عنوان

Effective CRM using predictive analytics /

(Number (ISBN

1119011558 (cloth)

(Number (ISBN

1119011566 (Adobe PDF)

(Number (ISBN

1119011574 (ePub)

(Number (ISBN

1119011582

(Number (ISBN

9781119011552 (cloth)

(Number (ISBN

9781119011569 (Adobe PDF)

(Number (ISBN

9781119011576 (ePub)

(Number (ISBN

9781119011583

Erroneous ISBN

9781119011552 (cloth)

Number

dltt

Title Proper

Effective CRM using predictive analytics /

General Material Designation

[Book]

First Statement of Responsibility

Antonios Chorianopoulos

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index

Text of Note

An overview of data mining: The applications, the methodology, the algorithms, and the data -- The applications -- The methodology -- The algorithms -- Supervised models -- Classification models -- Estimation (regression) models -- Feature selection (field screening) -- Unsupervised models -- Cluster models -- Association (affinity) and sequence models -- Dimensionality reduction models -- Record screening models -- The data -- The mining datamart -- The required data per industry -- The customer "signature": from the mining datamart to the enriched, marketing reference table -- Summary -- The Methodology -- Classification modeling methodology -- An overview of the methodology for classification modeling -- Business understanding and design of the process -- Definition of the business objective -- Definition of the mining approach and of the data model -- Design of the modeling process -- Defining the modeling population -- Determining the modeling (analysis) level -- Definition of the target event and population -- Deciding on time frames -- Data understanding, preparation, and enrichment -- Investigation of data sources -- Selecting the data sources to be used -- Data integration and aggregation -- Data exploration, validation, and cleaning -- Data transformations and enrichment -- Applying a validation technique -- Split or Holdout validation -- Cross or n-fold validation -- Bootstrap validation -- Dealing with imbalanced and rare outcomes -- Balancing -- Applying class weights -- Classification modeling -- Trying different models and parameter settings -- Combining models -- Bagging -- Boosting -- Random Forests -- Model evaluation -- Thorough evaluation of the model accuracy -- Accuracy measures and confusion matrices -- Gains, Response, and Lift charts -- ROC curve -- Profit/ROI charts -- Evaluating a deployed model with test-control groups -- Model deployment -- Scoring customers to roll the marketing campaign -- Building propensity segments -- Designing a deployment procedure and disseminating the results -- Using classification models in direct marketing campaigns -- Acquisition modeling -- Pilot campaign -- Profiling of high-value customers -- Cross-selling modeling -- Pilot campaign -- Product uptake -- Profiling of owners -- Offer optimization with next best product campaigns -- Deep-selling modeling -- Pilot campaign -- Usage increase -- Profiling of customers with heavy product usage -- Up-selling modeling -- Pilot campaign -- Product upgrade -- Profiling of "premium" product owners -- Voluntary churn modeling -- Summary of what we've learned so far: it's not about the tool or the modeling algorithm.

