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What is Descriptive Analytics?

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​​These days, a lot of collection and analysis of big data is outsourced to third party companies who specialized these things. It is very important to know about these companies. The way in which they're collecting this data, and the kinds of the questions that the data is trying to explore.
Examples of data that are used for analytics include scanner data at grocery stores, or metric for measuring audience engagement by media companies. Apart from this, several other industries engage in descriptive data analytics.
Now, why is knowing about these companies important? By learning about these companies, you will take away actionable techniques in thinking and forming questions, about descriptive data, techniques that you can employ in whatever descriptive data environment you are attempting to analyze.
What is descriptive analytics?
  • Descriptive analytics is a way of linking the market to the firm through decisions.
  • It is the information that's needed to make actionable decisions.
  • It is principles for systematically collecting and interpreting data that can aid decision makers.
The common thread here is getting good data.
Let us look at the synergy between data and decisions that managers have to make that makes for good analytics.
 
 
What are the kinds of decisions that managers might have to make?




​











​​Decisions related to answering exploratory questions
Example: Let us think about a brand manager, who is looking at their brand sales and the numbers suddenly start dropping. Question is why are they dropping?
  • Is it because customer preferences have changed?
  • Is it because customers like competitors?
  • Etc. etc.
Hence, this stage is a completely exploratory one. We're trying to understand why things are not working out the way we expect them to.
Decisions related to answering descriptive questions
Let us take the same example of the brand manager.
  • What's their customer share of wallet?
  • How much are they spending with the brand?
  • How much are they spending with their competitors?
  • Who are our customers?
  • What's our segmentation like?
As we can see, the kind of questions here have changed and require hard data in order to find the right answers.
Decisions related to answering causal questions
  • If I'm changing the landing page on my website, how will it change consumer behavior?
  • Would it in increase it in terms of click through rate, or would it bring it down?
These are the causal questions which require systematic data collection and careful thought in terms of how to collect data.
As we move from exploratory questions to causal questions, the type of data that needs to be collected, the type of conditions that the data needs to be collected under also keep changing.
 
Exploratory Research
Exploratory type of data collection is typically done to develop initial hunches or insights.
For example, the brand manager thinking about why the sales are dropping. It could be a variety of different reasons.
This type of data collection is a first step to get a broad understanding of what the underlying problems could be and it provides broad guidelines on what you should look for more rigorously.
 
One of the most common ways of exploratory data collections are focus groups.
  • Rationale: talking about the brand, free-flowing conversation, in-depth probing, unstructured discussion, ability to observe dynamics
  • Format: 8-10 individuals, 1 moderator who designs the overall flow, about 1 hour long, incentives for participants
  • Common uses: Product concept, ad copy, survey design
Through focus groups, a manager wants to get insights as to what might be certain pain points for the consumers.
 
These days focus groups have morphed to many different ways. One of them is market research online communities, or internet communities.
 
For example: VocalPoint (competitor - CSpace)
  • VocalPoint brings about 100 to 200, or sometimes 500 people in a group.
  • It monitors them over a period of six months to a year.
  • The idea here is to build relationship with your consumers.
  • Over time these 100 to 200 people, start building relationships with each other.
  • They become more and more comfortable talking about their real feelings and real insights.
 
Advantages of internet communities:
  • Enhanced engagement with customers à these customers are together, talking to each other, talking to the brand for about six months to a year. So this closed concentration in terms of talking to each other, communication with the brand really enhances their engagement.
  • Shorter deadlines are possible à With focus group there are logistical issues in terms of trying to get these people in the room, get a moderator, etc. With internet communities, you are looking at these customers for about six months to a year, so you can actually have much shorter deadline.
  • “aha” moments à The most famous example is Kraft's 100 calorie pack. Here is what they did. They basically had a community that had worked with CSpace. They started looking at what do people want in snacks? The key insight was that it is not that people wanted to stop eating snacks, what they really wanted was snacks with low calories. Nabisco's 100 calorie pack has been an amazing success.
 
Caveat:
ROI is very hard to determine à This is because as you start engaging with an internet community, early on it might be quite difficult to forecast, what kind of insights will come out.

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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