Solve Problems w/ Data - Exploring Business Applications

Common Problem Types in Data Analysis

Example: Anywhere Gaming Repair
- Problem: Determining the best advertising method for attracting new customers.
- Approach: Used data to envision different advertising outcomes.
- Outcome: Data-driven decision-making about likely success in advertising strategies.

Example: Improving Customer Satisfaction
- Problem: A business wants to enhance customer satisfaction levels.
- Approach: Analysts review recorded customer service calls to assess satisfaction levels.
- Process: Identify keywords and categorize them (e.g., politeness, satisfaction, dissatisfaction, empathy).
- Outcome: Helps to identify high-performing representatives and those needing coaching, ultimately leading to higher satisfaction scores.

Example: Health Tracking Apps
- Example of a woman with an athletic background receiving a notification on her smartwatch about an abnormal heart rate spike.
- Context: Normal resting heart rate around 70, sudden spike to 90 indicated a potential health issue.
- Importance: Data from the smartwatch prompted her to seek medical attention, revealing a serious health condition.

Example: User Experience Design
- Problem: A designer wants to improve the interactions customers have with a coffee maker.
- Approach: Collected anonymous survey data from users to gather insights about their experiences.
- Process: Identify key themes in feedback to determine common issues (e.g., unclear on/off indication).
- Outcome: Resulted in design optimizations leading to better user experience and satisfaction.

Example: Third Party Logistics
- Problem: Reducing wait times for shipments during loading.
- Approach: Sharing data between partner companies to analyze timelines and identify causes of delays.
- Outcome: Companies align their delivery schedules to minimize downtime and enhance efficiency.

Example: Oil and Gas Companies
- Problem: Stopping machines from breaking down.
- Approach: Analyze historical data to identify patterns leading to breakdowns.
- Insight: Found that machine failures increased when maintenance was neglected beyond a 15-day cycle.
- Outcome: Improved monitoring and timely intervention has become key in preventing breakdowns.

Data analysis plays a crucial role in helping individuals and businesses make informed decisions, enhance satisfaction, and drive improvements through various problem-solving techniques.