Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics

This book provides a comprehensive overview of the process of predicting demand for retailers, from data collection to evaluation and visualization of prediction results. It illustrates each step with code and implementation details and can be applied to most retail settings. It helps students and practitioners better master data-driven demand prediction in retail applications.

Format: Paperback / softback
Length: 155 pages
Publication date: 05 January 2023
Publisher: Springer Nature Switzerland AG

Introduction:
The demand for retail products is a complex and ever-changing phenomenon, driven by a multitude of factors such as consumer preferences, economic conditions, and seasonal trends. As a result, retailers need to be able to accurately predict future demand in order to optimize their inventory levels, staffing, and marketing strategies. This book provides a comprehensive overview of the process of predicting demand for retailers, from data collection to evaluation and visualization of prediction results.
Step 1: Data Collection:
The first step in predicting demand for retailers is to collect relevant data. This data can include historical sales data, customer demographics, and other relevant information that can help inform the prediction model. The data should be cleaned and pre-processed to ensure that it is suitable for predictive analytics. This may involve removing outliers, correcting data errors, and transforming the data into a format that is easier to analyze.
Step 2: Model Selection:
Once the data has been cleaned and pre-processed, the next step is to select a suitable prediction model. There are many different types of prediction models available, including regression analysis, time series analysis, and machine learning algorithms. The model selected should be based on the nature of the data, the goals of the prediction, and the available resources.
Step 3: Training and Validation:
Once the prediction model has been selected, it needs to be trained and validated. This involves training the model on the historical data and evaluating its performance on unseen data. The training process helps the model learn the patterns in the data and make accurate predictions. The validation process helps to ensure that the model is performing well on new data.
Step 4: Evaluation and Visualization:
Once the model has been trained and validated, the next step is to evaluate and visualize the prediction results. This involves analyzing the predictions and identifying any potential biases or errors. It also involves visualizing the predictions in a way format that is easy to understand and interpret.
Step 5: Implementation:
Once the evaluation and visualization of the prediction results have been completed, the next step is to implement the predictions in the retail setting. This involves integrating the prediction model into the retailer's inventory management system implementation, staffing, and marketing strategies. The implementation process should be carefully planned and executed to ensure that the predictions are accurate and effective.
Conclusion:
In conclusion, this book provides a comprehensive overview of the process of predicting demand for retailers, from data collection to evaluation and visualization of prediction results. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy. By following the steps, readers can gain a deeper understanding of the challenges and opportunities associated with predicting demand in retail applications and develop the skills necessary to succeed in this field.

Weight: 279g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030858575
Edition number: 1st ed. 2022