Rishitha Tellakula

Bonus Optimization Model Development

• Developed a Bonus Optimization Model using scikit-learn to apply clustering algorithms on gameplay activity to segment users based on their bonus-eligible behavior and engagement patterns.
• Segmentation enabled smarter bonus allocation, driving more effective promotions, reducing costs, and improving ROI. Data processed in Azure Databricks using PySpark, with insights visualized in a Power BI dashboard.

Revenue Performance Dashboard

• Built a comprehensive dashboard to review and compare Net Gaming Revenue (NGR) performance across the group, focusing on key revenue drivers.
• Enabled performance comparison against Budgeted (PLAN) and Previous Years' Results (SPLY), providing a detailed breakdown of revenue by primary drivers, supporting data-driven strategic decision-making.

Game Metrics & Player Behavior Analysis

• Tracked game-specific metrics to evaluate feature usage and player progression, driving data-informed decisions for game design and optimization.
• Applied strong SQL and analytical skills to manage and interpret complex behavioral datasets at scale, ensuring data quality and usability for long-term tracking.
• Utilized Google Analytics data via BigQuery to uncover user interaction patterns across key features of betting platforms.

Data Storytelling:

• Designed and maintained 8+ interactive Power BI dashboards, tracking core metrics like DAUs, clicks per user, and average session duration per game.
• Visualized customer journeys and performance across features such as Casino Filters/Search, Game Grid, Login/Logout, Community Chat, Betting Flows, and KYC Funnels.

Streaming Influence Analysis

• Performed an in-depth analysis of customer streaming behavior and correlated it with betting activity using Google Analytics, BigQuery, and DWH data.
• Findings contributed to a strategic decision to allocate €8M to streaming, driving a €32M increase in Net Gaming Revenue (NGR).

Enabled executive decision-making through the analysis of customer lifetime values

• Developed a Cohorts Performance Dashboard in Power BI to analyze customer lifetime values over the past 18 months, with drill-down capabilities across market, product, and channel for all financial and customer KPIs.

Promotion Analysis, Automation, & Optimization

• Reduced promo analysis delivery time from 4–5 hours per ticket to 0.5–1 hour through Python script optimization and automated dashboards, saving 420+ man-hours monthly.
• Developed reusable Python scripts, automating 80% of the logic for promo tickets, covering 85% of cases, and clearing backlogs by mid-month.

Budget and Forecast Distribution Automation

• Replaced a manual, error-prone heuristic model (5 hours/market, 30 man-hours/month) with a machine learning solution, reducing process time to just 4 hours and saving 26 man-hours monthly.

Revamped Age Segmentation Metric

• Cleansed five years of historical data and used K-means clustering to categorize customers based on stakes, wins, and active days across markets and products.
• Implemented precise data cleaning techniques, including capping VIP player data, to ensure accurate analysis.

Data Governance

• Collaborated with cross-functional teams to establish a centralized data dictionary and report catalog, enhancing transparency and consistency in reporting.
• Ensured data alignment across business functions by assisting in maintaining naming conventions, promoting data quality awareness, and supporting governance best practices within analytics workflows.

Led Intern Training Program

• Led a 4-week intern training program, managing a team of six interns, guiding their data analysis projects, and mentoring them in creating presentations.

Ad-hoc requests

• Managed 60+ ad-hoc requests, developing quick dashboards, and extracting data to uncover trends and patterns for various teams, providing actionable insights across departments.

• Collaborated with Salesforce and Product teams to design and deliver high-quality eGRC applications.
• Developed a PoC that reduced data export time by 60% through process optimization.
• Built dashboards and drilldowns to identify support bottlenecks, reducing customer tickets by 25%.
• Used SQL, Python, Excel, and Tableau to clean data and build interactive dashboards for GRC KPIs and customer behavior insights.
• Contributed to monthly and quarterly product development reports for leadership.