Data Mining Predictive
This document summarizes an application of data mining techniques on marketing and customer relationship management (CRM). It first defines key terms like data mining, methodology, and business intelligence. It then discusses how data mining can be used to identify good prospects, select communication channels, and choose appropriate messages for different customer groups. Specific applications mentioned include using data mining to choose advertising places, improve direct marketing campaigns, and learn about prospects from current customers. Descriptive techniques like clustering and association rule discovery are covered as well as predictive techniques like classification, estimation, and prediction. The document provides examples of classification techniques like decision trees, neural networks, and rule-based methods. It ends with discussing potential uses of data mining in the retail industry and previews
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Data Mining Predictive
- 1. Disusun oleh: Hendy Jati S. 112081054 Norman Akbar 112090116 Tiara I. Pratiwi 112090205
- 2. Defi- Damin Damin Appli- Pro- Study Soft- nitio & Metho- cation blem Case ware Busi- dology on Sol- n vable ness Mar- with Intelli- keting Damin gence &CRM
- 4. Sekumpulan Data Pola Informasi Data Mining
- 6. Damin & Business Intelligence
- 10. APPLICATION ON MARKETING & CRM Mengidentifikasi calon pelanggan yang baik (good prospect) Memilih saluran komunikasi untuk mecapai calon pelanggan Memilih pesan yang tepat untuk berbagai kelompok calon pelanggan
- 11. Data Mining to Choose the Right Place to Advertise Data Mining to Improve Direct Marketing Campaigns Using Current Customers to Learn About Prospects Data Mining for Customer Relationship Management
- 13. DESKRIPTIF CLUSTERING ASSOCIATION RULE DISCOVERY SEQUENTIAL PATTERN DISCOVERY PREDIKTIF CLASIFICATION ESTIMATION PREDICTION
- 14. Future Prediktif Value Unknown Value Data Variabel
- 15. CLASSIFICATION Periksa karakteristik Data Model Kelas Input ke kelas yg sudah terdefinisi
- 16. Input Classifier Output Teknik-teknik Model Training yang Data Set digunakan
- 17. Decision Tree Naive Bayes & Bayesian CLASSI- Neural Networks Belief FICATION networks Rule-based Method CLASSIFICATION TECHNIQUE
- 18. Decision Tree + -
- 19. Neural Networks (+) (-)
- 20. Rule based method Unknown Pola data Value Frekuensinya Besar Kelemahan: Suksesnya klasifikasi data, bergantung pada banyaknya data yang tersimpan
- 21. Naive Bayes & Bayesian Belief Network Minimasi Cost Mudah diimplementasi Minimasi kebutuhan data training
- 22. Estimation • Data Output • Nilai Input kontinu Proses pengolahan
- 23. Prediction Data Classification Future Value
- 27. Retail Industry 1st Place 2nd Place
- 28. • Founded by • Expansion George Dayton • Renamed as • Dayton Dry Target to Canada Goods Company Corporation 1902 2011 2000 1962 2005 • First Store, • Total 1357 in Roseville Store Minnesota History
- 31. Problem
- 35. Why KarmaCRM?
- 36. Symple contact management Manage your Team sales management Process Flexible Third party reporting integration Feature
- 38. System Requirements • Hanya memerlukan web browser seperti firefox, chrome, Opera • Juga dapat digunakan via mobile dengan dengan web browser yang ada di smartphone
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