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Questions and Answers
What is the main goal of Customer Lifetime Value (CLV) prediction in a subscription service?
What is the main goal of Customer Lifetime Value (CLV) prediction in a subscription service?
To predict the future value of customers to allocate marketing resources efficiently.
What type of analytics is used to examine historical data to understand the reasons behind past outcomes?
What type of analytics is used to examine historical data to understand the reasons behind past outcomes?
Diagnostic analytics
What is the purpose of diagnostic analytics?
What is the purpose of diagnostic analytics?
To understand why something happened by examining data.
What techniques are used in diagnostic analytics?
What techniques are used in diagnostic analytics?
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What is the outcome of diagnostic analytics?
What is the outcome of diagnostic analytics?
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What is the main application of diagnostic analytics in the healthcare industry?
What is the main application of diagnostic analytics in the healthcare industry?
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What is the purpose of data mining in diagnostic analytics?
What is the purpose of data mining in diagnostic analytics?
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What is the benefit of using regression analysis in Customer Lifetime Value (CLV) prediction?
What is the benefit of using regression analysis in Customer Lifetime Value (CLV) prediction?
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What is the primary goal of predictive analytics?
What is the primary goal of predictive analytics?
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What are the three key components of predictive analytics?
What are the three key components of predictive analytics?
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What is the purpose of time series analysis in predictive analytics?
What is the purpose of time series analysis in predictive analytics?
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What type of predictive model is used in regression analysis?
What type of predictive model is used in regression analysis?
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What is the role of machine learning in predictive analytics?
What is the role of machine learning in predictive analytics?
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What is an example of a practical application of predictive analytics?
What is an example of a practical application of predictive analytics?
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What type of data is needed for time series analysis?
What type of data is needed for time series analysis?
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What is the purpose of statistical software in predictive analytics?
What is the purpose of statistical software in predictive analytics?
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Study Notes
Predictive Analytics
- Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to predict future outcomes.
- It focuses on answering "how much?" or "how many?" by identifying patterns and trends in the data.
- Key components: historical data, statistical algorithms, and machine learning techniques.
Tools and Techniques for Predictive Analytics
- Linear Regression: predicts a continuous outcome based on one or more predictor variables.
- Time Series Analysis: for forecasting future values based on previously observed values.
- Machine Learning Algorithms: such as Random Forest, Gradient Boosting, and Neural Networks for complex and high-dimensional data.
- Statistical Software: tools like R, Python (with libraries like scikit-learn, TensorFlow), and specialized analytics software (SAS, SPSS).
Practical Examples
Time Series Analysis
- Involves statistical techniques for analyzing time-ordered data points.
- Used to forecast future values based on previously observed values.
- Example: Sales Forecasting, predicting next month's sales for planning inventory and staffing.
Regression Analysis
- Linear regression predicts a continuous outcome based on the linear relationship between the dependent variable and one or more independent variables.
- Example: Customer Lifetime Value (CLV) Prediction, predicting the future value of customers to allocate marketing resources efficiently.
Diagnostic Analytics
- Examines historical data to understand the reasons behind past outcomes.
- Helps in identifying patterns, relationships, and root causes of specific events or behaviors.
- Characteristics: purpose is to understand why something happened by examining data, uses techniques like drill down, data discovery, data mining, and correlation analysis.
Steps in Diagnostic Analytics
- Data Collection: gathers data on the failed batch, including material quality reports, production process logs, and inspection results.
- Drill Down: breaks down the data to examine specific aspects, such as material sources and production shifts.
- Correlation Analysis: identifies patterns or correlations between failures and specific variables, such as material supplier or production line used.
- Data Mining: uses advanced techniques to discover hidden patterns that may explain the failures.
Application in Various Fields
Healthcare
- Diagnosing the cause of a sudden increase in patient readmissions.
- Example: Type A: complications from a specific surgical procedure, Type B: inadequate post-discharge care.
Marketing
- Understanding why a marketing campaign failed.
- Example: Type A: poor targeting, Type B: ineffective messaging.
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Description
This quiz covers predictive analytics, a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to predict future outcomes.
