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    • 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
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      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
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    • 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
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      • Planning for data visualisation
      • Visualisation Component
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    • 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
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      • 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
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    • 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
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      • Building Your Own Start-up Technology Company, Part 3
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      • Renewable energy is no longer alternative energy
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      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
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    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model

​Net Promoter Score and Self-Reports 

Picture
​Net Promoter Score
  • How likely is it that you would recommend a particular brand, your company, to a friend or colleague?
  • It's done on a 0 to 10 scale.
  • Promoters are people who score a 9 or 10 on this scale.
  • Passives are people who score a 7 or 8.
  • Detractors are people who score 0 to 6.
  • NPS = Percentage of Promoters – Percentage of Detractors
NPS allows you to track the health of your brand. Do your customers overall like you? Or are there growing percentage of people who might become detractors?
 
Example: Zappos uses NPS to see how their customer services are.
 
When discussing Net Promoter Score, the bigger issue of customer satisfaction comes to mind. The key question here is how to measure customer satisfaction.
 
To do this, the NPS is used.
 
Net Promoter Score came about from an article from Frank Reichheld. This was published in HBR. The following graphs show that Net Promoter Score really well captures what's going on in overall customer satisfaction.
 


​
 








​Criticism of NPS:
  • Very similar to ASCI (American Satisfaction Consumer Index)
  • For many industries, ASCI index has a higher R2 with industry growth which is higher than NPS
  • There is no clear evidence that NPS is superior to other metrics
 
In the above image, we see that for a set of companies, the R2 from ASCI is higher than the R2 from NPS.
 
Is NPS related with Profitability?
In a way, yes. Net Promoter Score can be related to customer satisfaction, which in fact has been shown many times in past work that it is correlated with profitability. Overall, literature generally suggests that higher customer satisfaction leads to positive outcome for a firm.
What's the problem here?
The link actually might be much weaker than what managers generally think. While the correlations are positive, customer satisfaction only explains a limited part of firm value and firm performance.
Why is that the case?
The common thought is à More satisfied customers are, they'll be more happy to do business with that firm, and hence profitability should be higher. While that is true, it still explains only a limited part.
Why is that?
What would be a way in which satisfaction and profitability might be linked together? If you look at this graph, it shows that the way managers perceive the link between satisfaction and profitability is typically a straight line. So what they intuitively think is that if you keep increasing customer satisfaction, profitability will always keep rising.
 
 
 
However, what people have found, in fact it's much more complicated. There is a positive relationship, but it's not a straight line.
  • If you're at the lower ends, you do see an increase in profitability.
  • But many companies might actually be on the flat part of the curve, which is, it's kind of business as usual. Here we see that there is a big flat region where increasing satisfaction, does not actually increase profitability as much.
  • When you cross that particular region, then you go to the zone of delight. Much fewer companies, these are people who have amazing customer service, these are the standouts. And for them in their particular category, you would see again that increasing satisfaction does increase profitability.
 
The key point to note here is that the majority of companies are in the flat area. So increasing satisfaction might not make a measurable change in profitability.
Another thing that impacts this is competition.
 
 
When we’re doing any kind of survey, we want to think about a few things.
  • What is it capturing?
  • How does it compare with other metrics? Does it do better, does it do worse? How exactly what it's capturing is different from what other service could do?
How does the metric link with managerial outcomes?

Self-reports

With surveys, the company is reaching out to customers. However, many times customers can reach out to companies by giving self-reports of what they're buying and when they're buying it.
Example: InfoScout is an example of a company that attracts or incentivizes customers to do the following.
  • Once they have made their purchases, they take the receipt, they have the mobile device, take a picture of the receipt and send it back.
  • InfoScout then collects all of this information across many, many customers, to get insights into when people are buying certain products, where are they buying it, is it at pop stores, is it in convenience stores, is it in big box stores?
 
 
Another example is word of mouth dynamics. Here, we would like to know what people are purchasing and what people are talking about. How is our brand being mentioned?
Example: Keller Fay is a company that collects word of mouth dynamics.
  • How do they do that? They have panel of customers which basically are given the following task: when you talk to somebody note it down. Who is it? Is it a friend? Is it a colleague? What did you talk about?
  • Collecting this data in a diary format over time, across months, they are able to observe what people are talking about, who are they talking to, how is a brand being mentioned, and so on. 

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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