Connect Zendesk to Scoop to analyze your support operations, track ticket resolution patterns, and understand service quality across your customer base. Snapshot tickets to see exactly how they progress through your support workflow, measure response times, and identify areas for improvement.

What You Can Analyze

Analysis Type	Questions Answered
Volume	How many tickets are we handling? What are the trends?
Resolution Time	How long does it take to resolve issues?
First Response	Are we meeting SLA for initial response?
Agent Performance	How are individual agents performing?
Customer Patterns	Which customers need the most support?
Channel Mix	Where are tickets coming from (email, chat, phone)?

Connecting Zendesk to Scoop

Option 1: Native Connector

Create a new dataset in Scoop
Select Zendesk from the application list
Enter your Zendesk subdomain
Authenticate with admin credentials
Select objects to sync (tickets, users, organizations)

Option 2: Scheduled Email Reports

Configure Zendesk to email reports to Scoop:

In Zendesk, create a report in Explore
Schedule the report for email delivery
Use your Scoop dataset email as recipient
Set frequency (daily recommended for snapshot analysis)

Option 3: CSV Export

For one-time or ad-hoc analysis:

Export tickets from Zendesk as CSV
Upload to Scoop manually
Process and analyze immediately

Recommended Data to Extract

Essential Fields

Field	Analysis Use
Ticket ID	Unique identifier for snapshotting
Status	Track lifecycle (New → Open → Pending → Solved)
Created At	Volume trends and aging analysis
Solved At	Resolution time calculation
Priority	Urgency distribution
Assignee	Agent workload analysis
Requester	Customer pattern analysis

Valuable Additional Fields

Field	Analysis Use
Organization	Customer-level analysis
Tags	Issue categorization
Channel	Source analysis (email, chat, phone)
First Reply Time	SLA monitoring
Satisfaction Rating	Quality measurement
Group	Team-based analysis
Ticket Form	Request type segmentation

Setting Up Snapshot Analysis

For powerful process analysis, configure your Zendesk dataset as a Snapshot type:

Recommended Filter

Include tickets that are either open or recently resolved:

status:open OR status:pending OR status:new OR solved>2days

This captures:

All currently active tickets
Recently solved tickets (to record final state change)

Key Metrics Enabled

With snapshotting, you can analyze:

Metric	What It Shows
Status conversion rates	% of tickets reaching each status
Average time in status	Days spent in each stage
Resolution trends	Tickets solved over time
Aging analysis	Tickets open too long
Escalation patterns	How tickets move through teams

See Snapshot Datasets for setup details.

Analysis Examples

Support Operations Dashboard

Track overall support health:

Tickets created vs. resolved
Average resolution time
Open ticket backlog
SLA compliance rates

Agent Performance

Understand individual and team performance:

Tickets handled per agent
Average resolution time by agent
First response time
Customer satisfaction scores

Customer Analysis

Identify support patterns by customer:

Tickets per organization
Most common issues by customer
High-touch vs. low-touch customers
Support cost by account

Channel Effectiveness

Compare support channels:

Volume by channel (email, chat, phone)
Resolution time by channel
Satisfaction by channel
Cost per interaction

Blending Zendesk with Other Data

Zendesk becomes even more powerful when combined with other sources:

Zendesk + CRM Data

Goal: Calculate cost to serve and identify high-maintenance customers

Zendesk Field	CRM Field	Insight
Organization	Account ID	Match tickets to accounts
Ticket count	ARR	Support cost ratio
Resolution time	Customer tier	SLA alignment

Zendesk + Product Usage

Goal: Correlate support needs with product adoption

Do power users need less support?
Which features generate the most tickets?
Is there correlation between usage and satisfaction?

Zendesk + Financial Data

Goal: Understand true cost of customer support

Cost per ticket resolved
Support cost as % of revenue
ROI on support improvements

Key Performance Indicators

Volume Metrics

KPI	Formula	Target Example
Tickets Created	Count of new tickets	Trending down
Tickets Solved	Count of solved tickets	> Created
Backlog	Open tickets	< 100

Time Metrics

KPI	Formula	Target Example
First Response Time	Created → First Reply	< 1 hour
Resolution Time	Created → Solved	< 24 hours
Time in Status	Entry → Exit	Varies by status

Quality Metrics

KPI	Formula	Target Example
CSAT	Satisfied / Total Rated	> 90%
First Contact Resolution	Solved without handoff	> 70%
Reopening Rate	Reopened / Total Solved	< 5%

Best Practices

Data Hygiene

Use consistent tagging for issue categories
Ensure organizations are properly linked to tickets
Maintain accurate assignee information

Snapshot Frequency

Daily for active support operations
Consider more frequent for high-volume environments

Field Selection

Include all fields needed for analysis
Add custom fields containing business context
Consider privacy when including customer data

Troubleshooting

Missing Tickets

Check your Zendesk filter includes all needed statuses
Verify API permissions allow ticket access
Confirm date range covers expected data

Duplicate Records

Ensure Ticket ID is recognized as unique identifier
Check for test vs. production data mixing

Status Changes Not Tracking

Confirm dataset type is "Snapshot"
Verify daily data loads are occurring
Check filter includes recently solved tickets

Organization Not Matching

Verify organization field is included in export
Check for naming inconsistencies between systems
Consider using organization ID for reliable matching

Snapshot Datasets - Track ticket state changes
Process Analysis - Visualize support flows
Blending Datasets - Combine with CRM/financial data
CRM Writeback - Push support metrics to CRM

Zendesk