1-27 CSE 160 lecture
Introduction
Discussion about coding and potential use cases of data analytics in business.
Mention of practical applications within a banking context, focusing on a specific bank, Signet.
Overview of Signet Bank
Signet Bank known for its historical role in data-driven decision making in the late 20th century.
Initially, around 3% repayment default rate in credit card company charges (1994).
Evolution of Signet into Capital One, a leader in innovative credit card marketing strategies.
Data Mining Evolution
Capital One used data mining to improve marketing strategies that others later replicated.
Shift in credit card marketing due to insights gained from data analytics.
Emphasis on the technological limitations of the time (Intel 386 processes, floppy disks).
Market Identification Through Data
Idea of segmenting populations for better-targeted marketing.
Understanding of who would be interested in new products, e.g., life insurance.
Direct marketing strategies can be informed by specific demographic information (age, income).
Utilizing Data for Targeted Campaigns
Importance of sample sizes in testing market response (1,000, 10,000, etc.).
Analyze responses to gauge interest based on specific characteristics, such as age and income.
Segmentation of customers into sub-populations based on income thresholds (e.g., $50K).
Response Analysis
Observation of purchasing behavior based on income:
Those making less than $50K generally did not buy insurance.
Higher income individuals showed more interest in purchasing insurance.
Decision-making informed by data visualization showing responsiveness.
Segmentation Techniques
Illustration of using decision trees for market segmentation.
Decision Tree: helps classify customers based on characteristics (age, income).
Visual representation of segments allows for strategic decisions in marketing.
Decision Tree Analysis
Description of composition within different segments (positives vs. negatives).
A tree suggests potential buyer likelihood based on characteristics yielding actionable insight.
Uncertainty in predictions can be high near decision boundaries (e.g., near the $50K income segment).
Advanced Segmentation Models
Introduction of logistic regression as another model for prediction.
Distinction between classification (categorical outputs, yes/no) and regression (numeric values).
Explaining how statistical models can predict customer behavior.
Definitions of Key Terms
Model: Simplified representation of reality aimed at making predictions about future scenarios.
Example: Individual data point representing a fact (e.g., customer characteristics).
Training Data: Set of data points used to train models (e.g., customer responses to marketing).
Feature Vector: Collection of attributes or characteristics for a given example in a structured format.
Evaluating Model Validity
Validity vs. Quality in models:
A model can correctly represent the data (valid) but may still be misleading or impractical (not good).
Importance of fairness in models to prevent discrimination (e.g., age-related biases).
Types of Learning Algorithms
Supervised Learning: Models trained on labeled data to predict outcomes based on past observations.
Unsupervised Learning: Models identifying natural groupings within data without previously labeled examples.
Real-world applications like stock price predictions using supervised learning techniques.