Analysis overview and configuration
| Parameter | Value | _row |
|---|---|---|
| sla_thresholds | 4,8,24,48 | sla_thresholds |
| time_unit | hours | time_unit |
| resolution_bins | 0,1,4,8,24,48,72,168 | resolution_bins |
| min_group_size | 5 | min_group_size |
| significance_level | 0.05 | significance_level |
| alpha | 0.05 | alpha |
This SLA analysis evaluates support ticket resolution performance across 2,769 resolved tickets, examining compliance rates, resolution speed, and operational bottlenecks. The analysis identifies which priority levels, ticket types, channels, and customer segments experience the highest SLA breaches to guide operational improvements.
The analysis reveals a priority-driven performance crisis. Critical tickets breach at 2.3× the overall rate, while Medium and Low priorities show zero breaches, suggesting SLA targets may be misaligned with actual capacity. The weak satisfaction-resolution correlation
Data preprocessing and column mapping
| Metric | Value |
|---|---|
| Initial Rows | 8,469 |
| Final Rows | 2,769 |
| Rows Removed | 5,700 |
| Retention Rate | 32.7% |
This section documents the data filtering applied to the ticket dataset before SLA analysis. The 67.3% row removal rate is substantial and directly impacts the scope of conclusions—the analysis reflects only resolved tickets, excluding unresolved cases that may exhibit different SLA patterns. Understanding this preprocessing is critical for assessing whether findings generalize to the full ticket population.
The heavy filtering toward resolved tickets creates a survivorship bias in the SLA analysis. The 29% breach rate and 7.74-hour average resolution reflect only completed cases; unresolved tickets may have different characteristics (longer durations, higher complexity). This preprocessing choice aligns with the stated objective to analyze SLA performance on closed tickets, but excludes insights into tickets stuck in workflow or pending closure—potentially the most problematic cases.
The absence of a documented train/test split suggests this
| category | finding | impact |
|---|---|---|
| Overall SLA | Overall SLA breach rate is 29.0% (802 of 2769 resolved tickets) | Medium |
| Resolution Speed | Median resolution time is 6.7 hours (P90: 16.1 hours) | Info |
| Priority | Critical priority has highest breach rate at 66.3% (726 tickets) | High |
| Ticket Type | Cancellation request tickets have highest breach rate at 31.4% | High |
| Top Bottleneck | Priority: Critical has highest impact score (481.3): 66.3% breach rate across 726 tickets | High |
| Ticket Subject | Hardware issue has highest breach rate at 36.1% (183 tickets) | High |
| Demographics | Age group 36-45 has highest breach rate at 29.9% (532 tickets) | Info |
| Satisfaction | Average satisfaction rating is 2.99 out of 5.0 | Info |
This analysis evaluates ticket resolution performance against Service Level Agreement (SLA) targets across 2,769 resolved tickets. The objective is to identify compliance gaps and understand which operational factors drive SLA breaches, enabling targeted improvement efforts.
The 29% breach rate reflects a structural misalignment between SLA targets and operational capacity
Key SLA performance indicators including breach rate, resolution time statistics, and overall compliance metrics
| metric | value |
|---|---|
| Total Tickets (Resolved) | 2769 |
| SLA Breaches | 802 |
| Breach Rate (%) | 29% |
| Avg Resolution (hrs) | 7.74 |
| Median Resolution (hrs) | 6.7 |
| P90 Resolution (hrs) | 16.15 |
| Std Dev (hrs) | 5.61 |
This section establishes the baseline SLA performance across all resolved tickets, providing a high-level view of compliance and resolution efficiency. Understanding these aggregate metrics is essential for identifying whether systemic issues exist and for contextualizing performance variations across priority levels, ticket types, and channels examined in subsequent analyses.
The 29% breach rate represents a significant operational gap, with roughly 1 in 3 tickets failing to meet SLA commitments. The tight clustering around the median (6.7 hours) suggests most tickets resolve predictably, but the P90 threshold reveals a meaningful subset experiencing substantially longer resolution cycles. This distribution pattern indicates the breach problem is not uniform
Distribution of ticket resolution times across time buckets to identify where most tickets cluster
This section reveals how ticket resolution times cluster across the organization, identifying whether most tickets resolve quickly or if significant delays are common. Understanding this distribution is critical for assessing SLA compliance patterns and identifying whether the 29% breach rate stems from systemic delays or isolated problem cases.
The distribution reveals a bimodal tendency: a core group resolving efficiently within 8 hours, and a substantial tail (42%) extending into longer timeframes. This explains the 29% overall breach rate—while median performance is solid, the extended tail creates systematic SLA violations, particularly for priority levels with tight targets (Critical: 4-hour SLA). The moderate standard deviation masks the impact
SLA breach rate and resolution time performance broken down by ticket priority level
This section evaluates SLA compliance across four priority tiers to identify which ticket categories are meeting contractual resolution targets. Since different priorities have distinct SLA windows (Critical: 4 hours, High: 8 hours, Medium: 24 hours, Low: 48 hours), this breakdown reveals whether the organization's response capacity aligns with urgency levels and where performance gaps exist.
