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Data mining techniques [[electronic resource] ] : for marketing, sales, and customer relationship management / / Gordon S. Linoff, Michael J.A. Berry
| Data mining techniques [[electronic resource] ] : for marketing, sales, and customer relationship management / / Gordon S. Linoff, Michael J.A. Berry |
| Autore | Linoff Gordon S |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Indianapolis, Ind., : Wiley Pub., Inc., 2011 |
| Descrizione fisica | 1 online resource (889 p.) |
| Disciplina | 658.802 |
| Altri autori (Persone) | BerryMicahel J. A |
| Soggetto topico |
Data mining
Marketing - Data processing Business - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-68619-7
9786613663139 1-118-08745-3 1-118-08750-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Data Mining Techniques; Contents; Introduction; Chapter 1 What Is Data Mining and Why Do It?; What Is Data Mining?; Data Mining Is a Business Process; Large Amounts of Data; Meaningful Patterns and Rules; Data Mining and Customer Relationship Management; Why Now?; Data Is Being Produced; Data Is Being Warehoused; Computing Power Is Affordable; Interest in Customer Relationship Management Is Strong; Every Business Is a Service Business; Information Is a Product; Commercial Data Mining Software Products Have Become Available; Skills for the Data Miner; The Virtuous Cycle of Data Mining
A Case Study in Business Data Mining Identifying B of A's Business Challenge; Applying Data Mining; Acting on the Results; Measuring the Effects of Data Mining; Steps of the Virtuous Cycle; Identify Business Opportunities; Transform Data into Information; Act on the Information; Measure the Results; Data Mining in the Context of the Virtuous Cycle; Lessons Learned; Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management; Two Customer Lifecycles; The Customer's Lifecycle; The Customer Lifecycle; Subscription Relationships versus Event-Based Relationships Event-Based Relationships Subscription-Based Relationships; Organize Business Processes Around the Customer Lifecycle; Customer Acquisition; Who Are the Prospects?; When Is a Customer Acquired?; What Is the Role of Data Mining?; Customer Activation; Customer Relationship Management; Winback; Data Mining Applications for Customer Acquisition; Identifying Good Prospects; Choosing a Communication Channel; Picking Appropriate Messages; A Data Mining Example: Choosing the Right Place to Advertise; Who Fits the Profile?; Measuring Fitness for Groups of Readers Data Mining to Improve Direct Marketing Campaigns Response Modeling; Optimizing Response for a Fixed Budget; Optimizing Campaign Profitability; Reaching the People Most Influenced by the Message; Using Current Customers to Learn About Prospects; Start Tracking Customers Before They Become "Customers"; Gather Information from New Customers; Acquisition-Time Variables Can Predict Future Outcomes; Data Mining Applications for Customer Relationship Management; Matching Campaigns to Customers; Reducing Exposure to Credit Risk; Predicting Who Will Default; Improving Collections Determining Customer Value Cross-selling, Up-selling, and Making Recommendations; Finding the Right Time for an Offer; Making Recommendations; Retention; Recognizing Attrition; Why Attrition Matters; Different Kinds of Attrition; Different Kinds of Attrition Model; Predicting Who Will Leave; Predicting How Long Customers Will Stay; Beyond the Customer Lifecycle; Lessons Learned; Chapter 3 The Data Mining Process; What Can Go Wrong?; Learning Things That Aren't True; Patterns May Not Represent Any Underlying Rule; The Model Set May Not Reflect the Relevant Population Data May Be at the Wrong Level of Detail |
| Record Nr. | UNINA-9910464836703321 |
Linoff Gordon S
|
||
| Indianapolis, Ind., : Wiley Pub., Inc., 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining techniques [[electronic resource] ] : for marketing, sales, and customer relationship management / / Gordon S. Linoff, Michael J.A. Berry
| Data mining techniques [[electronic resource] ] : for marketing, sales, and customer relationship management / / Gordon S. Linoff, Michael J.A. Berry |
| Autore | Linoff Gordon S |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Indianapolis, Ind., : Wiley Pub., Inc., 2011 |
| Descrizione fisica | 1 online resource (889 p.) |
| Disciplina | 658.802 |
| Altri autori (Persone) | BerryMicahel J. A |
| Soggetto topico |
Data mining
Marketing - Data processing Business - Data processing |
| ISBN |
1-280-68619-7
9786613663139 1-118-08745-3 1-118-08750-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Data Mining Techniques; Contents; Introduction; Chapter 1 What Is Data Mining and Why Do It?; What Is Data Mining?; Data Mining Is a Business Process; Large Amounts of Data; Meaningful Patterns and Rules; Data Mining and Customer Relationship Management; Why Now?; Data Is Being Produced; Data Is Being Warehoused; Computing Power Is Affordable; Interest in Customer Relationship Management Is Strong; Every Business Is a Service Business; Information Is a Product; Commercial Data Mining Software Products Have Become Available; Skills for the Data Miner; The Virtuous Cycle of Data Mining
A Case Study in Business Data Mining Identifying B of A's Business Challenge; Applying Data Mining; Acting on the Results; Measuring the Effects of Data Mining; Steps of the Virtuous Cycle; Identify Business Opportunities; Transform Data into Information; Act on the Information; Measure the Results; Data Mining in the Context of the Virtuous Cycle; Lessons Learned; Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management; Two Customer Lifecycles; The Customer's Lifecycle; The Customer Lifecycle; Subscription Relationships versus Event-Based Relationships Event-Based Relationships Subscription-Based Relationships; Organize Business Processes Around the Customer Lifecycle; Customer Acquisition; Who Are the Prospects?; When Is a Customer Acquired?; What Is the Role of Data Mining?; Customer Activation; Customer Relationship Management; Winback; Data Mining Applications for Customer Acquisition; Identifying Good Prospects; Choosing a Communication Channel; Picking Appropriate Messages; A Data Mining Example: Choosing the Right Place to Advertise; Who Fits the Profile?; Measuring Fitness for Groups of Readers Data Mining to Improve Direct Marketing Campaigns Response Modeling; Optimizing Response for a Fixed Budget; Optimizing Campaign Profitability; Reaching the People Most Influenced by the Message; Using Current Customers to Learn About Prospects; Start Tracking Customers Before They Become "Customers"; Gather Information from New Customers; Acquisition-Time Variables Can Predict Future Outcomes; Data Mining Applications for Customer Relationship Management; Matching Campaigns to Customers; Reducing Exposure to Credit Risk; Predicting Who Will Default; Improving Collections Determining Customer Value Cross-selling, Up-selling, and Making Recommendations; Finding the Right Time for an Offer; Making Recommendations; Retention; Recognizing Attrition; Why Attrition Matters; Different Kinds of Attrition; Different Kinds of Attrition Model; Predicting Who Will Leave; Predicting How Long Customers Will Stay; Beyond the Customer Lifecycle; Lessons Learned; Chapter 3 The Data Mining Process; What Can Go Wrong?; Learning Things That Aren't True; Patterns May Not Represent Any Underlying Rule; The Model Set May Not Reflect the Relevant Population Data May Be at the Wrong Level of Detail |
| Record Nr. | UNINA-9910789062603321 |
|
Linoff Gordon S
|
||
| Indianapolis, Ind., : Wiley Pub., Inc., 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||