TRAINING GAINING CUSTOMER INSIGHT THROUGH DATA MINING
TRAINING GAINING CUSTOMER INSIGHT THROUGH DATA MINING
TRAINING GAINING CUSTOMER INSIGHT THROUGH DATA MINING
Training Customer Relationship Management
Pembicara / Fasilitator
Salman Taufik: gaining experienced as banking professional since 1990, mastering in risk modeling and data analysis. He has background in statistics and has worked with large data set for economic and business research using SAS and SPSS. Now he is consultant and trainer in risk management and data mining for business intelligent.
Emphasizing practical skills as well as providing theoretical knowledge, this hands-on course covers segmentation analysis and profiling in the context of business data mining. Topics include the data mining modeling techniques and application : classification and prediction model, clustering, association and sequence.
SESSION-1: INTRODUCTION
* The Customer Relationship Management Strategy
* Data Mining in the CRM Framework
* Customer Segmentation
* Direct Marketing Campaigns
* Market Basket and Sequence Analysis
Supervised Modeling
* Classification modeling: predicting Events, marketing application
* Screening models
* Prediction Model
Unsupervised Modeling Techniques
* Segmention with Clustering Techniques
* Dimensionality of Data Reduction Techniques
* Association or Affinity ModelingTechniques
* Sequence Modeling Techniques
* Record Screening Modeling Techniques
Machine Learning/Artificial Intelligence vs. Statistical Techniques
SESSION-2: CUSTOMER SEGMENTATION
Customer Segmentation
* An Introduction to Customer Segmentation
* Segmentation in Marketing
* Segmentation Tasks and Criteria
Segmentation Types in Consumer Markets
* Value-Based Segmentation
* Behavioral Segmentation
* Propensity-Based Segmentation
* Loyalty Segmentation
* Socio-demographic and Life-Stage Segmentation
* Needs/Attitudinal-Based Segmentation
Segmentation methods
* Behavioral Segmentation Methodology
* Value-Based Segmentation Methodology
Data mining for segmentations
* Principal Components Analysis
+ Why consider PCA (Principle Component Analysis)
+ How Many Components Are to Be Extracted?
+ What Is the Meaning of Each Component?
+ Does the Solution Account for All the Original Fields?
+ Proceeding to the Next Steps with the Component Scores
+ Recommended PCA Options
* Clustering Techniques
+ Data Considerations for Clustering Models
+ Clustering with K-means
+ Clustering with the TwoStep Algorithm
+ Clustering with Kohonen Network/Self-organizing Map (SOM)
* Examining and Evaluating the Cluster Solution
+ The Number of Clusters and the Size of Each Cluster
+ Cohesion of the Clusters
+ Separation of the Clusters
* Understanding the Clusters through Profiling
+ Profiling the Clusters
+ Additional Profiling Suggestions
* Selecting the Optimal Cluster Solution
* Cluster Profiling and Scoring with Supervised Models
* An Introduction to Decision Tree Models
+ The Advantages of Using Decision Trees for Classification Modeling
+ One Goal, Different Decision Tree Algorithms: C&RT, C5.0, and CHAID
Exercise Case:
Segmentation Application in banking
Segmentation Application in Telecommunication
SESSION-3: DIRECT MARKETING CAMPAIGNS
* Approaches for direct marketing
* How’s data mining help in marketing campaign
* Scoring in RFM analysis
* Approach-1: Budget optimization
* Concept of lift and gains chart
* Approach-2: Optimizing the campaign
* Measuring the P/L
* Approach-3:Customer optimization
* Find the best model: regression, neural network, decision tree
* Confusion matrix
* Churn/attrition model
* Preventing Customer attrition
* Decision tree analysis
SESSION-4: MARKET BASKET AND SEQUENCE ANALYSIS
The RFM Analysis
* The RFM Segmentation Procedure
* RFM: Benefits, Usage, and Limitations
* Grouping Customers According to the Products They Buy
Wajib diikuti oleh
Anyone who wants to learn how to find meaningful segments in their customer data, focusing on practical business solutions as well as statistical rigor; business analysts, managers, marketers, programmers, and others can benefit from this course
Karena kompleksnya pelatihan ini, maka dibutuhkan pendalaman yang lebih komprehensif melalui sebuah training.Dan menjadi sebuah kebutuhan akan training provider yang berpengalaman di bidangnya agar tidak membuat peserta menjadi cepat bosan dan jenuh dalam mendalami bidang teknik ini.
Jadwal Pelatihan Nisbi Indonesia 2024 :
Batch 1 : 03 – 04 Januari 2024 | 16 – 17 Januari 2024
Batch 2 : 06 – 07 Februari 2024 | 20 – 21 Februari 2024
Batch 3 : 05 – 06 Maret 2024 | 19 – 20 Maret 2024
Batch 4 : 03 – 04 April 2024 | 23 – 24 April 2024
Batch 5 : 07 – 08 Mei 2024 | 21 – 22 Mei 2024
Batch 6 : 05 – 06 Juni 2024 | 25 – 26 Juni 2024
Batch 7 : 09 – 10 Juli 2024 | 23 – 24 Juli 2024
Batch 8 : 06 – 07 Agustus 2024 | 20 – 21 Agustus 2024
Batch 9 : 04 – 05 September 2024 | 18 – 19 September 2024
Batch 10 : 08 – 09 Oktober 2024 | 22 – 23 Oktober 2024
Batch 11 : 06 – 07 November 2024 | 26 – 27 November 2024
Batch 12 : 04 – 05 Desember 2024 | 18 – 19 Desember 2024
TEMPAT PELAKSANAAN PELATIHAN
- REGULER TRAINING
- YOGYAKARTA, Hotel Dafam Malioboro
- JAKARTA, Hotel Amaris Tendean
- BANDUNG, Hotel Golden Flower
- SURABAYA
- BALI, Hotel Ibis Kuta
- LOMBOK – NTB
- ONLINE TRAINING VIA ZOOM
- IN HOUSE TRAINING
Note :
Waktu dan tempat pelaksanaan pelatihan bisa kami sesuaikan dengan kebutuhan peserta.
Investasi Pelatihan :
Investasi pelatihan selama tiga hari tersebut menyesuaikan dengan jumlah peserta (on call). *Please feel free to contact us.
Apabila perusahaan membutuhkan paket in house training, anggaran investasi pelatihan dapat menyesuaikan dengan anggaran perusahaan.
Fasilitas training membuat dan menggunakan rencana pengembangan diri murah :
- FREE Airport pickup service (Gratis Antar jemput Hotel/Bandara)
- FREE Akomodasi Peserta ke tempat pelatihan bagi peserta training terupdate
- Module / Handout training terupdate
- FREE Flashdisk
- Sertifikat training teknik komunikasi murah
- FREE Bag or bagpackers (Tas Training)
- Training Kit (Dokumentasi photo, Blocknote, ATK, etc)
- 2xCoffe Break & 1 Lunch, Dinner
- FREE Souvenir Exclusive
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