RIYA SHRINGI

Insurance Renewal Tracking & Follow-Up Insights

Tools: SQL, Excel, Power BI

Description: Developed an analytics solution to track upcoming insurance renewals and enable data-driven prioritization of customer follow-ups.

Key Contributions:

• Extracted renewal data for 15/30/45-day windows covering ~2,000–2,500 customer records per month using SQL.
• Cleaned, standardized, and validated datasets using Excel and Power Query to ensure data consistency.
• Developed Power BI analytical views highlighting upcoming renewals, top 20% high-value customers, RM-wise follow-up status, and premium at risk by branch.
• Enabled 10–12 sales managers and relationship managers to monitor renewal workload and priorities on a daily basis.

Impact:

• Reduced manual MIS preparation time by 30–40%, saving ~8–10 hours per week.
• Contributed to an estimated 10–15% reduction in missed renewal cases through improved follow-up prioritization.
• Improved visibility into premium exposure, helping teams focus on high-value renewals contributing ~60–70% of total premium.

Sales Performance & Target Achievement Analysis

Tools: Excel, Power BI

Description: Performed sales performance analysis using structured MIS data to track target achievement, identify gaps, and support planning decisions.

Key Contributions:

• Analyzed monthly sales and target data covering 4–6 branches and 15–20 relationship managers, processing 300–500 records per month.
• Developed Power BI analytical summaries tracking target vs achievement %, premium trends, RM productivity rankings, and product-level revenue contribution.
• Implemented KPI indicators to flag the bottom 20% underperforming products and teams, enabling faster corrective actions.
• Supported monthly and quarterly business reviews with structured, insight-driven sales analysis.

Impact:

• Reduced monthly MIS reporting time from 3–4 hours to under 1 hour, saving ~20–25 man-hours per month.
• Improved reporting accuracy and consistency, reducing manual rework by ~25–30%.

• Sales Target Planning & Forecast Accuracy Improvement
• Tools: SQL, Excel
• Analyzed 6–12 months of historical sales data covering 300–500 records per month to identify performance trends and seasonality.
• Compared target vs actual sales across 4–6 branches and 15–20 RMs to identify consistent over- and under-targeting patterns.
• Calculated forecast variance and accuracy metrics to highlight planning gaps at branch and product levels.
• Supported target planning recommendations by segmenting data by product, branch, and RM performance.
• Improved sales forecast accuracy by ~10–15%, reducing large target deviations by ~20%.
• Reduced mid-cycle target revisions and planning rework, saving ~15–20% effort for sales managers.