- 2024 Operations Overview – volume, vendor performance, and day-of-week patterns https://public.tableau.com/views/CustomerSupport2024Overview/2024Overview?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
- Outages & Reliability – SLA on outage vs non-outage days and outage volume share https://public.tableau.com/views/CustomerSupportOutagesReliability/OutagesReliability?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
- 2025 Forecast & Staffing – Q1–Q2 forecast and required headcount by vendor https://public.tableau.com/views/CustomerSupport2025ForecastStaffing/2025ForecastStaffing?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
Customer support call analytics, forecasting, and vendor optimization case study
- Analyzed 4.8M+ customer support calls across shared and dedicated queues to understand 2024 volume patterns, vendor performance, and service quality.
- Found that Vendor A handled ~59% of volume with 90.5% SLA at a lower cost per call, while Vendor B achieved higher satisfaction (~39.5%) despite weaker SLA.
- Quantified that outages occurred on ~29% of days but reduced SLA by <1 percentage point, showing strong operational resilience in the support model.
- Built a Q1–Q2 2025 forecast (~2.5M calls) with event-based adjustments (e.g., Easter, Mother’s Day) and translated it into staffing plans of ~1,200 agents in Q1 and ~800 in Q2.
- Recommended a phased shift in vendor allocation toward Vendor A to capture cost savings while maintaining an overall SLA target of 88%+ and tracking satisfaction closely.
In addition to the Python notebooks, this repo includes an Excel-based implementation of the same analysis and forecasting logic, plus the original written report:
excel-solution/Customer_Support_Forecast_Excel.xlsx– 2024 summary, Q1–Q2 2025 forecast, and staffing model built entirely in Excel.excel-solution/Word report – case-study write‑up answering the original assessment questions.