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  • 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
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      • Formulating an Optimization Problem
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      • Digital Marketing Application of Optimization
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      • Formulation and Solution of Binary Optimization Problems
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      • Marketing Analytics and Customer Satisfaction
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      • Primary Scales of Measurement
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      • A/B Testing: Introduction
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      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
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      • N-Gram - Frequcy Count and phase mining
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      • 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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      • 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
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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
    • Narendra Modi Development Model of Gujarat
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    • Digital Marketing Application of Optimization
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      • IIMC says PepsiCo CEO Indra Nooyi was an average student
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      • The Start-Up of you.
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      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
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      • 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
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      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
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  • Untitled
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    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas

​Survey Design

Picture
Steps in survey design
0. Make sure your results are generalizable to an appropriate population
We survey 1000, 2000, 5000 people. But we need to ensure that what we’re capturing here is generalizable to our entire customer base
Things to note:
  • Population must be defined correctly (e.g. 25-30 year old males)
  • Sample must be representative of the population
  • Respondents selected to be interviewed are able and willing to cooperate
  • Respondents understand the question and have knowledge
  • Motivate them to provide information (incentive)
Problem: Response rate – in general, 5%
How to handle low response rates?
  • Collect data on non-respondents
  • Test the differences between those who respond and those who do not – the two should not be systematically different since the generalization is not good then.
For example: If you're interested in capturing people's responses of prices that you're charging for your product, and let's say there's a systematic difference between people who respond and people who don't respond in terms of their income.
That's clearly not very good because then what you're capturing is not a representation of the true target market.
  • Follow-up non-respondents
  • Try to convert them into responding
  • Could be a problem if they give bad data after repeated requests – compare waves of respondents to see their responses
  • Compare each wave to make sure they are similar
 
1. Develop detailed listing of ‘bits’ of information
Ensure that you translate research objectives into information requirements.
Check for relevancy à What would you do with the answer if you knew it?
2. Determine appropriate data collection method for each question
Formatting the question can be done in two ways:
  • Open-response questions à Why do you shop at Genuardi’s?
Advantages:
  • Respondents can give general reactions to questions such as “Why do you say that brand X is better?”
  • Response given in “real world” terminology, i.e., consumer’s own language
  • Can help interpret closed ended data à e.g. Why was color the most important product attribute?
  • May suggest additional alternatives to be used in closed-ended questions.
Disadvantages:
  • Often not good for self-administered surveys
  • Answers depend on respondent’s ability to articulate
  • Post-coding is tedious
  • Closed-response questions à Respondents are provided with pre-determined descriptions and selects one or more of them. e.g. How often do you shop at Genuardi’s? (Scale: Very often to Not at all)
Advantages:
  • Easy to use in field
  • Less threatening for respondent
  • Simple to code and enter data
  • Cheaper to administer
Disadvantages:
  • Usually requires pre-testing
  • Presumes the list of responses is complete
Best practice: Use open-ended questions for exploratory research and use closed-ended research for quantitative research
 
3. Write draft questions
  • Use simple, conventional language
Example:
Did you notice any malfunction at the time of purchase?
OR
Did you notice anything wrong with it when you bought it? (easier for the general audience to understand)
  • Avoid leading and loaded questions
  • Avoid ambiguity: Be as specific as possible
  • No long questions (not more than 20 words)
  • Start broad and then narrow down
4. Design Flow and Layout
Layout Guidelines
  • Open the survey with an easy and non-threatening question
  • Smooth and logical flow
  • From general to specific
  • Sensitive or difficult questions should not be placed at the beginning of the questionnaire
Order Bias (How we order the questions, where is it being asked in the overall survey is extremely important)
  • Common bias: fatigue
  • Randomize ordering of your questions
 


​










​5. Evaluate/Pilot test/ Redraft
  • The idea here is to make sure that before we implement the survey with your final target sample, just clean out the survey.
  • Make sure all the questions that you do are meaningful.
  • Ensure that people who are interested in answering the surveys, they understand what the questions are.
  • You have designed the questions with the respondent in mind. What that means is, you make sure that you're using verbiage or language, that the respondent would be familiar with.
  • Ensure that you're asking questions which go from more general to more specific.
  • Always lead off with questions which are non-threatening so that consumers or respondents who are answering the survey can easily get into the survey.
6. Get approval from all parties
If you're working within your company or if you're a consultant working for another company, get approval from all the parties. Extremely important, of course, if you're to implement the survey with customers
7. Pilot test/ Redraft
Similar points as in Step 5.
8. Final copy/ implementation
 

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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