Aziza Aldamurodova - Data Analyst | ContraWork by Aziza Aldamurodova
Aziza Aldamurodova

Aziza Aldamurodova

Data Analyst | Power BI, Tableau & Excel

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Aziza is ready for their next project!

Tashkent, Uzbekistan

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Tashkent, Uzbekistan

Cover image for I just finished a deep
I just finished a deep dive into marketplace seller retention and churn.I analyzed 2,945 sellers from the Olist Brazilian E-Commerce dataset to understand: which sellers generate most of the GMV what early seller activity looks like before retention how seller behavior changes before churn which seller segments need different retention strategies One finding that stood out: 14.9% of sellers generated 69.6% of marketplace GMV.I also found that sellers making 6+ sales in their first 30 days had 94.6% next-period retention after correcting for right-censoring.The interesting part wasn't just finding the numbers — I went back and re-ran the analysis after identifying methodological issues in the first version, including right-censoring and confidence intervals.The result is a seller lifecycle analysis covering segmentation, retention, churn trajectory, and engagement. Project: Seller Lifecycle & Retention Analytics I'd be interested to hear how other analysts approach retention and churn problems in marketplace businesses. #DataAnalytics #ProductAnalytics #Retention #ChurnAnalysis #DataVisualization
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Cover image for Seller Lifecycle & Retention Analytics — Marketplace Churn S...
Seller Lifecycle & Retention Analytics — Marketplace Churn Study
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Cover image for Telecom Churn & Retention Analysis
Telecom Churn & Retention Analysis Dashboard An end-to-end telecom customer churn and retention analysis covering 7,043 customers. I analyzed churn patterns, customer segments, revenue concentration, and retention opportunities using data analysis, customer segmentation, and profit-driven retention modeling.
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Cover image for Online Retail Customer Retention Analysis
Online Retail Customer Retention Analysis Customer retention analysis and dashboard for an online retail business. I analyzed customer behavior using RFM segmentation, retention lifecycle metrics, and behavioral personas. The project identifies key retention patterns, dormant customer segments, VIP revenue concentration, and opportunities for improving repeat purchases and reactivation.Tools: Python, Pandas, Excel, Data Visualization, RFM Analysis, K-means Clustering.
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