Clarity from data
Results for YOUr business
Your business already runs on data every day.
I help bring that data into focus—highlighting patterns, opportunities, and next steps that can improve performance and support growth.
The goal is simple: make good decisions even stronger.
HOW DO WE DO IT ?
*
HOW DO WE DO IT ? *
Start with Clarity
We begin by understanding your business, your data, and the decisions you’re trying to make.
Analyze What Matters
Instead of overwhelming you with numbers, I focus on the data points that actually impact your bottom line.
Uncover What’s Driving Results
Together, we identify what’s working, what can be improved, and where the biggest opportunities are.
Deliver Actionable Insight
Everything is translated into clear next steps—no jargon, just decisions you can confidently act on.
REAL WORLD PROJECTS ALREADY COMPLETED
Customer Retention Analysis
Developed a predictive machine learning model to identify customers likely to churn before it occurs, enabling earlier detection of at-risk behavior and more informed decision-making.
Results:
Identified up to 70% of at-risk customers in advance
Enabled a potential 15–25% reduction in customer churn
Equivalent to retaining 200+ customers per 1,000 at risk
Strong predictive performance on unseen data (AUC: 0.85)
Time Series Forecasting Model
Developed a time-series machine learning model to predict short-term demand using historical data. The model incorporates lag features and temporal patterns such as hour-of-day and day-of-week to capture real-world trends and seasonality.
Results:
Achieved RMSE of 35.04 on unseen test data
Delivered reliable short-term (1-hour ahead) forecasts
Identified consistent demand patterns across time intervals
Effectively modeled temporal dependencies to improve prediction accuracy
HOW This helps your business
Customer Retention Analysis
For businesses that rely on repeat customers, this type of model provides early visibility into which customers are most likely to leave. This allows for more focused and efficient retention strategies.
Prioritize outreach toward high-risk customers
Allocate marketing resources more effectively
Improve customer lifetime value through timely intervention
This shifts retention efforts from reactive to proactive, helping preserve revenue and support long-term stability.
Time Series Forecasting Model
For businesses with fluctuating demand—across services, staffing, or inventory—this type of forecasting provides a clearer view of upcoming demand patterns.
Align resources with expected demand levels
Reduce inefficiencies during low-demand periods
Support more consistent, data-informed operational planning
This leads to a more balanced and efficient operation, with better utilization of resources across both high and low demand periods.