Business Intelligence / Customer Analytics
Customer Support Analytics & Root Cause Identification
A reporting framework and case-classification process built to analyze support trends, operational performance, and recurring customer challenges across a large customer support organization. The work connected every case from the symptom a customer reported, through its true root cause, to the resolution that closed it - turning a flat case log into a decision tool.
The problem
The support organization handled tens of thousands of cases each quarter across multiple operational areas - account access, onboarding, configuration, and product usage. Leadership could see how many cases came in and how fast they closed, but not why they kept coming. Recurring login and access complaints in particular were widely assumed to be software defects and were being escalated to engineering as such. Cases were logged with free-form symptoms and inconsistent category labels, with no shared vocabulary linking what a customer said ('I can't log in') to what was actually wrong (an onboarding step never completed) or what finally fixed it (a walkthrough, not a code change). Without that chain, the same issues recurred quarter after quarter and the underlying cause stayed invisible.
Approach
- 01
Designed a three-layer classification taxonomy applied to every case: Layer 1 captured the customer's reported symptom, Layer 2 captured the underlying root cause, Layer 3 captured the resolution that closed it. A fourth conceptual layer - business insight - translated patterns into recommended organizational action.
- 02
Coalesced category-specific symptom, root-cause, and resolution fields into a single consistent set so any case could be sliced by any layer - making the data uniformly analyzable for the first time.
- 03
Distinguished automated internal processing (third-party roster integrations, system-generated notifications) from genuine customer-driven causes, so reports could surface real friction rather than internal noise.
- 04
Built six interconnected Tableau views on top of the classified data: executive KPI strip, category distribution, weekly volume trend, root-cause ranking, resolution ranking, and a symptom-to-cause-to-resolution flow view.
- 05
Established the framework as a recurring quarterly read-out so trends could be tracked over time rather than re-discovered each season.
Findings
Two operational categories - provisioning and access - together accounted for the majority of all support volume. Provisioning was largely automated processing; access was where genuine customer friction lived.
Case volume showed a strong seasonal pattern, with onboarding cycles driving a multi-fold spike over baseline. The surge was dominated by access and provisioning, which made improving the customer experience for those categories the highest-leverage opportunity.
Once internal automated processing was set aside, the leading customer-driven root causes were process questions, not knowing how to access the product, and password confusion - instruction problems, not software faults.
After roster adjustments, the single largest human resolution was user education - walking a customer through a step. Education-based resolutions accounted for roughly a fifth of all human-touched volume: no code shipped, an instruction delivered.
Drilling into access cases specifically: the majority traced to onboarding or password issues, while only a small minority were genuine authentication defects. The product-defect assumption was wrong by an order of magnitude.
The key finding
The reframe
What changed when symptoms were separated from causes.
Onboarding
Where the majority of access complaints actually originated
Education
The most common human resolution - a walkthrough, not a fix
Engineering
Where escalations had been heading before the reframe
Finding · Login and access complaints had been treated as a product-reliability problem. The classification showed the opposite - they were mostly customers who didn't know how to access the product, had the wrong password, or lacked the right permissions. The number-one way these cases closed was user education: a walkthrough, not a code change. That reframe redirected onboarding and documentation effort toward the failure points that actually moved the numbers.
What's next
The classification framework was designed to be repeatable across quarters so trends could be tracked rather than re-discovered each cycle. Natural extensions include time-to-resolution by root cause (to surface which causes drag on operational metrics most), and a customer-segment view to identify whether onboarding gaps cluster in specific account types or product configurations.