Business Analytics, Data Analysis & Metrics Skills Training Delivering Strategic and organizational Growth

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Why Choose this Training Course?

This is a highly interactive workshop where case studies and hands-on data analytics are used to illustrate key learning points. A case study presentation will form part of the workshop allowing participants to apply concepts acquired during the workshop to a real-life scenario.

Course Objectives

This course is designed to equip professionals working in the fields of Finance, Marketing, Economics, Statistics, Mathematics, Computer Science, IT, Analytics, Marketing Research, or Commodity markets with the essential tools, techniques and skills to answer important business questions.

Career Benefits

The use of Business Analytics is widespread across all industries and functions, including Information Technology, Web/E-commerce, Healthcare, Law Enforcement, Banking and Insurance, Biotechnology, Human Resource Management. Some of the application areas include critical product analysis, target marketing, customer lifecycle management, customer service, social media behavior and link analysis, fraud detection, genetic research, inventory management, etc.

Learning Outcomes:

By the end of this course, participants will be able to:

  • Gain actionable insight into financial and business performance
  • Develop customer insight
  • Sell the right product to the right person at the right time
  • Build models to gain customer insight
  • Combine structured and unstructured data
  • Work efficiently with large data volumes
  • Explore data to find new patterns and relationships (Data Mining)
  • Predict the relationship between different variables (Predictive Modeling, Predictive Analytics)
  • Predict the probability of default and create customer Scorecards (Logistic Regression)
  • Understand a Problem in Business, explore and analyze the problem
  • Use tools like R (open source) and Excel to interpret data
  • Solve business problems using analytics (in R) in different fields

Course Content

Introduction and Data Analytics

  • Introduction to Analytics Overview: Analytics v/s Analysis
  • Data – Topic covered: Summarizing Data
  • Outlier Treatment
  • Categorization of data variables: Exploring credit card customer database to define variable types & categorizing them.

Module 1: Introducing Big Data Analytics

  • What big data is, and why it is so important
  • How different institutes are leveraging the advantage of big data analytics
  • Basic tools and concepts of big data
  • Types of big data: Customer data, transaction data, external data, social media data
  • Comparison and contrast of small data with big data
  • Challenges of big data

Module 2: The Art of Data Analytics

  • R programming as a tool for analytics
  • Some common analytical tasks
  • Using R databases and business intelligence systems
  • Data visualization and exploration
  • Clustering, segmentation and classification in big data
  • Simple regression models
  • Multiple regression
  • ROC curves
  • Logistic regression models
  • Risk models
  • High-dimensional regression models
  • Decision tree and neural network
  • Association rules for market basket analytics
  • Data export and output
  • Optimizing R code

Module 3: Predictive Analytics

Customer retention and acquisition management

  • Data sources for supporting acquisition
  • Direct marketing channel
  • Acquisition metrics
  • Profit and loss framework
  • Retention and profitability
  • Usage and retention
  • Retention strategies
  • Customer win-back
  • Industry examples

Customer depth ratio and lifetime value

  • RFM model
  • Past customer value
  • Lifetime value metrics
  • Customer equity
  • Probability model for customer base analysis
  • Clumping
  • Markov transition model
  • Popular customer selection strategy
  • Misclassification rate
  • Lift analysis

Usage management

  • Spend management
  • Balance build
  • Credit line management for financial services
  • Loyalty management

Cross-sell and up-sell

  • Benefits and challenges
  • Cross-sell process
  • Cross-sell model for multiproduct marketing
  • Recommender system
  • Response-revenue maximization

Market mix models

  • Optimization
  • Dynamic model
  • Price elasticity of demand
  • Advertising elasticity of demand
  • Building a comprehensive model

Default prediction

Module 4: Unstructured Data Analytics

  • Text mining
  • Sentiment analysis
  • Analytics of unstructured data from social media
  • Combining offline and online data
  • Digital analytics
  • Paid search advertising

Course Summary and Personal Take Out

  • Course Certificate
  • Handouts used during the course
  • New supportive material
  • Recommended reading
  • Links to our favorite videos
  • Photos of the day



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P O Box 27859 00100 Nairobi, Kenya Tel +254-20-2211362/4/5 or 2211382 Cell+254-712-636404

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