Salesforce CRM Analytics
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Summary
Developed and optimized dashboards for sales pipeline monitoring and CRM data analysis to enhance business strategy and performance.
Analytical and detail-oriented Data Analyst with 9 months of hands-on experience in data-driven decision-making through internships. Proven expertise in SQL, Power BI, Tableau, Python, and Excel, with a track record of building automated dashboards that reduced reporting time by 25% and improved forecast accuracy by 40%. Adept at financial, insurance, and CRM analytics, skilled in interpreting regulatory requirements and supporting risk assessment to drive business intelligence and strategic outcomes.
Data Analyst Intern
Hyderabad, Telangana, India
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Summary
As a Data Analyst Intern at ExcelR, Rachana optimized data processing workflows and developed interactive Power BI dashboards, significantly improving financial forecast accuracy and reducing reporting time.
Highlights
Optimized data extraction and processing workflows using SQL and Python, achieving a 25% reduction in reporting time.
Designed interactive Power BI dashboards for financial KPIs, enhancing forecast accuracy by 40%.
Automated recurring compliance and performance reports, streamlining management review processes and improving efficiency.
Conducted in-depth data investigations to identify anomalies and ensure high data quality in financial datasets.
Collaborated with stakeholders to align analytical outputs with regulatory requirements and PMO objectives.
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B.Tech.
Computer Science Engineering
Grade: 70.2%
Issued By
Forage
Issued By
ExcelR
Issued By
Not specified
Joins, CTEs, Aggregations, Window Functions.
Data Processing, Logistic Regression.
Advanced Excel, Data Cleaning, ETL.
Dashboarding, KPI Tracking, DAX, Data Visualization.
Dashboarding, Data Visualization.
ETL.
Power Query.
Churn Prediction.
Sales Pipeline, Lead Conversion.
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Summary
Developed and optimized dashboards for sales pipeline monitoring and CRM data analysis to enhance business strategy and performance.
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Summary
Analyzed customer and policy data to identify churn patterns and risk profiles, applying predictive modeling for improved retention strategies.