Project Learnings

People Analytics

1. HR Metrics for Employee Engagement and Attrition

   • Objective: Develop robust HR metrics to monitor engagement and predict attrition, optimizing recruitment, satisfaction, and retention strategies.

   • Approach: Created an HR KPI dashboard in Power BI to visualize trends. Logistic regression was employed to analyze factors like job satisfaction, compensation, and tenure. Descriptive analytics provided actionable insights into employee behavior across demographics.

   • Outcome: Achieved a 15% reduction in attrition and enhanced employee satisfaction. The dashboard provided HR leaders with real-time insights for proactive workforce management.

   • Key Learning: Data-driven HR strategies can precisely identify attrition drivers, enabling targeted retention efforts and long-term engagement.

   • Tools & Techniques: Power BI, Logistic Regression, Descriptive Analytics

2. Workforce Planning for a Global FMCG Client

   • Objective: Optimize workforce structure through Span of Control analysis, enhancing efficiency and reducing redundancies.

   • Approach: Conducted Span of Control and compensation analysis. Decision tree analysis was utilized to evaluate hierarchy, identify redundancies, and design a streamlined organizational structure.

   • Outcome: Reduced operational costs and improved team productivity through optimized resource allocation.

   • Key Learning: Structured workforce planning drives efficiency and enhances management capacity.

   • Tools & Techniques: Excel, Decision Tree Analysis, Compensation Benchmarking

3. Leadership 360° Assessment for a Consulting Firm

   • Objective: Improve executive coaching by tailoring development strategies based on leadership traits and behavioral patterns.

   • Approach: Leadership survey data was analyzed using correlation analysis to uncover relationships between traits and decision-making patterns. A Tableau visual report highlighted key strengths and improvement areas.

   • Outcome: Enabled customized coaching strategies, fostering leadership growth aligned with organizational goals.

   • Key Learning: Data-driven leadership analysis supports targeted coaching, maximizing individual and organizational impact.

   • Tools & Techniques: Tableau, Correlation Analysis

Customer Analytics

4. Customer Satisfaction and NPS Prediction

   • Objective: Identify key drivers of customer satisfaction and predict Net Promoter Scores (NPS) to enhance loyalty and service quality.

   • Approach: A multiple regression model analyzed variables like service speed, meal quality, and ambiance. An interactive dashboard in Looker Studio tracked NPS trends and areas for improvement.

   • Outcome: Delivered actionable insights to enhance customer satisfaction, with real-time NPS monitoring supporting continuous service improvements.

   • Key Learning: Regression analysis of satisfaction drivers empowers targeted improvements in customer loyalty.

   • Tools & Techniques: Looker Studio, Multiple Linear Regression, Correlation Analysis

Pricing Analytics

5. Price Recommendation Tool for an Automotive OEM

   • Objective: Establish optimal pricing for a new product by analyzing demand sensitivity.

   • Approach: Developed a price elasticity model using historical sales data and linear regression. Competitor and market condition analyses defined an optimal pricing range.

   • Outcome: Achieved a successful product launch with strong market penetration through a competitive pricing strategy.

   • Key Learning: Price elasticity modeling enables informed pricing decisions aligned with market dynamics.

   • Tools & Techniques: Excel, Python, Price Elasticity Modeling, Linear Regression

Market Research

6. Market Sizing and Competitor Analysis for a Beverage Company

   • Objective: Formulate a market entry strategy based on market size and competitor positioning.

   • Approach: Conducted industry research, analyzed competitor data, and segmented consumers by demographics to identify high-potential target groups.

   • Outcome: Successfully launched a product with positioning aligned to market demand, supported by competitive insights.

   • Key Learning: Consumer segmentation refines product positioning and ensures relevance in competitive markets.

   • Tools & Techniques: Excel, Market Segmentation

Change Management

7. Data Migration and Process Transition for Zendesk Data

   • Objective: Seamlessly migrate data from Zendesk to an Upwork data warehouse while maintaining continuity in reporting and KPI tracking.

   • Approach: Led the migration using SQL and ETL scripts to ensure data consistency. A Zendesk Explore dashboard monitored KPIs, ensuring data integrity throughout the process.

   • Outcome: Successfully completed migration with uninterrupted reporting and improved data accessibility.

   • Key Learning: Structured data migration with real-time KPI tracking ensures seamless transitions and scalability.

   • Tools & Techniques: Zendesk Explore, SQL, ETL Processes

Predictive Analytics

8. Horse Race Outcome Prediction

   • Objective: Forecast horse race outcomes to provide an edge in sports betting.

   • Approach: Applied Random Forest and Gradient Boosting algorithms to analyze factors such as historical performance, track conditions, and jockey stats.

   • Outcome: Delivered a high-accuracy prediction model, enabling informed betting decisions and measurable competitive advantage.

   • Key Learning: Ensemble methods like Random Forest and Gradient Boosting excel in complex predictive tasks, delivering high-accuracy insights.

   • Tools & Techniques: Python, Random Forest, Gradient Boosting

For further information, connect via LinkedIn or the Upwork profile provided.