Customer Care Analytics: Centralized Self-Service Insight for a Loyalty Program

The major US airline is the world's third-largest airline, operating one of aviation's most asset-intensive, safety-sensitive, real-time networks. The a large application estate is anchored by a large microservice substrate, with thousands of apps involved in dispatching a single aircraft and hundreds of unique services triggered by one boarding pass. Loyalty (their loyalty program with tens of millions of active members), traveler commerce (their advertising platform, hundreds of millions of unique travelers, millions of daily website visitors), and a multi-year applied-AI program sit on top of the core airfare business. Taller's engagement at this airline has been continuous since March 2023, sourced through a staffing partner. The dominant footprint spans four AI / data workstreams that Volume II captures plus two .NET / Angular modernization workstreams. Multi-year SOW extensions have been confirmed across cohorts. Taller engineers placed at this airline have earned strong client-side recognition, with client stakeholders reporting strong satisfaction with team performance.

The customer-care function at this airline faced the familiar operational economics problem: a high volume of repetitive member inquiries that did not require an agent's judgment but consumed agent capacity. Without a self-service substrate, every additional loyalty program member converted directly into incremental contact-center cost.

A four-person Taller team stood up a Tibco Spotfire-based analytics workflow in under three weeks, centralizing customer-care data that had previously been distributed across operational systems. The team built Python and SQL pipelines against Oracle and SQL Server sources to push normalized event data into Spotfire's analytical layer. The choice to stand up an analytics workflow rather than a customer-facing application was deliberate — analytics enabled the operations team to validate self-service hypotheses before the product surface was rebuilt, with the same data spine then powering the chat-case-creation experience that actually reduces inbound volume.

The analytics workflow delivered a thirty percent reduction in agent dependency through self-service across the affected categories, with a significant decrease in call volume via chat-based case creation.

Self-service at their loyalty program surface is one of the levers that determines whether the airline's loyalty program scales economically. Centralizing customer-care data into a substrate that enables both analytics and self-service is the operational decision that lets the program grow without proportional contact-center cost — and the three-week delivery cadence is the property that converts an analytics investment into operational change at the pace the business actually requires.