Datamining for CRM

Knowledge of Descriptive Statistics.

Classes are made up of two components, theoretical and practical, in order to provide the student with a
acquisition of knowledge structured on theoretical foundations and validated with practical application tools.
The student will have access to the slides that the bibliography includes as well as the study material developed by the teacher.

Goals:

The search for relevant and meaningful information in large amounts of data is a determining factor in the era of “Big Data”.

The extraction of relevant information, the discovery of patterns and relationships to support decision-making in business management is assumed as a fundamental objective of this curricular unit.

Understand the CRISP-DM model in order to carry out an exploratory analysis of the data and then be able to select the most correct model, knowing how to perform a comparative analysis between the eligible models.

Application of classification and segmentation models in marketing campaigns.

Skills:

Know how to solve Customer Relationship Management problems through the search for relevant information in the different business communication channels.

Knowing how to use the CRISP-DM model in order to carry out an exploratory analysis of the data and then be able to select the most correct model, knowing how to perform a comparative analysis between the eligible models.

Know how to use classification and segmentation models in marketing campaigns.

1. Introduction to Data Mining techniques
    1.1. Basic concepts
    1.2. Data set
2. Learning Models and Algorithms
    2.1. Error measures
    2.2. Induction Trend
    2.3. About – adjustment and generalization
3. Data Preparation
    3.1. Exploratory data analysis
    3.2. Data preprocessing
4. Decision trees
5. Classification rules
6. Functional models
7. Artificial neural networks
8. Model evaluation and comparison
9. Classification model methodologies
    9.1. Using classification models in marketing campaigns
    9.2. Acquisition Models
    9.3. Sales models
10. Behavioral segmentation methodologies
    10.1. Customer segmentation
    10.2. Segmentation into clusters
    10.3. Segmentation applications in the financial sector
    10.4. Segmentation applications in telecommunications
    10.5. Retail segmentation applications

NAO

1. Data Analytics for Marketing and CRM (Data Analytics Applications) , Jie Cheng, Auerbach Publications; 1st edition (January  2021, ISBN-13 ‏ : ‎ 978-1498764247
2. Creating Value with Data Analytics in Marketing: Mastering Data Science,  Peter C. Verhoef et al., TAYLOR & FRANCIS LTD, ISBN: 9780367819798
3. Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights, Joanne Rodrigues ,1st edition, Addison-Wesley Professional,2021,ISBN-13: 978-0135258521.
4. Data Mining Techniques In Crm – Antonios Chorianopoulos, Wiley 2016 ISBN: 978-0470743973.    
5.  Introduction to Data Mining, 2nd Edition, – Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, , 2018 • Pearson, ISBN-13: 978-0-13-312890-1.
6. Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, 1st Edition by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel, Wiley;2019,ISBN-13 : ‎978-1119549840.
7. Business Analytics, Global Edition, 2/E ,James R. Evans, 2017 • Pearson , ISBN-13: 9781292095448.
8. Commercial Data Mining, Processing, Analysis and Modeling for Predictive Analytics Projects,1st Edition,, Morgan Kaufmann 2014 ,ISBN-13: 9780124166028.
9. Data Mining and Predictive Analytics, Daniel T. Larose, Chantal D. Larose, 2nd Edition, 2015 , ISBN: 978-1-118-11619-7.