Published August 10, 2026 | Version 1
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Predictive Analytics Using IBM SPSS: A Business Consulting Approach

Authors/Creators

  • 1. Academic Staff

Description

Book Description

Predictive Analytics Using IBM SPSS: A Business Consulting Approach is a practical, business-oriented guide designed to help students, researchers, professionals, and aspiring data analysts transform organizational data into meaningful predictions and evidence-based decisions.

Unlike traditional statistics textbooks that emphasize formulas and isolated analytical procedures, this book adopts a business consulting approach. Readers learn predictive analytics by assuming the role of an analytics consultant responsible for solving realistic organizational problems. The learning journey begins with understanding business challenges and defining prediction objectives before progressing to data preparation, exploratory analysis, predictive modeling, model evaluation, interpretation, and managerial decision making.

Using IBM SPSS Statistics as the primary analytical platform, the book provides accessible, step-by-step applications of important predictive techniques, including Binary Logistic Regression and Multiple Linear Regression. The continuing MarketPulse Retail case demonstrates how customer and marketing data can be transformed into predicted probabilities, customer prioritization strategies, and actionable business intelligence. A comprehensive Horizon Hotel Group case study extends these skills to predicting guest satisfaction and identifying operational improvement priorities.

The book moves beyond conventional statistical analysis by introducing training and testing samples, out-of-sample validation, prediction error, Mean Absolute Error (MAE), future-case prediction, what-if scenario analysis, and managerial simulation. Practical exercises and advanced capstone consulting assignments encourage readers to apply analytical results to realistic management situations.

Ultimately, the book develops a complete professional mindset:

Business Problem → Data → Predictive Model → Prediction → Validation → Business Intelligence → Management Decision

Its central message is simple: predictive analytics is not merely about building models—it is about using data to make better decisions.

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Additional details

Dates

Available
2026-08-10

References

  • IBM Corporation. (2026). IBM SPSS Statistics 31 documentation. IBM.
  • Bruce, P. C., Stephens, M. L., Shmueli, G., Anandamurthy, M., & Patel, N. R. (2023). Machine learning for business analytics (2nd ed.). Wiley.
  • Field, A. (2024). Discovering statistics using IBM SPSS Statistics (6th ed.). SAGE Publications.
  • Hair, J. F., Page, M., & Brunsveld, N. (2023). Essentials of business research methods (5th ed.). Routledge.
  • Kelleher, J. D., Mac Namee, B., & D'Arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies (2nd ed.). MIT Press.
  • Kuhn, M., & Johnson, K. (2019). Feature engineering and selection: A practical approach for predictive models. CRC Press.