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Predictive Analytics for Marketers

Predictive Analytics for Marketers

Using Data Mining for Business Advantage

Barry Leventhal

£19.99

Understand how to apply predictive analytics to better manage a company and its resources more effectively, with this revolutionary book for marketing professionals.

Available to pre-order from 3rd November 2017
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About the book

Predictive Analytics has revolutionised 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.


Table Of Contents

  • Section - ONE: How can Predictive Analytics Help Your Business?;
    • Chapter - 01: What is a Predictive Analytics Model?;
    • Chapter - 02: What Activities are Modelling vs. What Aren’t Modelling?;
    • Chapter - 03: Two Types of Model – Predictive and Descriptive – The Role of Each;
    • Chapter - 04: “All Models are Wrong, But Some are Useful”;
    • Chapter - 05: ‘Hard’ and ‘Soft’ Benefits of Predictive Analytics;
    • Chapter - 06: Where Can Models Add Value? – The Profitability See-Saw;
    • Chapter - 07: Generating Customer Knowledge;
    • Chapter - 08: “Competing on Analytics”;
    • Chapter - 09: Case Study Example – Cross-Sell Models for Financial Institutions;
  • Section - TWO: A Key Approach – The Data Mining Process;
    • Chapter - 10: What is Data Mining, Why is a Data Mining Process Necessary?;
    • Chapter - 11: The CRISP Approach to Data Mining;
    • Chapter - 12: Implications – Importance of Understanding Both the Business Objectives and Available Data;
    • Chapter - 13: Implications – Building and Deploying Models are Separate Activities;
    • Chapter - 14: Implications – Model Evaluation is Essential Prior to Deployment;
    • Chapter - 15: Implications – Using Training and Validation Samples;
    • Chapter - 16: Considering the Tasks in Each Phase in Data Mining;
    • Chapter - 17: Stakeholders in the Data Mining Process;
  • Section - THREE: The Data for Predictive Analytics;
    • Chapter - 18: Data Sources that can be Leveraged;
    • Chapter - 19: Harnessing Big Data for Predictive Analytics;
    • Chapter - 20: “Licking the Data into Shape”;
  • Section - FOUR: The Toolkit of Analytical Modelling Techniques;
    • Chapter - 21: Traditional Techniques, e.g. Regression, Decision Trees, Factor Analysis, Cluster Analysis;
    • Chapter - 22: Artificial Intelligence/Machine Learning Techniques, e.g. Neural Networks, Genetic Algorithms;
    • Chapter - 23: Case Study Example – Comparison of Techniques;
    • Chapter - 24: Combining Models Together;
    • Chapter - 25: Which is the Right Technique to Use?;
    • Chapter - 26: The Wealth of Software Solutions for Predictive Analytics;
    • Chapter - 27: Finding the Right Resources – Internal or External to Your Organisation?;
  • Section - FIVE: From Customers to Citizens – Applications in Different Sectors;
    • Chapter - 28: The Retail Sector – Segmenting Customers Vs. Baskets;
    • Chapter - 29: The Telco Sector – Segmenting Mobile Phone Subscribers;
    • Chapter - 30: The Financial Services Sector – Targeting and Segmenting Accounts;
    • Chapter - 31: The Public Sector – Analytics on Citizens;
  • Section - SIX: From People to Products – Using Predictive Analytics for Pricing and Markdown Management;
    • Chapter - 32: Different Pricing Problems and Approaches Used – Every Day Pricing, Pricing for New Products, Promotional Pricing, Markdown Pricing;
    • Chapter - 33: Case Study – Pricing for a Pharmaceuticals Retailer;
    • Chapter - 34: Case Study – Markdown Management for a Supermarket Chain;
    • Chapter - 35: Bundling Products Together – The Use of Association Analysis;
  • Section - SEVEN: Segmenting Your Customers;
    • Chapter - 36: Segmentation Principles;
    • Chapter - 37: The Use of Segmentation in Marketing;
    • Chapter - 38: Many Ways to Segment;
    • Chapter - 39: Importance of Asking the Right Questions to Understand Business Requirements;
    • Chapter - 40: Segmentation Approach 1 – Behavioural Segmentation (Telco Case Study);
    • Chapter - 41: Segmentation Approach 2 – Segmentation on Recency, Frequency, Monetary Value (RFM);
    • Chapter - 42: Segmentation Approach 3 – Survey-Based Segmentation – Financial Services Case Study;
    • Chapter - 43: Measuring Segment Stability;
  • Section - EIGHT: Predicting the Future Behaviour of Your Customers;
    • Chapter - 44: Example Applications for Managing the Customer Journey – Recruitment, Cross-Sales, Churn Prediction, Win-Back;
    • Chapter - 45: Building Predictive Models – The Model Development Timeline;
    • Chapter - 46: Evaluating Models Using Lift and Gains Charts;
    • Chapter - 47: Case Study Example – Cross-Sales Model in Financial Services;
    • Chapter - 48: Allowing for Natural Uplift;
    • Chapter - 49: Different Types of Software Solutions for Predictive Modelling;
  • Section - NINE: How Long Will Each Customer Stay with You?;
    • Chapter - 50: Survival Analysis Concepts;
    • Chapter - 51: Descriptive Survival Analysis for Insurance Customers – Case Study Example;
    • Chapter - 52: Predictive Survival Models for Mobile Phone Customers – Case Study Example;
  • Section - TEN: Using Network Analysis to Identify Your Key Influencers;
    • Chapter - 53: Applying Network Analysis to Social Media – e.g. Facebook (Contributed Article);
    • Chapter - 54: Use of Network Analysis on Customer Data (Contributed Article);
  • Section - ELEVEN: Testing and Evaluating the Benefits of Predictive Analytics;
    • Chapter - 55: Carrying Out Live Tests – The Use of Test and Control Groups;
    • Chapter - 56: Testing Multiple Criteria Using Advanced Experimental Design;
  • Section - TWELVE: Visualising the Behaviour of Your Customer Base;
    • Chapter - 57: The Objectives of Data Visualisation;
    • Chapter - 58: Data Visualisation Guidelines – Good and Bad Practices;
    • Chapter - 59: Using Data Visualisation in Excel – What can be Done and the Limitations;
    • Chapter - 60: Advanced Data Visualisation Techniques;
  • Section - THIRTEEN: Final Tips and Conclusions



Book Details

  • EAN: 9780749479930
  • Edition: 1
  • Published: 3rd February 2018
  • Paperback
  • Format: 234x156
  • 272 pages

About the Author

Dr Barry Leventhal is a leading UK authority on geodemographics and marketing analytics expert. He chairs the Census and Geodemographics Group (CGG), which is an advisory board of The Market Research Society (MRS) and is a leading voice in the UK information industry.


Barry Leventhal


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