CASE STUDIES

Real problems, real dashboards, real recovery

Two projects from my work as a KPI specialist for an outsourced customer service operation supporting a large international brand. Details are anonymized (organization, exact figures, and identifying specifics disguised) but the methodology, dashboard structure, and approach are exactly what I built.

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Case Study 1: Finding the Hidden Driver Behind a Declining Resolution-Time KPI

Client context: A customer service operation delivering outsourced support on behalf of a large, internationally known brand, across dozens of markets, working under strict internal IT and data-governance constraints (all reporting built on Excel/Access-fed data flowing into a Power BI semantic model, no external data movement allowed).

The challenge

The operation’s core efficiency KPI, Case Resolution Time (CRT%), tracked separately for escalated and non-escalated cases, had been sliding for two consecutive months. It was affecting both customer segments, though unevenly: the Premium segment was down in about 4 markets and still sitting in a neutral band (below bonus threshold but not yet in penalty territory), while the Standard segment was down in roughly 8 markets and had dropped deep into penalty territory. Leadership needed to know which underlying KPIs were actually driving the decline, out of dozens of candidate metrics being tracked, before committing resources to a fix.

What I built

A diagnostic correlation and driver-analysis dashboard in Power BI, connected to the operation’s Fabric-based semantic model. The core of it: a rolling 52-week correlation engine comparing CRT% against roughly 8 candidate driver KPIs, weighted by case volume per market so that high-volume markets couldn’t be drowned out by noisy low-volume ones. The output was a matrix, one axis by driver KPI, the other by market and segment, with conditional formatting turning it into an at-a-glance heatmap. Behind each cell sat a DAX measure that didn’t just show a correlation coefficient, it returned a plain-language verdict, “No effective correlation,” “Watch,” or “Critical: urgent action,” so a non-technical stakeholder could read the matrix in seconds without interpreting statistics. Full slicer control across time, market, LOB and segment let any manager drill into their own scope.

The outcome

Instead of managers manually cross-referencing dozens of KPIs against CRT% market by market, the matrix immediately surfaced a short list of driver/market combinations flagged “Critical,” turning a wide, ambiguous investigation into a prioritized action list. This dashboard became the entry point for the root-cause work described below.

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Case Study 2: From Root Cause to a 10-Week Recovery Plan

Client context: Same operation, same KPI. Following the diagnostic work above, I was assigned as the internal specialist to own CRT% recovery.

The challenge

Diagnosing the problem wasn’t enough, leadership needed a credible, documented plan to actually reverse it, across markets and segments that each had different underlying causes. That meant getting buy-in and real operational detail from multiple Operational Managers who didn’t report to me, running a structured investigation to find true root causes rather than symptoms, and turning findings into a plan leadership could hold the business accountable to.

What I built

I ran the project under a DMAIC framework (Define, Measure, Analyze, Improve, Control), with full supporting documentation at each stage. Working directly with the Operational Managers across affected teams, I planned and ran a structured case-scrubbing exercise, a manual review of individual case records, to pin down the actual root causes market by market rather than relying on aggregate KPI trends alone. That produced segment- and market-specific action plans covering nearly every affected market. Each action plan was then tied to a modeled glidepath, a projected week-by-week recovery trajectory for CRT%, first built in Excel to work through the logic with stakeholders, then rebuilt in Power BI as a live tracking dashboard comparing actual performance against the planned trajectory.

The outcome

Leadership got a defensible, documented recovery plan instead of an open-ended “we’re working on it,” with a live dashboard showing real progress against target every week. Over the 10-week glidepath, the KPI recovered by roughly 2 percentage points, a meaningful move for a metric that had been sliding for two straight months, and the tracking dashboard gave the business early visibility into which markets were on plan and which needed a second look.

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