OLAITAN BELLO

Engineered and deployed analytical frameworks on messaging datasets to optimize customer response times and boost engagement efficiency, driving data-driven product and service enhancements.

• Performed customer churn analysis and trend forecasting on support data, providing data-driven insights that directly enhanced resolution efficiency and reduced ticket volume.
• Conducted deep-dive analysis of comprehensive messaging data to segment customer concerns and identify recurring issues, leading to targeted product and service enhancements.
• Designed and implemented KPIs (Key Performance Indicators) to measure customer satisfaction and evaluate chatbot performance, providing data to optimize automated support systems.

Developed automated reporting systems to enhance visibility into customer concerns and resolution metrics, significantly improving operational oversight.

• Utilized SQL and Power BI to manage and analyze extensive customer support datasets, proactively identifying recurring product issues and informing targeted improvements in product usability.
• Streamlined issue resolution workflows by conducting user data analysis, resulting in a quantifiable increase in support efficiency and faster customer issue resolution.
• Tracked and logged customer issues, ensuring swift resolutions and identifying systemic patterns to proactively address larger product problems.

Integrated data-driven usability testing and user-centered design principles to lead product initiatives, resulting in a documented 20% boost in customer satisfaction and enhanced product functionality.

Implemented data-informed logistics strategies and defined operational policies, resulting in optimized service delivery and a formalized end-to-end workflow.

Managed full-cycle accounts payable, resolving complex billing discrepancies to achieve a 20% reduction in payment errors and upholding stringent financial data integrity.

This project focuses on identifying the root causes of employee attrition at Indicino and building a predictive model to flag employees at high risk of leaving. The goal is to provide the HR Group Head with actionable, data-backed recommendations to reduce the overall turnover rate of 16.12%. Tools Used: Pandas, Numpy, Matplotlib, Seaborn, and Scikit-Learn.

Goal: Companies always want to know who is about to leave (churn). This project proves the use of Machine Learning to save money by identifying at-risk customers. - Developed a predictive classification model (e.g., Random Forest Classifier) using Python (Scikit-learn and Pandas) to forecast customer churn. - Cleaned, engineered, and transformed a proprietary dataset of 50,000 customer records, achieving an optimal feature set for model training. - The final model achieved an accuracy of 86%, providing marketing teams with a prioritized list of high-risk users for targeted retention campaigns. - Tools Used: Python, Scikit-learn, Pandas, Matplotlib, Hypothesis Testing.