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Businesses need the right tools to make sense of the large data in our modern world.
Data from financial instruments help to predict client spending habits, and information from social media help to make sense of marketing campaigns.
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Customer Analytics & Machine Learning
Turning customer data into business intelligence.
At Cemendur, we develop data-driven solutions that help businesses understand their customers, identify opportunities and make better decisions.
Using Stripe payment data from over 300 customers and 3,000 transactions, we developed a machine learning and analytics project focused on customer behaviour, geographic trends and predictive intelligence.
Key Capabilities
01. Geospatial Analysis
Using Python and interactive maps to understand where customers are located, identify high-demand areas and support business expansion decisions.
02. Customer Behaviour Analytics
Analysing spending patterns, customer lifetime value, payment frequency and retention to better understand customer relationships.
03. Predictive Machine Learning
Developing Logistic Regression and Random Forest models to predict customer churn, identify valuable customer segments and support targeted marketing.
Business Applications
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Smarter expansion: Identify promising locations using real customer data.
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Better retention: Understand which customers may be at risk of leaving.
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Targeted marketing: Reach the right customers more effectively.
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Informed decisions: Use statistical evidence instead of assumptions.
Technologies
Python · Pandas · NumPy · SQLite · Scikit-learn · Geopy · Plotly · Seaborn
CEMENDUR
Engineering intelligent solutions for real-world business challenges.
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