Includes bibliographical references and index.

Machine generated contents note: 01.How can predictive analytics help your business?
Introduction
What is predictive analytics?
The analytical model
`AH models are wrong, but some are useful'
Two types of model
predictive and descriptive
The profitability seesaw
Applying predictive analytics to e-mail marketing
Making a difference
eight examples of useful models
Generating customer knowledge
Competing on analytics
Data protection and privacy issues
Conclusion
Notes
02.Using data mining to build predictive models
What is data mining?
Who are the stakeholders?
The data-mining process
Involvement of the stakeholders
The relationship between data mining, data science and statistics
03.Managing the data for predictive analytics
The roles of data
The useful data for predictive analytics
Data sources that can be leveraged
Having the right data
Contents note continued: Types of data
structured and unstructured
Data quality checks
the data audit
Data preparation
04.The analytical modelling toolkit
Types of techniques
Widely used predictive models
Widely used descriptive methods
The Bayesian approach
Which is the right technique to use?
Combining models together
05.Software solutions for predictive analytics
The architecture required for data mining
Software for analytical modelling
Communicating models between development and deployment
Model management
Scalable analytics in the Cloud
06.Predicting customer behaviour using analytical models
Overview
building and deploying predictive models
Defining the business requirements
Framing the business problem
The timelines for model development and deployment
The sample size required
Contents note continued: Preparing the analytic dataset
Building the model
Assessing model performance
Planning model deployment
From testing to implementation
07.Predicting lifetimes
from customers to machines
Importance of the customer lifecycle
Survival analysis applications
Key concepts of this technique
Describing customer lifetimes
Predicting survival times
Applications to customer management
Differences between survival and churn models
Applications to asset management
08.How to build a customer segmentation
Principles of segmentation
Potential business applications
Steps in developing and implementing customer segmentation
Some useful segmentation approaches
09.Accounts, baskets, citizens or businesses
applying predictive analytics in various sectors
Applications in retail banking
Analytics in mobile telecoms
Contents note continued: Customer analysis in retail
Use of advanced analytics in the public sector
Analysing businesses
10.From people to products
using predictive analytics in retail
An overview of retail applications
Price optimization
Markdown pricing
Forecasting base demand
11.How to benefit from social network analysis
Analysing social networks of customers
Business applications of SNA
Applying SNA to learn more about customers
Extending network analysis to social media
12.Testing the benefits of predictive models and other marketing effects
The purpose of testing
Golden rules
Planning a marketing test
Advanced experimental design
Constructing and running the test
Analysing test results
Testing in the online world
13.Top tips for gaining business value from predictive analytics
Contents note continued: Reprise of main messages
Final tips.

Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics has been used to successfully achieve a range of business purposes.