Text of Note

It's about the methodology and the design of the process -- Behavioral segmentation methodology -- An introduction to customer segmentation -- An overview of the behavioral segmentation methodology -- Business understanding and design of the segmentation process -- Definition of the business objective -- Design of the modeling process -- Selecting the segmentation population -- Selection of the appropriate segmentation criteria -- Determining the segmentation level -- Selecting the observation window -- Data understanding, preparation, and enrichment -- Investigation of data sources -- Selecting the data to be used -- Data integration and aggregation -- Data exploration, validation, and cleaning -- Data transformations and enrichment -- Input set reduction -- Identification of the segments with cluster modeling -- Evaluation and profiling of the revealed segments -- "Technical" evaluation of the clustering solution -- Profiling of the revealed segments -- Using marketing research information to evaluate the clusters and enrich their profiles -- Selecting the optimal cluster solution and labeling the segments -- Deployment of the segmentation solution, design and delivery of differentiated strategies -- Building the customer scoring model for updating the segments -- Building a Decision Tree for scoring: fine-tuning the segments -- Distribution of the segmentation information -- Design and delivery of differentiated strategies -- Summary -- The Algorithms -- Classification algorithms -- Data mining algorithms for classification -- An overview of Decision Trees -- The main steps of Decision Tree algorithms -- Handling of predictors by Decision Tree models -- Using terminating criteria to prevent trivial tree growing -- Tree pruning -- CART, C5.0/C4.5, and CHATD and their attribute selection measures -- The Gini index used by CART -- The Information Gain Ratio index used by C5.0/C4.5 -- The chi-square test used by CHAID -- Bayesian networks -- Naïve Bayesian networks -- Bayesian belief networks -- Support vector machines -- Linearly separable data -- Linearly inseparable data -- Summary -- Segmentation algorithms -- Segmenting customers with data mining algorithms -- Principal components analysis -- How many components to extract? -- The eigenvalue (or latent root) criterion -- The percentage of variance criterion -- The scree test criterion -- The interpretability and business meaning of the components -- What is the meaning of each component? -- Moving along with the component scores -- Clustering algorithms -- Clustering with K-means -- Clustering with TwoStep -- Summary -- The Case Studies -- A voluntary churn propensity model for credit card holders -- The business objective -- The mining approach -- Designing the churn propensity model process -- Selecting the data sources and the predictors -- Modeling population and level of data -- Target population and churn definition -- Time periods and historical information required -- The data dictionary -- The data preparation procedure -- From cards to customers: aggregating card-level data -- Enriching customer data -- Defining the modeling population and the target field -- Derived fields: the final data dictionary -- The modeling procedure -- Applying a Split (Holdout) validation: splitting the modeling dataset for evaluation purposes -- Balancing the distribution of the target field -- Setting the role of the fields in the model -- Training the churn model -- Understanding and evaluating the models -- Model deployment: using churn propensities to target the retention campaign -- The voluntary churn model revisited using RapidMiner -- Loading the data and setting the roles of the attributes -- Applying a Split (Holdout) validation and adjusting the imbalance of the target field's distribution -- Developing a Naïve Bayes model for identifying potential churners -- Evaluating the performance of the model and deploying it to calculate churn propensities -- Developing the churn model with Data Mining for Excel -- Building the model using the Classify Wizard -- Selecting the classification algorithm and its parameters -- Applying a Split (Holdout) validation -- Browsing the Decision Tree model -- Validation of the model performance -- Model deployment -- Summary -- Value segmentation and cross-selling in retail -- The business background and objective -- An outline of the data preparation procedure -- The data dictionary -- The data preparation procedure -- Pivoting and aggregating transactional data at a customer level -- Enriching customer data and building the customer signature -- The data dictionary of the modeling file -- Value segmentation -- Grouping customers according to their value -- Value segments: exploration and marketing usage -- The recency, frequency, and monetary (RFM) analysis -- RFM basics -- The RFM cell segmentation procedure -- Setting up a cross-selling model -- The mining approach -- Designing the cross-selling model process -- The data and the predictors -- Modeling population and level of data -- Target population and definition of target attribute -- Time periods and historical information required -- The modeling procedure -- Preparing the test campaign and loading the campaign responses for modeling -- Applying a Split (Holdout) validation: splitting the modeling dataset for evaluation purposes -- Setting the roles of the attributes -- Training the cross-sell model -- Browsing the model results and assessing the predictive accuracy of the classifiers -- Deploying the model and preparing the cross-selling campaign list -- The retail case study using RapidMiner -- Value segmentation and RFM cells analysis -- Developing the cross-selling model -- Applying a Split (Holdout) validation -- Developing a Decision Tree model with Bagging -- Evaluating the performance of the model -- Deploying the model and scoring customers -- Building the cross-selling model with Data Mining for Excel -- Using the Classify Wizard to develop the model -- Selecting a classification algorithm and setting the parameters -- Applying a Split (Holdout) validation -- Browsing the Decision Tree model -- Validation of the model performance -- Model deployment -- Summary -- Segmentation application in telecommunications -- Mobile telephony: the business background and objective -- The segmentation procedure -- Selecting the segmentation population: the mobile telephony core segments -- Deciding the segmentation level -- Selecting the segmentation dimensions -- Time frames and historical information analyzed -- The data preparation procedure -- The data dictionary and the segmentation fields -- The modeling procedure -- Preparing data for clustering: combining fields into data components -- Identifying the segments with a cluster model -- Profiling and understanding the clusters -- Segmentation deployment -- Segmentation using RapidMiner and K-means cluster -- Clustering with the K-means algorithm -- Summary

0

0

Title

Effective CRM using predictive analytics

International Standard Book Number

9781119011552

Customer relations-- Management-- Data processing.

Data mining.

BUS-- 041000

BUS-- 042000

BUS-- 082000

BUS-- 085000

Number

Edition

Class number

Chorianopoulos, Antonios.

Ohio Library and Information Network.

Date of Transaction

20160603080004.1

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y