Data-Driven Insights: Optimizing Customer Engagement and Operational Efficiency
Data-Driven Insights: Optimizing Customer Engagement and Operational Efficiency
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This project showcases the development and analysis of KPI metrics for daily customer interactions within a leading E-Commerce platform. Utilizing Google Sheets for data presentation and SQL queries at the backend, the raw data is extracted seamlessly from a Google Cloud-based database. Automated data insertion, powered by Google Data Suite, ensures efficiency and accuracy. The comprehensive dashboard evaluates performance across customer support channels, including calls, emails, and chats, while also analyzing customer satisfaction (CSAT) data and daily operational trends. Key insights include case handling efficiency, buyer-seller segmentation, and root-cause analysis of issues via reason codes. Geographical segmentation and historical comparisons further enrich the insights, providing actionable opportunities to optimize customer engagement and operational effectiveness. This project exemplifies the integration of technology and data-driven decision-making for strategic business improvement.
This project showcases the development and analysis of KPI metrics for daily customer interactions within a leading E-Commerce platform. Utilizing Google Sheets for data presentation and SQL queries at the backend, the raw data is extracted seamlessly from a Google Cloud-based database. Automated data insertion, powered by Google Data Suite, ensures efficiency and accuracy. The comprehensive dashboard evaluates performance across customer support channels, including calls, emails, and chats, while also analyzing customer satisfaction (CSAT) data and daily operational trends. Key insights include case handling efficiency, buyer-seller segmentation, and root-cause analysis of issues via reason codes. Geographical segmentation and historical comparisons further enrich the insights, providing actionable opportunities to optimize customer engagement and operational effectiveness. This project exemplifies the integration of technology and data-driven decision-making for strategic business improvement.
MYCS Daily KPI Report from DS Data.xlsxSheet Descriptions
MYCS MP: The sheet focused on overall marketplace metrics. The data includes several KPI metrics by row (available in column C) by communication channels (available in column B) which are required for meaningful analysis.
MYCS Seller: Similar structure to "MYCS MP". This sheet appears to focus on seller-specific metrics, including performance or activity indicators.
MYCS Buyer: Mirrors "MYCS Seller. The focus here is on buyer-specific KPIs or behaviors.
HVI Summary: The sheet summarizes high-value metrics or indicators. The column structure includes data points labeled as updates for specific dates.
Top ReasonCode (L1): This sheet presents Level 1 analysis of reason codes. It provides insights into high-level issues or trends in customer interactions.
Top ReasonCode (L3): This sheet expands on "Top ReasonCode (L1)" with a deeper (Level 3) analysis.
L3 Analysis: This sheet delvs into Level 3 data with specific metrics. Columns include dates and potentially segmented data for detailed analysis.
Pivot (L1): A compact pivot table summarizing Level 1 data aggregating key metrics for easier interpretation.
Pivot (L3): Similar to "Pivot (L1)", summarizing Level 3 data.
Regional-RAW: This sheet contains raw data, automatically inserted as per scheduled from Google Cloud Data Suite.
Rest of the sheets contain raw data same as “Regional-Raw” sheet, for different channels and for different KPIs.
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