Trend Analysis

Identify patterns and predict future support needs.

Understanding Trends

What Trends Show

• How metrics change over time
• Patterns and cycles
• Growth or decline
• Impact of changes

Why Trends Matter

• Plan staffing levels
• Identify problems early
• Measure improvement efforts
• Set realistic goals

Volume Trends

Ticket Volume Over Time

Track tickets created across:

• Days (short-term patterns)
• Weeks (medium-term trends)
• Months (seasonal patterns)

What to Look For

Growth: Steady increase may indicate:

• Business growth
• Product issues
• Inadequate self-service

Decline: Steady decrease may indicate:

• Improved product quality
• Better self-service
• Seasonal slowdown

Spikes: Sudden increases may indicate:

• Product launch/update
• Outage or bug
• Marketing campaign
• Seasonal events

Patterns: Regular cycles may indicate:

• Day-of-week patterns (Mondays heavy)
• Monthly patterns (billing cycles)
• Seasonal patterns (holiday shopping)

Response Time Trends

First Response Time

Track how quickly your team responds:

• Improving = responses getting faster
• Degrading = responses slowing down

Factors Affecting Response Time

Identifying Issues

If response times trend upward:

1. Check if volume increased
2. Check if staffing decreased
3. Check if issue complexity changed
4. Check if assignments are working

Resolution Trends

Time to Resolution

How long tickets take to close:

• Shorter = more efficient
• Longer = more complex or blocked

Resolution Rate

Percentage of tickets resolved:

• Higher = team is keeping up
• Lower = backlog growing

Backlog Trend

Open ticket count over time:

• Growing = falling behind
• Shrinking = catching up
• Stable = sustainable pace

SLA Trends

Achievement Rate Over Time

Track SLA performance:

• Improving = better service delivery
• Degrading = capacity or process issues

Breach Trends

Track breaches over time:

• Increasing = investigate causes
• Decreasing = improvements working

By SLA Type

Compare trends across:

• First response SLA
• Resolution SLA
• By priority level

Satisfaction Trends

CSAT Over Time

Customer satisfaction trajectory:

• Improving = happier customers
• Declining = experience issues

Correlations

Look for relationships:

• Does CSAT drop when response times increase?
• Does CSAT improve after product fixes?
• Do certain agents have higher CSAT trends?

Comparison Views

Period Comparison

Compare two time periods:

• This week vs. last week
• This month vs. last month
• This quarter vs. last quarter

Year-over-Year

Compare same period across years:

• December 2024 vs. December 2023
• Accounts for seasonal patterns

Before/After

Measure impact of changes:

• Before new hire vs. after
• Before automation vs. after
• Before product fix vs. after

Trend Alerts

Anomaly Detection

System can alert on:

• Sudden volume spikes
• Response time degradation
• CSAT drops

Setting Thresholds

Configure alerts for:

• Volume > X tickets/hour
• Response time > X hours
• CSAT drops below X%

Forecasting

Volume Prediction

Based on historical patterns:

• Expected tickets next week
• Expected tickets next month
• Seasonal predictions

Use Cases

Staffing planning:

• Predict holiday volume
• Plan vacation coverage

Capacity planning:

• When to hire
• When to implement automation

Creating Trend Reports

Step by Step

1. Go to Analytics → Trends
2. Select metric to analyze
3. Choose time range
4. Apply any filters
5. View trend visualization

Available Metrics

• Ticket volume
• Response times
• Resolution times
• SLA achievement
• Customer satisfaction
• Agent performance

Granularity

View trends by:

• Hour (last 48 hours)
• Day (last 90 days)
• Week (last year)
• Month (historical)

Exporting Trend Data

Chart Export

Download charts as images for presentations.

Data Export

Download raw data as CSV for:

• Further analysis
• Custom visualizations
• Executive reporting

Scheduled Reports

Automate trend reports:

• Weekly trend summary
• Monthly performance review
• Quarterly business review

Best Practices

Establish Baselines

Before analyzing trends:

1. Collect at least 30 days of data
2. Identify normal patterns
3. Document baseline metrics

Look for Root Causes

When trends change:

1. Identify when change started
2. Look for correlating events
3. Investigate before acting

Track Improvement Efforts

When making changes:

1. Document what changed
2. Monitor trend impact
3. Allow time for effect
4. Measure results

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