. It's more crucial to predict the demand for your items effectively for the following months the more products you offer, whether online or offline.
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Traditional Demand Forecasting Methods
Earlier demand forecasting was limited to specific stores, and it was sufficient to have one person tracking product movements and estimating needs.
However, with the proliferation of sales channels over the last ten years, including websites, apps, and numerous stores—often located in various countries—it is critical to approach forecasting with an omnichannel perspective.
Because of omnichannel, a vast amount of data—about customer behavior and product movement—is available that cannot be accessed by a small group of people using spreadsheets.
Two main components make up conventional demand forecasting techniques:
Quantitative Methods:
This method uses statistical and mathematical models to interpret the data and trends. These include regression analysis, econometric modeling, lifecycle modeling, time-series modeling, moving averages, linear approximation, exponential smoothing, and percentages over the previous year.
Qualitative Methods:
They are social and subjective techniques for gathering data and using the concepts they provide to address the issue. Market research, expert opinions, the Delphi Method, panel consensus, historical analogy, and focus groups are a few of these.
Why is machine learning a Better Approach to Demand Forecasting than Conventional Techniques?
Most conventional demand forecasting techniques are manual, depending on data collection and spreadsheet formula analysis.
However, manual forecasting is too laborious and prone to human mistakes when your retail data points to millions and the variables influencing a product's demand amount to dozens.
Furthermore, it is impossible to combine all of the data points and various analytical models into a single spreadsheet or graphic to provide a 360-degree view; as a result, some details are omitted, and separate interpretations are produced. This highlights the significance of ML in demand forecasting. ML is a better option because it is easy, convenient, and simple to use.
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