Customer Analytics Training

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About The Course

Customer analytics is the process of identifying the behavior of customers based on their transactions and interaction through various channels. Customer analytics involves developing customer segments based on their behavior similarity, building RFM (Recency, Frequency, Monetary) models, identifying the life time value of the customer. Insights which come out of the customer segments and models built will help in building the right services to various customer segments and personalized services to high profile customers. Customer analytics also include identifying the potential customers who might churn out which can help in taking proactive actions to retain the customer.

Some of the areas where customer analytics are extensively used are:

Retail industry to understand customer purchasing behavior and how to use the purchasing pattern for cross and up sell

  • Financial industry to understand customer life time value, churn analysis etc. and provide personalized services
  • Telecom industry to understand the customer usage behavior and provide bundled packages and other incentives to increase the revenues and reduce customer churn

Additionally customer experience analytics includes identifying the behavior of the overall customer behavior and take insights in building the best products which caters to the needs of different customer segments.

Are you interested in providing solutions to increase the customer experience by understand the behavior of the customer based on the customer transaction data, are you passionate in building products by understanding customer preferences, is your overall objective is to increase the revenue and retain the valuable customers then customer analytics is the right area which you need to focus on.

We provide customer analytics training covering customer segmentation, web analytics, and customer churn management etc. using real time examples with the support of widely used industry tool R.

Topics You Will Learn…


The Rationale for Customer Analytics in New Product Development

  • Overview of Key Analytical Methods

Introduction to Software:

  • MeXL (Marketing Engineering for Excel)

Data for Need-based Segmentation:

  • Voice of the Customer / Customer Co-Creation
  • Data for Segmentation

Analytics for Need Based Segmentation:

  • Managing “Voice of the Customer”Data
  • The Cluster Analysis + Discriminant Analysis Approach
  • Advanced Issues in Segmentation
  • Rationale for Segment Targeting

Approaches to Choosing Target Segment/s:

  • The GE/McKinsey Portfolio Matrix
  • Segmentation using MeXL
  • Analytics for Perceptual Mapping and Product Positioning
  • Product Positioning

Mapping: MDS and Factor Analysis

  • Relevance of Mapping for Product Positioning
  • Preference Mapping
  • Incorporating Preferences in Perceptual Mapping
  • Advanced Issues in Preference Mapping
  • Perceptual Mapping using MeXL
  • Analytics for Product/Service Design
  • The Relevance of Trade-off Approaches
  • Conjoint Analysis
  • Approaches to Conjoint Analysis
  • Conjoint Analysis using MeXL
  • Complete Conjoint Analysis
  • Interpreting Conjoint Results
  • Optimizing Design using Conjoint Results
  • Analytics for Tracking Customer Growth

Diffusion Models: The Bass Model

  • Application of Bass Model using MeXL


Decision Analytics for Emerging Digital Media Options

  • Framework to choose among different digital media options
  • Emerging display advertising ecosystem (e.g. ad exchanges bidding) and Retargeting and real time bidding) and Retargeting
  • Decision Analytics for real time and targeted display advertising
  • Using Experiments to Optimize Value of Digital Media
  • An introduction to experiments. Why do we need them?
  • Using Logistic Regressions
  • Experiment Design for E-retailing
  • Challenges in selling focal products and their accessories

Can product videos create demand for slower selling products?

  • Using multivariate regressions to measure effectiveness of product videos for e-retail
  • Analytics of Multivariate Experiments
  • Testing digital media e.g. websites
  • Experiments with large number of factors
  • Analytics of User Generated Content
  • Text and sentiment analytics
  • Designing effective rating mechanisms
  • Mining positive and negative opinions and assigning their impact for improving business outcomes


  • New Digital Business Models

Resource Models:

  • Open innovation, emerging principles and key learnings in crowdfunding

Data Driven Search Engine Advertising:

  • Fundamentals of search engine marketing, web analytics,and digital advertising
  • Application of multivariate regression on sponsored search advertising case

The Analytics of Digital Attribution:

  • Digital marketing attribution

Social Media:

  • Fundamentals of social media (Paid, earned and owned media)
  • How are businesses adopting social media: Discussion of Ford Fiesta case
  • Social listening and text analytics

The Analytics of Mobile Advertising:

  • Painting the mobile landscape
  • Discussion of mobile analytics
  • Application of multivariate regression on mobile services adoption case

Tools You Will Learn…

  • R
  • MeXL/XLMiner

Course Introduction Video

Watch our sample e-learning video recorded by industry’s best trainers with extensive subject knowledge expertise and who are considered to be the best trainers of the industry. All the participants will be provided access to our state-of-the-art Learning management system (LMS) at, where one can access end to end course videos at your own pace & convenience sitting back at your home. Videos can be accessed from your desktop, mobile, tablet, etc. Switch back and forth as you choose



Excellent training delivered by ExcelR, The content covered, Examples reffred to PMBOK with the questions .

Chandrakala Mamidala, Senior Quality Analyst, Oracle



Excellent!! Very good presentation skill. Explained all queries. Clear understanding of subject.

Satendra sharma Sharma, L1 Support Engineer, Accenture Banglore


Modes of Training

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