Dataset: Technical Support Dataset

About this Dataset

Technical Support Dataset

In today’s business landscape, companies of all sizes depend on technology for their day-to-day operations. Efficient technical support is vital in ensuring these systems run smoothly. This month’s challenge offers a real-world scenario for you to dive into—analyzing the performance of a technical support center. It’s a valuable opportunity to collaborate with peers, sharpen your analytical skills, and expand your professional experience.

Data Analysis Focus Areas:
You are free to choose your analytical approach, but consider the following key questions based on Technical Support Center Key Performance Indicators (KPIs):

Ticket Volume Trends:

Analyze daily, weekly, and monthly ticket volumes
Compare ticket volumes on workdays versus weekends
Study ticket distribution during work hours versus after hours
Identify peak times for ticket creation

Ticket Content and Resolution:

Spot trends in ticket topics
Assess first response and resolution times against SLAs
Compare performance across support channels (chat, phone, email)
Examine ticket submissions geographically for trends or product issues

Performance Metrics:

Measure agent adherence to SLAs for first responses and resolutions
Review customer satisfaction rates across agents, topics, and other factors
Analyze the speed at which tickets are resolved

Term	Description
Status	Ticket status within the support pipeline (Open: new ticket awaiting processing, In Progress: being addressed by an agent, Resolved: solution provided, Closed: ticket confirmed closed by the customer).
Ticket ID	Unique ticket identification number.
Source	Channel through which the request was made (chat, phone, email).
Priority	Urgency of the ticket (low, medium, high).
Support Level	Ticket difficulty level (Tier 1, Tier 2).
Product group	The product group related to the customer’s request.
Topic	Subject matter of the customer's inquiry.
Agent Group	Group to which the agent belongs (1st level support, 2nd level support).
Agent Name	Name of the agent currently handling the ticket.
Created time	Timestamp indicating when the ticket was received.
Expected SLA to first response	Deadline for providing the initial response.
First response time	Timestamp of when the initial response was given.
SLA For first response	First response compliance status (Within SLA, SLA Violated).
Expected SLA to resolve	Deadline for resolving the ticket.
Resolution time	Timestamp when the ticket was resolved.
SLA For Resolution	Resolution compliance status (Within SLA, SLA Violated).
Close time	Timestamp when the ticket was closed.
Agent interactions	Total number of agent interactions per ticket.
Survey results	Customer satisfaction score on a scale of 1 to 5.
Country	Country of origin for the customer creating the ticket.
Latitude	Latitude coordinates of the customer’s country.
Longitude	Longitude coordinates of the customer’s country.

Tables

Technical Support Dataset

@kaggle.suvroo_technical_support_dataset.technical_support_dataset

155.44 KB
2330 rows
22 columns


CREATE TABLE technical_support_dataset (
  "status" VARCHAR,
  "ticket_id" BIGINT,
  "priority" VARCHAR,
  "source" VARCHAR,
  "topic" VARCHAR,
  "agent_group" VARCHAR,
  "agent_name" VARCHAR,
  "created_time" TIMESTAMP,
  "expected_sla_to_resolve" TIMESTAMP,
  "expected_sla_to_first_response" TIMESTAMP,
  "first_response_time" TIMESTAMP,
  "sla_for_first_response" VARCHAR,
  "resolution_time" TIMESTAMP,
  "sla_for_resolution" VARCHAR,
  "close_time" TIMESTAMP,
  "agent_interactions" DOUBLE,
  "survey_results" DOUBLE,
  "product_group" VARCHAR,
  "support_level" VARCHAR,
  "country" VARCHAR,
  "latitude" DOUBLE,
  "longitude" DOUBLE
);