book

Effective CRM using Predictive Analytics

January 2016

Intermediate to advanced

392 pages

12h 6m

English

Read now

Unlock full access

1.1 The applications1.2 The methodology1.3 The algorithms1.4 The data1.5 Summary
2.1 An overview of the methodology for classification modeling2.2 Business understanding and design of the process2.3 Data understanding, preparation, and enrichment2.4 Classification modeling2.5 Model evaluation2.6 Model deployment2.7 Using classification models in direct marketing campaigns2.8 Acquisition modeling2.9 Cross-selling modeling2.10 Offer optimization with next best product campaigns2.11 Deep-selling modeling2.12 Up-selling modeling2.13 Voluntary churn modeling2.14 Summary of what we’ve learned so far: it’s not about the tool or the modeling algorithm. It’s about the methodology and the design of the process
3.1 An introduction to customer segmentation3.2 An overview of the behavioral segmentation methodology3.3 Business understanding and design of the segmentation process3.4 Data understanding, preparation, and enrichment3.5 Identification of the segments with cluster modeling3.6 Evaluation and profiling of the revealed segments3.7 Deployment of the segmentation solution, design and delivery of differentiated strategies3.8 Summary
4.1 Data mining algorithms for classification4.2 An overview of Decision Trees4.3 The main steps of Decision Tree algorithms4.4 CART, C5.0/C4.5, and CHAID and their attribute selection measures4.5 Bayesian networks4.6 Naïve Bayesian networks4.7 Bayesian belief networks4.8 Support vector machines4.9 Summary

5.1 Segmenting customers with data mining algorithms5.2 Principal components analysis5.3 Clustering algorithms5.4 Summary
6.1 The business objective6.2 The mining approach6.3 The data dictionary6.4 The data preparation procedure6.5 Derived fields: the final data dictionary6.6 The modeling procedure6.7 Understanding and evaluating the models6.8 Model deployment: using churn propensities to target the retention campaign6.9 The voluntary churn model revisited using RapidMiner6.10 Developing the churn model with Data Mining for Excel6.11 Summary
7.1 The business background and objective7.2 An outline of the data preparation procedure7.3 The data dictionary7.4 The data preparation procedure7.5 The data dictionary of the modeling file7.6 Value segmentation7.7 The recency, frequency, and monetary (RFM) analysis7.8 The RFM cell segmentation procedure7.9 Setting up a cross-selling model7.10 The mining approach7.11 The modeling procedure7.12 Browsing the model results and assessing the predictive accuracy of the classifiers7.13 Deploying the model and preparing the cross-selling campaign list7.14 The retail case study using RapidMiner7.15 Building the cross-selling model with Data Mining for Excel7.16 Summary
8.1 Mobile telephony: the business background and objective8.2 The segmentation procedure8.3 The data preparation procedure8.4 The data dictionary and the segmentation fields8.5 The modeling procedure8.6 Segmentation using RapidMiner and K-means cluster8.7 Summary

Bibliography

Anderson, Kristin. Customer relationship management. New York: McGraw-Hill, 2002.
Berry, Michael J. A., and Gordon Linof. Mastering data mining: the art and science of customer relationship management. New York: John Wiley & Sons, Inc., 1999.
Fernandez, George. Data mining using SAS applications. Boca Raton: Chapman & Hall/CRC, 2003.
Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. 3rd ed. Morgan Kaufmann series in data management systems. Amsterdam: Elsevier/Morgan Kaufmann, 2012.
Hughes, Arthur Middleton. Strategic database marketing. 4th ed. New York: McGraw-Hill, 2012.
IBM. IBM SPSS Modeler 16 algorithms guide. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316.
IBM. IBM SPSS Modeler 16 applications guide. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316.
IBM. IBM SPSS Modeler 16 modeling nodes. Armonk: IBM, 2014. http://www-01.ibm.com/support/docview.wss?uid=swg27038316.
IBM Redbooks. Mining your own business in banking using DB2 Intelligent Miner for Data (IBM Redbooks). Armonk: IBM, 2001. http://www.redbooks.ibm.com/.
IBM Redbooks. Mining your own business in retail using DB2 Intelligent Miner for Data. Armonk: IBM, 2001. http://www.redbooks.ibm.com/.
IBM Redbooks. Mining your own business in telecoms using DB2 Intelligent Miner for Data (IBM Redbooks). Armonk: IBM, 2001. http://www.redbooks.ibm.com/.
Larose, Daniel T. Discovering knowledge in data: an introduction ...

Mark Tossell

Konstantinos K. Tsiptsis, Antonios Chorianopoulos

Michele Chambers, Thomas W. Dinsmore

Cesar Brea

ISBN: 9781119011552Purchase book