TRAINING GAINING CUSTOMER INSIGHT THROUGH DATA MINING

 

DESKRIPSI

Dalam dunia bisnis yang berbasis data, kemampuan menggali insight pelanggan dari data yang tersedia menjadi keunggulan kompetitif yang penting. Training Gaining Customer Insight Through Data Mining dirancang untuk membantu peserta memahami bagaimana teknik data mining dapat digunakan untuk mengidentifikasi pola perilaku pelanggan, preferensi, dan peluang peningkatan layanan. Dengan analisis tepat yang dipelajari dalam pelatihan analisis data marketing mining, perusahaan dapat merancang strategi pemasaran yang lebih terarah, personal, dan berdampak langsung terhadap kepuasan serta loyalitas pelanggan.

Pelatihan ini membahas mengenai customer riset data mining dan tidak tuntas jika dipelajari dalam hitungan jam. Oleh karena itu, diperlukan waktu tersendiri dan bimbingan yang profesional.

 

MATERI

  1. SESSION-1: INTRODUCTION

    • 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

  2. 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

  3. 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

  4. 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

 

TRAINING GAINING CUSTOMER INSIGHT THROUGH DATA MINING

 

SIAPA YANG DAPAT MENGIKUTI TRAINING INI?

  • Analis Data Pelanggan
  • Digital Marketer
  • Manajer CRM
  • Staf Business Intelligence
  • Manajer Pengembangan Produk

 

TRAINER PADA TRAINING INI

Instruktur yang berpengalaman dalam bidang strategi data mining akan mengisi pelatihan database customer.

 

JADWAL PELATIHAN 2026

  • BATCH 1 : 05-06 Januari 2026 | 19-20 Januari 2026
  • BATCH 2 : 02-03 Februari 2026 | 18-19 Februari 2026
  • BATCH 3 : 09-10 Maret 2026 | 25-26 Maret 2026
  • BATCH 4 : 06-07 April 2026 | 27 – 28 April 2026
  • BATCH 5 : 04-05 Mei 2026 | 18-19 Mei 2026
  • BATCH 6 : 08-09 Juni 2026 | 22-23 Juni 2026
  • BATCH 7 : 06-07 Juli 2026 | 20-21 Juli 2026
  • BATCH 8 : 03-04 Agustus 2026 | 19-20 Agustus 2026
  • BATCH 9 : 07-08 September 2026 | 21-22 September 2026
  • BATCH 10 : 05-06 Oktober 2026 | 19-20 Oktober 2026
  • BATCH 11 : 02-03 November 2026 | 16-17 November 2026
  • BATCH 12 : 07-08 Desember 2026 | 14-15 Desember 2026

Calon peserta dapat menyesuaikan jadwal tersebut sesuai dengan kebutuhan

Upgrade diri Anda dengan mengikuti pelatihan bersama kami, berkembang bersama NISBI Indonesia!

 

LOKASI

Pelatihan ini sudah pernah diadakan di Jakarta dan Yogyakarta. Kami juga bisa menyelenggarakan di kota lain, antara lain :

  • Bandung
  • Bali
  • Lombok
  • Makassar

 

INVESTASI

Investasi pelatihan 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.

 

BENEFIT

  • Module / Handout
  • FREE Flashdisk
  • FREE Bag or bagpack (Tas Training)
  • Training Kit (Dokumentasi photo, Blocknote, ATK, etc)
  • 2x Coffee Break & 1 Lunch
  • FREE Souvenir Exclusive

Post Views: 296