Data Analyst

Role Title : Data Analyst – Customer Service (Express Business)

Role Purpose/ Overview

To institutionalize a data-driven culture within the Customer Service (CS) function by converting shipment, interaction, and operational data into actionable insights. This role centralizes analytics and provides the "single source of truth" for customer experience, service quality, and team productivity.

Key Accountability Areas

• Performance Dashboards: Design and automate daily/weekly/monthly "Customer Service Scorecards" covering KPIs like First Call Resolution (FCR), Turnaround Time (TAT), Net Promoter Score (NPS).
• Root Cause Analysis (RCA): Perform deep-dive analysis into recurring shipment delays or service failures to identify systemic bottlenecks in the express network.
• Productivity Mapping: Analyse agent-wise and hub-wise productivity metrics to optimize manpower allocation and identify training gaps.
• Trend Prediction: Utilize historical data to forecast peak season (festive/e-commerce sale) volumes, enabling proactive staffing and capacity planning in CS.
• Stakeholder Presentation: Present monthly CX insights to the functional head, highlighting "Top 5 Friction Points" for customers and recommending process improvements.

Qualification

• Graduate in Statistics, Mathematics, Economics, Business Analytics, or Engineering (B.E./B. Tech).
• Certification: Preferred certifications in Data Visualization (Tableau/Power BI) or Advanced Analytics.

Work Experience

• Duration: 3–6 years of experience as a Data Analyst
• Domain: Minimum 2 years of experience within Express Distribution, 3PL, or E-commerce Logistics is mandatory.
• CS Focus: Proven track record of working with Customer Service metrics and CRM datasets (e.g., Salesforce, Zendesk, or proprietary logistics ERPs).

Technical / Functional Competencies

• Data Visualization: Mastery in Power BI or Tableau to build interactive, real-time dashboards.
• Database Management: Expert-level SQL for extracting and interpreting data from large relational databases.
• Advanced Excel: Proficiency in Macros, Power Query, and complex modeling.
• Logistics Acumen: Deep understanding of express logistics concepts (e.g., Hub-and-Spoke, Transit Time, Last-Mile Delivery, Reverse Logistics).
• Statistical Analysis: Familiarity with regression, correlation, and trend analysis to validate data integrity and reliability.

Behavioral Competencies

• Analytical Rigor: Ability to "see the story" behind the numbers and spot anomalies that others miss.
• Solution Orientation: Focusing on "Why it happened" and "How to fix it" rather than just "What happened."
• Stakeholder Management: Ability to translate complex data into simple, non-technical language for CS Managers and Team Leaders.
• Tenacity & Agility: Comfort with managing large, sometimes "messy" datasets in a fast-paced organizational environment.