In modern customer operations, data is everywhere—but insights that truly improve performance are rare. Teams often focus on surface metrics such as average handle time (AHT), repeat contact rates, and customer satisfaction scores without understanding why those numbers move.
This is where root cause analytics becomes essential. It goes beyond measuring outcomes to uncovering the underlying drivers of inefficiency, customer frustration, and operational waste.
Understanding Root Cause Analytics
Root cause analytics (RCA) is a structured approach to identifying the fundamental reasons behind performance trends or recurring issues. In customer operations, it uses advanced analytics and conversation data to move from “what happened” to “why it happened.”
For instance, a high AHT might initially appear as an agent productivity issue—but the real cause could be outdated workflows, confusing knowledge bases, or customers being transferred between multiple departments. RCA helps isolate these drivers through a combination of data science, qualitative review, and process mapping.
Instead of guessing, decision-makers gain concrete, data-backed evidence to prioritize actions that deliver measurable improvement.
Why AHT and Repeat Contacts Need Deeper Insight
Average Handle Time (AHT) and repeat contacts are two of the most widely tracked KPIs in customer service. Yet both can be misleading when evaluated without context.
1. Average Handle Time (AHT):
A lower AHT is not always better. Rushing through interactions may reduce handle time but can also increase repeat calls if customer issues are not fully resolved.
2. Repeat Contacts:
When customers repeatedly contact support, it often signals broken processes, unclear communication, or system limitations—not necessarily poor agent performance.
By applying root cause analytics, operations teams can connect patterns across multiple data sources—call transcripts, CRM logs, agent notes, and ticket resolutions—to identify which factors truly drive these metrics.
Common Root Causes Behind High AHT and Repeat Contacts
Root cause analysis often reveals that what appears to be a frontline performance problem is systemic. Some of the most frequent contributors include:
1. Complex Processes and Disconnected Systems:
Agents spend too much time navigating multiple platforms or seeking approvals. Automating key workflows or integrating systems can drastically reduce AHT.
2. Inadequate Knowledge Management:
When support teams lack quick access to accurate, updated information, it slows resolution and increases repeat interactions.
3. Product or Policy Confusion:
Customers frequently call back when policies are unclear or when product issues recur. Analytics can pinpoint common query themes that indicate unclear documentation or design flaws.
4. Poor Handoffs Between Departments:
Transferring customers between departments often extends handle time and increases frustration. RCA can identify which transfer paths are most problematic.
5. Training Gaps and Coaching Needs:
Inconsistency in call handling or empathy levels across agents can show up as AHT variation. Root cause analytics helps identify which behaviours most impact success rates.
6. Turning Insights into Action
Root cause analytics is only valuable if it leads to action. Once key drivers are identified, leaders can prioritize interventions based on impact and feasibility.
Conclusion
This approach transforms AHT and repeats contact metrics from lagging indicators into tools for continuous learning and operational excellence. The result is faster resolutions, happier customers, and a more efficient support operation built on insight, not assumption.
In a data-driven world, understanding the “why” behind performance is no longer optional—it is the foundation of every high-performing customer service team.
