Support Trend Analysis: Maximizing Service Efficiency (Q2 Y1 – Q2 Y2)

Support Trend Analysis: Maximizing Service Efficiency

A Proof of Concept in Data Visualization - Using Real Data

Executive Summary: AI-Driven Service Efficiency & Deflection Analysis

Project Overview

This project is a high-value data visualization proof-of-concept demonstrating the transformation of raw, anonymized enterprise support ticket data (from three historical exports) into a single, interactive infographic. The goal was to deliver actionable insights on service trends, customer sentiment shifts, and high-impact deflection opportunities.

Business Value & Impact

The analysis quantifies where support time is wasted on repetitive queries. By identifying that 43% of current ticket volume could be resolved via self-service, this project delivers a clear, evidence-based strategy for cost reduction, improved agent efficiency, and faster resolution times.

Skills Demonstrated (AI & Visualization)

  • Generative AI Synthesis: Used LLM capabilities to analyze thousands of unstructured text fields to categorize tickets and extract qualitative customer sentiment.
  • Data Transformation: Structured raw CSV data into a comparative five-quarter model for longitudinal trend analysis.
  • Data Visualization & UX: Developed a fully responsive SPA using Chart.js, HTML/CSS, and Tailwind.

5 Quarters of Historical Data

Q2 Y1 through Q2 Y2

~15,000 Anonymized Tickets

Focus on Thematic Grouping

Strategic Focus Areas

Sentiment Shift & Deflection Potential

Q2 Y2 Key Performance Indicators (Latest Quarter)

2,704
Total Tickets Processed (Q2 Y2)
43%
Estimated Knowledge Deflection Potential
1,529
Tickets in Top 5 Service Themes

Top 5 Service Themes: Volume & Composition Over Time

This stacked bar chart isolates the five most frequent service request themes, showing how their proportional demand shifted across the quarters. This breakdown identifies stable workload drivers versus variable, event-driven demand.

  • Core Drivers: General Maintenance/Repair and General Information & Admin consistently account for the largest share of overall demand, highlighting a constant requirement for day-to-day operational support.
  • Variable Load: The HR & Onboarding Events theme shows proportional spikes during high-volume periods, confirming that staffing or event cycles directly influence support load.

Customer Sentiment Shifts (Qualitative Analysis)

Analysis of ticket summaries and descriptions reveals distinct changes in customer urgency and request complexity, directly corresponding to the ticket volume cycle. This timeline illustrates the evolution of customer needs.

Q2 Y1

Neutral & Procedural Focus

Low-volume quarter dominated by straightforward, transactional requests (e.g., "Internal Comms Request").

Q3 Y1

Increased Urgency/Time-Sensitive Needs

Volume spike led to a noticeable increase in tickets marked as urgent (e.g., "Need meeting room NOW").

Q4 Y1

Complexity and Operational Strain

The highest volume quarter featured more complex, multi-step requests (e.g., event hosting), reflecting user frustration.

Q1 Y2

Informational Seeking Dominance

As volume moderates, the majority of tickets revert to simple "how-to" and policy questions.

Q2 Y2

Efficiency-Driven Querying

Current quarter maintains a high level of repetitive, solvable-by-documentation questions, confirming a major deflection opportunity.

Deflection Opportunity (Quantity)

Quantifying the total number of tickets identified as suitable for self-service deflection across each quarter. The consistent high count confirms this as a priority strategy for reducing manual workload.

Top 3 Deflection Targets (Overall)

Focusing on these three recurring themes across all quarters provides the highest return on investment for knowledge base creation:

  • 1. FAQ & Policy Guide

    Targeting 'General Information & Admin' - Over 1,900 tickets.

  • 2. Commuting & Parking Guide

    Targeting 'Logistics & Asset Movement' sub-queries - Over 600 tickets.

  • 3. Meeting/Amenity Room Booking Tutorial

    Targeting confusion in system use - Over 800 tickets.