Confidential Client · Multi-Region E-Commerce Brand
A growing e-commerce company had expanded across multiple markets, but its customer service processes, tooling, and cross-functional workflows had not fully scaled with that growth.
I was brought in to assess the customer service operation, identify the root causes behind recurring service issues, and design a more proactive support strategy for both normal and high-volume seasons.
My role: Customer Experience & Operations Consultant
Focus areas
Customer service operations · Zendesk · Process design · Cross-functional workflows · Capacity planning · Reporting
Methods
Stakeholder interviews · Workflow analysis · Zendesk review · Operational data analysis · Root-cause analysis
The Challenge
What I Found
From Symptoms to Root Causes
How I Approached the Problem
Recommendations
What I Built
What This Work Changes
The company’s customer service team was highly resourceful, but much of the operation had become reactive.
As the business expanded into multiple markets and fulfillment regions, support agents were increasingly absorbing problems created elsewhere in the operation: shipment delays, subscription and routing issues, launch timing changes, website errors, product information gaps, and unclear internal handoffs.
The result was a team that could solve problems quickly, but had limited ability to prevent them.
Leadership wanted better visibility into customer service performance, fewer recurring problems, and a clearer operating model for both normal and peak seasons.
Zendesk was being used as the team’s primary support platform, but inconsistent tagging, ticket statuses, assignment practices, and macro usage made it difficult to understand what was driving customer contacts.
The team was resolving issues, but the underlying data did not consistently show why customers were contacting support, where work was accumulating, or which problems were repeating.
Business impact
Leadership wanted measurable customer service performance and issue trends, but inconsistent data practices limited reporting quality.
Many of the issues appearing in the support queue did not originate within customer service.
Recurring contacts were connected to:
The support team could respond to these issues, but often lacked the capacity, visibility or authority needed to prevent them.
Business impact
Agent capacity was being used to manage avoidable demand instead of proactive customer experience improvements.
Product and subscription launches required coordination across customer service, marketing, technical operations, fulfillment, inventory, website management, and international partners.
Teams were completing their individual responsibilities, but there was no consistently used operating system connecting dependencies, deadlines, readiness checks, and escalation paths.
When inventory or shipping timelines changed, customer service often became responsible for managing the downstream consequences.
Business impact
Predictable launches became recurring periods of operational stress, elevated support volume, and last-minute coordination.
The team was strong at troubleshooting.
When an issue appeared, someone usually found a way to resolve it.
But recurring issues were not consistently categorized, documented, or moved into a structured improvement process.
This meant the organization could successfully solve the same class of problem multiple times without eliminating the underlying cause.
Business impact
Institutional knowledge remained distributed across individuals rather than becoming reusable systems and processes.
One of the most important parts of the project was separating the visible customer service problem from the operational cause underneath it.
What I found:
Some support volume was being created by preventable operational failures. Improving response time alone would not solve the underlying problem.
What I found:
The reporting problem began earlier in the workflow. Inconsistent tagging, statuses, assignment, and ticket handling reduced the quality of the data going into the system. The wrong reports were being reviewed.
What I found:
The company lacked a consistently used cross-functional launch process connecting owners, dependencies, deadlines, testing, and readiness criteria.
What I found:
The problem was not simply communication frequency. Teams lacked consistent rules for ownership, escalation channels, urgency, response expectations, and follow-up.
What I found:
Capacity had to be evaluated alongside preventable demand. Adding people would create breathing room, but it would not eliminate operational issues continuously generating support contacts.
I interviewed stakeholders across customer service, operations, technology, and fulfillment to understand how work actually happened day to day.
Rather than relying only on documented processes, I compared how different people described the same workflows, pain points, and escalation paths.
I reconstructed key operational workflows, including:
This helped reveal where customer service depended on information or actions owned by other teams.
I looked for patterns across interviews, workflows, Zendesk practices, ticket handling, staffing, and recurring operational issues.
The goal was to distinguish isolated incidents from structural problems.
I translated those findings into practical recommendations around:
Standardize ticket handling so customer service data can support operational decision-making.
Recommendations included:
Goal: Turn Zendesk from primarily an inbox into a reliable source of customer and operational intelligence.
Create one shared launch process across teams.
The proposed model included:
Goal: Shift launches from individual teams coordinating informally to a visible, repeatable operating process.
Define how customer-impacting issues move between customer service, operations, technical teams, and external partners.
The process should answer:
Goal: Reduce delays, duplicate communication, and risky workarounds during high-pressure situations.
Create a lightweight process for capturing recurring issues after the immediate customer problem is resolved.
Each recurring issue should connect:
Customer contact → Issue category → Root cause → Operational owner → Corrective action
Goal: Help the company identify which customer contacts should be handled more efficiently and which should be prevented entirely.
A structured review of the team’s workflows, responsibilities, systems, dependencies, and recurring service problems.
Recommendations for improving tagging, statuses, ticket ownership, macros, reporting, and consistent team usage.
A repeatable cross-functional structure for coordinating launches, dependencies, testing, readiness, and escalation.
A model for changing staffing priorities, workflows, reporting, and improvement work based on expected demand.
A focused set of measures designed to help leadership understand support demand, service performance, backlog, recurring issues, and operational risk.
From
Customer service reacting to recurring problems
To
Customer service identifying patterns and helping prevent future demand
From
Operational knowledge living across individual team members
To
Shared workflows, documentation, ownership, and escalation standards
From
Zendesk functioning primarily as an inbox
To
Zendesk providing usable operational insight
From
Launch-day scrambling
To
Shared readiness criteria and visible dependencies
From
Adding capacity whenever support becomes overwhelmed
To
Understanding which demand should be staffed for and which demand should be eliminated
I’m most useful when a business problem crosses functional boundaries and does not have an obvious owner.
I can enter an unfamiliar environment, understand how work actually happens, identify patterns across people, process, technology, and data, and turn those findings into practical operational improvements.
The work is not just about fixing individual customer service problems.
It is about building systems that make those problems less likely to happen again.