The data reveals a critical misalignment: tickets marked Critical are breaching SLA at two-thirds the rate, indicating the organization cannot consistently meet its most stringent commitments. Medium and Low priorities achieve perfect compliance, suggesting adequate capacity exists for longer windows but insufficient resources for rapid turnaround. The
Ticket volume and SLA breach rate analysis by ticket type to identify problematic categories
This section identifies which ticket types are most problematic for SLA compliance. By comparing breach rates and resolution times across five ticket categories, it reveals whether certain issue types systematically exceed service level targets—indicating potential process gaps, complexity mismatches, or resource allocation problems that warrant investigation.
The 4.4-percentage-point variance between best and worst performers is modest relative to the overall 29% breach rate, suggesting ticket type is not the primary SLA driver. However, Cancellation and Refund requests consistently underperform, correlating with longer resolution times. This pattern indicates these types may require more complex decision-making, customer interaction, or backend processing rather than simple routing or staff
SLA breach rate and satisfaction analysis by specific ticket subject/issue type
This section identifies which specific ticket subjects (issue types) drive SLA breaches and resolution delays. By analyzing 16 distinct subjects, it reveals whether performance problems are concentrated in particular issue categories or distributed broadly—critical for understanding whether targeted process improvements can meaningfully reduce overall breach rates.
Hardware issues represent a concentrated performance problem: they account for 183 tickets with disproportionately high breach rates and resolution times. This suggests specific technical or diagnostic challenges inherent to hardware troubleshooting. The weak correlation between breach rate and satisfaction indicates customers may accept longer resolution times for complex issues
Customer demographics analysis showing SLA breach rates and satisfaction by age group
This section examines whether SLA performance and customer satisfaction vary meaningfully across age demographics. By segmenting the 2,769 resolved tickets into five age groups, it reveals whether certain customer cohorts experience systematically different resolution times or breach rates—critical for identifying whether service quality is equitable across the customer base.
Age demographics show remarkably homogeneous SLA performance, with breach rates hovering within 1.7 percentage points and satisfaction ratings virtually identical (0.09-point range). The 46-55 cohort experiences slightly longer resolution
Performance comparison across support channels (email, phone, chat, social media)
This section evaluates SLA performance consistency across four support channels (Chat, Email, Phone, Social Media). Understanding channel-level compliance reveals whether operational challenges are systemic or channel-specific, informing resource allocation and workflow optimization decisions.
Channel performance is remarkably homogeneous, with all four channels clustering around the 29% organizational breach rate. This uniformity suggests that SLA breaches are driven by systemic factors (priority levels, ticket complexity, staffing constraints) rather than channel-specific inefficiencies. The slight variations in resolution speed do not translate to meaningful breach rate differences, indicating that speed alone does not determine compliance.
These findings align with the overall 29% breach rate and suggest that channel optimization alone will
Relationship between resolution speed and customer satisfaction ratings
This section quantifies the relationship between how quickly tickets are resolved and customer satisfaction levels. Understanding this connection is critical for evaluating whether SLA performance improvements directly translate to better customer experience—a key business objective for operations optimization.
The near-zero correlation reveals a critical insight: resolution speed alone is not driving customer satisfaction. Despite 42% of tickets resolving within 8–25 hours, satisfaction remains flat at ~3.0 across all timeframes. This suggests underlying issues—such as solution quality, communication clarity, or unmet expectations—are more influential than speed. The slight uptick in satisfaction for slower resolutions contradicts typical expectations and warrants investigation into what differentiates those cases.
This analysis assumes satisfaction ratings are comparable across resolution buckets and channels. The weak correlation does not eliminate
SLA breach rate analysis by product or service category
This section examines SLA compliance across 42 product categories to identify which products generate the most support burden and breach risk. Understanding product-level performance reveals whether certain products have inherent complexity, quality issues, or documentation gaps that drive higher ticket volumes and SLA failures—critical for prioritizing product improvements or support resource allocation.
Product-level breach rates exceed the overall 29% baseline, revealing that specific products drive disproportionate SLA failures. The 11.3-percentage-point spread (30.9%–42.2%) suggests product complexity, support documentation quality, or
Statistical hypothesis tests for significance of performance differences across categories
| test_name | statistic | p_value | effect_size | interpretation |
|---|---|---|---|---|
| ANOVA (Resolution by Priority) | 3.01 | 0.029 | 0.0033 | Significant |
| Kruskal-Wallis (Resolution by Priority) | 7.558 | 0.0561 | Not significant | |
| Chi-Square (Breach by Type) | 2.546 | 0.6365 | Not significant |
This section validates whether observed differences in SLA resolution times across priority levels are statistically significant or attributable to random variation. Statistical testing is critical for distinguishing genuine performance patterns from noise, ensuring that resource allocation decisions are based on real operational differences rather than sampling artifacts.
The ANOVA confirms that Critical priority tickets resolve differently than other tiers—a real operational phenomenon. However, the negligible effect size reveals this difference is modest in practical magnitude. While Critical tickets show a 66.3% breach rate versus 0% for Medium/Low, the actual resolution time differences (7.56 vs 7.36 hours) are small. This suggests priority classification influences S
Top bottleneck categories ranked by impact score (breach rate x volume) to prioritize improvement efforts
This section identifies which ticket categories contribute most significantly to overall SLA breaches by combining breach rate with ticket volume. The impact score reveals where operational improvements would yield the greatest reduction in the current 29% breach rate, enabling prioritized resource allocation across priority levels, channels, and ticket types.
The data reveals a priority-driven problem: Critical and High priority tickets account for the top two impact scores (802.1 combined), while lower priorities show zero breaches. This concentration indicates SLA targets for critical tickets are either misaligned with achievable resolution times