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Demand Forecasting in Retail using Machine Learning

Machine learning is changing the way retailers do business!

From groceries to clothes and to technology products, the possibilities in retail space are full of promises. Retailers have access to huge customer data. By applying machine learning to this data, it can forecast demand for certain products, provide tailored product recommendation, offer promotions and also identify fraudulent purchases.

Demand forecasting is all about how efficiently companies use the available data and derive actionable insights. It is a critical process of predicting what the demand for certain products will be in the future. In this process, it identifies what both current & future customers will want to buy and recommend retailers what they should actually focus on.

The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Inventory, production, storage, shipping, marketing – every facet of business is affected by accurate forecasting.

In this article, we will learn how machine learning approaches can help with demand forecasting in retail world.

Now what is Demand Forecasting in Machine learning?

With rising levels of product complexity and market volatility, traditional methods struggle. By applying machine learning algorithms, businesses are now able to treat very large datasets more effectively than ever before.

Machine learning techniques are used to predict the demand for a particular product/ service. This technique identifies hidden patterns in data, accelerates data processing speed, analyses more data, creates robust system and provides more accurate forecast as compared to traditional demand forecasting methods.

What are the different Data Sources used by Machine Learning Algorithm?

We have internal and external data sources which are both structures and unstructured. Few examples include sales transactions, purchase orders, customer reviews, marketing polls, IoTs, weather forecast, social media (likes, retweets, shares) and many more.

Source: Institute of Business Forecasting and Planning

What are the benefits of Demand Forecasting?

Improved Accuracy - There are many technologies to improve the accuracy of demand forecasting. Honestly, it might not be 100% precise, yet it can be precise enough to help you achieve your business goals. In fact, ML algorithms make better prediction over time.

Better customer relationship – Demand forecasting allows you to predict customer needs/ requirements for a particular period. This leads to increased customer satisfaction and Brand loyalty. While retailers grow their business physically and expand their online offerings, shoppers are given a customized experience on a micro level.

“Great customer experience will come from blending technology with a more personalized touch” – says Karen Katz CEO of Neiman Marcus Group.

Better supplier relationship – Machine Learning models help in stocking up supplies for future depending upon customer demands. This makes it easy to increase or decrease the number of suppliers.

Better Logistics – With better supply chain management, product is more likely to be in stock. At the same time, unsold goods will not occupy prime retail space.

Well, Machine learning Demand Forecasting is executed based on time frame.

  • Short-term forecasting – It is done for 6 months or less than 12 months. Purpose of short-term forecast is to provide uninterrupted supply of products/ services, financial maintenance, hiring requirement, sales target, performance evaluation.

  • Long-term forecasting – It usually done for a longer period of time say for more than a year. Purpose of long-term forecast includes long-term financial planning, business expansion and annual strategic planning.

Now that we have a better understanding of Machine Learning Demand Forecasting, let's get to know ML models used in retail industry and how they work.

In reality, historical data collected from internal and external sources often are not an ideal one. This data needs to be cleaned, checked for relevance, anomalies and restored before it can be used. Once the data is cleaned/ prepared it is given a structure and then it will be ready for visualization.

Next step is to choose a Machine learning model. There are no “one-size-fits-all” forecasting algorithms. Demand Forecasting feature uses multiple ML algorithms that take into account several factors such as business goals, data availability, quality of the data and other external factors.

In this article we will look at below mentioned ML approaches applied to retail industry

  • Time Series method

  • Random Forest

  • Feature Engineering

  • Linear Regression

Time Series Method

A time series is a sequence of numerical data points in successive order. Time series forecasting uses information regarding historical values and associated patterns to predict future activity. This relates to trend analysis, cyclical fluctuation analysis and issues of seasonality.

In retail section, ARIMA and SARIMA are the most applicable time series models. ARIMA (Autoregressive Integrated Moving Average) is suitable for univariant time series trend and without seasonal components. It makes accurate predictions for short-termed forecasts like demand, sales and production.

SARIMA (Seasonal Autoregressive Integrated Moving Average) is suitable for univariate time series trend and/or with seasonal components. It is an extension to ARIMA that supports the direct modelling of the seasonal components.

If data points are still volatile, then we can apply smoothing the series. By applying smoothing, we can have a better idea about the series and its components. It also makes the series more predictable.

Random Forest

Random forest is both a supervised learning algorithm and an ensemble algorithm, which creates multiple decision trees and averages their predictions. Single decision trees on their own can be very effective at learning non-linear relationships. However, they tend to over-fit very easily due to high variance. Random forest reduces this variance by averaging many trees.

RF is used in e-commerce to determine whether a customer will actually like the product or not. Using a certain pattern and following the product’s interest of a customer, you can suggest similar products to your customers.

Feature Engineering

Feature engineering is about creating new input features from your existing ones to improve model performance. These new input features captures additional information which is not easily apparent in the original feature set. As a result, algorithm gains more predictive power. There are different techniques to perform feature engineering. Practice and experience will teach you which technique is best suited to your domain/ problem.

Few basic techniques involve Imputation, Handling Outliners, Feature Split, Scaling, Binning, Log Transform among others. Use of domain knowledge and the creation of features make machine learning models predict more accurately.

Linear Regression

Linear regression is one of the most commonly used predictive modelling techniques. It is a supervised learning algorithm which is used to predict continuous values based on past values. Algorithm is trained with both input features and output labels. Regression helps in establishing a relationship among the variables by estimating how one variable affects the other. It is represented by an equation 𝑌 = 𝑎 + 𝑏𝑋 , where a is an intercept and b is the slope of the line. This equation can be used to predict the value of a target variable based on given predictor variable(s). For example, it can predict the food supply for a grocery store based on sales data - This helps in avoiding food wastage. It can also predict housing price based on real estate data.

For further understanding, you can read Regression topic in my article - An Introduction to Machine Learning.


Machine Learning is not limited to demand forecasting. It has the ability to address many more concerns in the retail industry and those retailers who are counting on it, will have an upper hand in such a competitive environment. The future potential of this technology depends on how well we take advantage of it.

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