Building an Intelligent Digital Marketing Prediction System Using IBM SPSS; A Complete Hands-On Case Study
Year: 2026
Type: book
Abstract: Digital marketing operates in a data-intensive environment where organizations interact with customers through social media, search engines, websites, email, mobile applications, online advertising, and e-commerce platforms. Although these interactions generate extensive evidence about customer behavior, many organizations still rely on historical reports, isolated indicators, and managerial intuition when allocating marketing resources. This book demonstrates how digital marketing data can be transformed into an intelligent prediction system. Its central question is whether an organization can predict digital conversion from previous cross-channel behavior. A conversion may represent a purchase, subscription, registration, application, download, donation, or another action of organizational value. The analytical framework integrates structured indicators from social media, paid advertising, search, email, websites, mobile access, campaigns, customer profiles, and transaction history. Unstructured social media content must first be transformed into measurable variables, such as sentiment, topic, or complaint category, before entering the analysis. IBM SPSS is used for predictive modeling rather than raw data scraping or advanced natural-language processing. Binary logistic regression, decision trees, and artificial neural networks form the analytical core of the book. These models are compared according to predictive performance, interpretability, operational usefulness, and implementation risk. Evaluation extends beyond overall accuracy to sensitivity, specificity, precision, confusion matrices, ROC analysis, and the business consequences of incorrect predictions. The book emphasizes analytical reasoning rather than software commands. It progressively connects problem formulation, data preparation, model development, customer scoring, implementation, and monitoring. Chapter 6 integrates the process through a complete consulting case study focused on predicting digital conversion and prioritizing customers. Designed as a self-contained second volume in the series, the book serves advanced students, researchers, analysts, consultants, and managers. Privacy, consent, fairness, transparency, and human oversight are addressed throughout, ensuring that prediction supports responsible and defensible marketing decisions.
Author: Iriqat, Yousef Mohammad
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7203628824