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​What is Cluster Analysis

Cluster analysis refers to a technique for identifying groups in which the elements of each group are similar. It is often used in marketing in order to understand differences among customers.
There are three types of analytics models – descriptive, predictive, and prescriptive. Cluster analysis is essentially a data mining tool which is considered predictive, but since it help managers make better decisions, it can also be considered prescriptive.
Cluster Analysis has several applications:
  • Market segmentation
The main idea of this application of cluster analysis is to create homogeneous market segments to target product offerings.
  • Product segmentation
In product segmentation, cluster analysis identifies groups of consumers who want different benefits or levels of functionality of a product category. The results are used to offer different versions of a product, in order to match consumers needs more precisely.
Example: Coffee producers offer regular and decaffeinated versions of the same brand.
  • Price segmentation
Here, the same product is offered at different prices to different groups. Higher prices are offered to customers in groups that are willing to pay for extras or pay for better quality packaging, or for additional customer services.
  • Human Resources
In human resources, clustering could identify characteristics of successful employees in order to improve recruiting and hiring practices.
  • Operations Management
Grouping customers by geographical areas is a typical application of cluster analysis in operations management.
 
Importance of market segmentation
The key idea in market segmentation is to divide a broad market into segments or clusters. The technique produces groups whose members have similar needs, interests, and priorities. And this helps companies design specific strategies. For example, an advertising campaign, or a set of promotions, to target each market segment.
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In the above graph, one can observe that it is not possible to find perfectly homogeneous groups. However, the groups resulting from cluster analysis are similar in some ways.
The important thing here is to identify and describe each cluster. In the above example, the orange cluster may be described as a group of multi-coloured round objects. The green cluster may be characterized as a group of square objects. And the blue cluster consists of triangular and diamond-shaped objects.

Use case for market segmentation
Product category: Natural and organic products.
Study details:
  • Conducted by Information Resources Inc. in the US markets    Surveyed 5,000 customers
  • Matched the participants answers with the actual purchasing behaviour
Basis of segmentation:
  • Lifestyle
  • History of purchasing organic and natural products
  • Attitudes towards organic and natural products
  • Demographics
Segments identified:
1.True Believers
  • passionate about staying fit and healthy
  • focused on trying new things
  • serving as a strong role models for their children
  • strong believers in the benefit of natural and organic products
  • median income is highest among all segments
  • average age is 40
  • have at least a college education

2.Enlightened Environmentalist
  • passionate about the environment and about making good choices to preserve it
  • committed to make healthier choices
  • go out of their way to shop at stores that carry natural and organic products
  • older than True Believers - averaging 63 years old
  • slightly lower median income than True Believers
3.Strapped Seekers
  • believe and seek natural products, when their budget allows it
  • realize the benefit of natural and organic products
  • have the potential of becoming True Believers as their income increases
4.Healthy Realists
  • passionate about being fit but find it difficult deciding whether to buy healthy or traditional products
5.Indifferent Traditionalists
  • lead a simple life with few passions
  • not likely to buy natural/organic products because they don't see the reason to change
6.Struggling Switchers
  • know that they should be eating healthier and exercising more but focus on staying within their budgets
7.Resistant Non-believers
  • very little desire to support all their options for things to buy
  • stay loyal to the products they know

Key segments: True Believers and Enlightened Environmentalists  represent 18% of the population  together, they drive nearly half of the total sales
Strong target: Strapped Seekers and Healthy Realist  representing 25% of the consumers  driving 24% of sales 
Segments not worth pursuing: Indifferent Traditionalists, Struggling Switchers and Resistant Non-believers







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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas