Data-rich Tableau dashboards allowed the telecom provider reduce shipping times across the US

The Challenge

Wing, a New York-based telecom provider, was managing a complex operation spanning the entire United States. Their core challenge was threefold: they were issuing thousands of credits monthly with no systematic way to audit them for discrepancies or fraud, they lacked visibility into insurance plan allocation and ARPU at a granular geographic level, and their shipment logistics were suboptimal, with no data-driven approach to reducing transit times.

The data existed — scattered across internal databases and operational systems — but Wing’s team lacked the analytics expertise and bandwidth to transform raw data into visual, actionable dashboards. They needed a partner who could both architect the analytical layer and build production-quality Tableau dashboards that multiple stakeholders could use for daily decision-making.

The operational complexity was compounded by the geographic spread of Wing’s business. With customers across multiple US states, each with different regulatory environments and shipping logistics, the data needed to support both company-wide views and state-level drill-downs. Previous attempts at using Excel for this analysis had failed due to the volume and complexity of the data — over 50,000 monthly transactions across credit issuance, insurance plans, and shipments.

Our Approach

We started with a deep-dive into Wing’s data landscape. After a series of stakeholder interviews with their operations, finance, and marketing teams, we mapped out the key questions each group needed answered and the data sources required to answer them.

Our approach followed three tracks executed in parallel:

• Credits & Fraud Analytics: We built an Owed and Applied Credits dashboard showing the number of credits issued per month, including applied percentage and unique credits. This allowed the team to quickly identify anomalies — unusual credit volumes by agent, region, or time period — that could signal fraudulent activity or process failures.
• Revenue & Insurance Analytics: We created state-level and zip-code-level dashboards for insurance plan allocation, ARPU analysis, and total revenue breakdown. This gave the marketing team the geographic intelligence they needed to refine targeting and identify high-value demographic segments.
• Shipment Optimization: We visualized shipment allocation patterns and transit times across US regions. The dashboards highlighted specific routes and locations where shipping delays were concentrated, enabling data-driven optimization of supply chain logistics.

All dashboards were built in Tableau with a focus on usability: interactive filters, drill-down capabilities, and clean visual design that non-technical stakeholders could navigate independently.

A critical design decision was building all dashboards with consistent navigation patterns and visual language, so stakeholders from different departments could move between credit analytics, revenue analysis, and shipment optimization without relearning the interface. We also implemented data quality checks at the ingestion layer to flag anomalous records before they reached the dashboards, preventing the “garbage in, garbage out” problem that had plagued earlier spreadsheet-based analysis attempts.

Results

• Comprehensive Tableau dashboard suite covering credits, insurance, revenue, and shipment analytics.
• Data-driven insights into optimal shipment locations, contributing to reduced transit times across the US network.
• Fraud detection capability through automated credit anomaly identification, reducing manual audit burden.
• Geographic revenue intelligence enabling the marketing team to allocate budget to the highest-ROI regions.
• Self-service analytics empowering non-technical stakeholders to explore data without depending on engineering support.

Technologies Used

Tableau, SQL databases, Python for data preparation, AWS infrastructure.

Project Screenshots