Project Title:
Enhancing Customer Experience with API and Export Usage Analytics
Project Objective:
To leverage advanced data analytics and machine learning techniques to gain deep insights into customer usage patterns of both API and report/list export options, enabling proactive issue resolution, personalized recommendations, and improved overall customer satisfaction.
Technical Approach:
1.Data Collection and Preparation:
- Data Extraction: Utilized SQL queries to extract relevant data from various databases, including API logs, export logs, user activity logs, and product usage metrics.
- Data Cleaning: Employed Python libraries like Pandas and NumPy to clean and preprocess the data, handling missing values, outliers, and inconsistencies.
- Feature Engineering: Created meaningful features from raw data, such as:
- API call frequency and response time
- Error rates for API and export functions
- Export frequency, file size, and format usage
- User demographics and time-based features
2.Exploratory Data Analysis (EDA):
- Data Visualization: Leveraged Python libraries like Matplotlib and Seaborn to visualize key metrics and trends, including API call volume, response time distribution, error rate analysis, export volume, file size distribution, and export format usage.
- Statistical Analysis: Employed statistical techniques to identify correlations, patterns, and anomalies in the data.
3.Predictive Maintenance:
- Model Development: Trained machine learning models, such as Random Forest and XGBoost, to predict potential API performance issues, export-related problems, or system bottlenecks based on historical usage data and system metrics.
- Model Deployment: Deployed the trained models into a production environment to enable real-time monitoring and alerting.
4.Personalized Recommendations:
- Recommendation System: Implemented a recommendation system using techniques like collaborative filtering and content-based filtering to suggest relevant API endpoints, export formats, customization options, or best practices to customers based on their usage behavior and preferences.
5.Anomaly Detection:
- Anomaly Detection Algorithms: Applied anomaly detection algorithms, such as Isolation Forest and One-Class SVM, to identify unusual API usage patterns or export behaviors that may indicate security threats, fraudulent activity, or system errors.
6.Customer Segmentation:
- Clustering Algorithms: Utilized clustering algorithms like K-Means and Hierarchical Clustering to segment customers based on their API and export usage patterns, demographics, and other relevant factors.
7.Dashboard Development:
- Dashboard Creation: Developed interactive dashboards using Power BI to visualize key metrics, trends, and insights from both API and export usage, enabling easy monitoring and decision-making.
Impact and Benefits:
- Improved System Reliability: Proactive identification and resolution of potential API and export-related issues.
- Enhanced Customer Experience: Personalized recommendations and optimized API and export options.
- Increased Customer Engagement: Tailored product suggestions and support.
- Enhanced Security: Early detection of security threats and fraudulent activity.
- Data-Driven Decision Making: Informed business decisions based on actionable insights from both API and export usage.
Technical Skills Utilized:
- Programming Languages: Python
- Data Engineering: Data extraction, cleaning, preprocessing, and feature engineering
- Machine Learning: Predictive modeling, recommendation systems, anomaly detection, and clustering
- Data Visualization: Creating insightful visualizations using libraries like Matplotlib and Seaborn
- Business Intelligence: Developing interactive dashboards using tools like Power BI
- SQL: Data extraction and query optimization
By effectively leveraging these technical skills and data-driven approaches, this project has significantly contributed to enhancing customer experience and optimizing the overall usage of API and export functionalities.