T-Mobile Store Locator @ T-Mobile

T-Mobile Store Locator & Local Commerce Experience

Role: Technical SEO & Product Operations Lead

Company: T-Mobile

Scope: ~6,000 retail locations nationwide

Tools: Quantum Metric, Notify, Google Business Profile, CMS tooling, internal experimentation frameworks

Overview

At T-Mobile, I led the optimization and operational management of the Store Locator and Local Store Experience, supporting nearly 6,000 physical retail locations across the United States. This surface was a critical intersection between local discovery, e-commerce conversion, and in-store activation, directly influencing both online revenue and foot traffic.

My work focused on scaling UX improvements, standardizing location data, and building custom local experiences that increased engagement and improved downstream conversion metrics—particularly Add to Cart and Store Visit intent.

The Problem

T-Mobile’s store locator ecosystem faced several structural challenges:

• Massive scale: Thousands of store pages with inconsistent data, layouts, and user behavior patterns.
• Fragmented ownership: UX, SEO, engineering, analytics, and retail stakeholders all touched the experience.
• Conversion leakage: Users frequently dropped off between store discovery and meaningful action (Add to Cart, appointment intent, or in-store visit).
• Limited visibility into UX friction at a granular, per-location level.

The challenge was not just improving a page—it was operating a national retail experience as a product.

My Role & Responsibilities

I operated as the central owner and integrator across product, SEO, analytics, and engineering for the store locator ecosystem.

Key responsibilities included:

• Managing the end-to-end experience for ~6,000 store pages
• Defining UX standards and scalable patterns for local pages
• Translating behavioral insights into actionable product changes
• Acting as the connective tissue between SEO strategy, UX design, and conversion optimization

Strategy & Execution

1. Treating Store Pages as a Conversion Product

Rather than viewing store pages as static location listings, I reframed them as conversion-driven commerce surfaces.

This meant:

• Prioritizing Add to Cart, device availability, and appointment-adjacent actions
• Designing layouts that balanced local intent with commerce urgency
• Reducing cognitive load while preserving store-specific context

2. UX Optimization Using Behavioral Analytics

I leveraged Quantum Metric extensively to move beyond aggregate metrics and into real user behavior:

• Session replays to identify friction points (scroll depth, rage clicks, dead ends)
• Funnel analysis across store discovery → product view → Add to Cart
• Comparative analysis across high-performing vs underperforming locations

These insights informed:

• CTA placement changes
• Content hierarchy adjustments
• Removal or de-prioritization of low-value elements
• UX consistency improvements across device types

3. Real-Time Feedback & Issue Detection with Notify

Using Notify, I implemented workflows to surface real-time customer feedback and experience breakpoints, including:

• Store-specific complaints tied to UX or data accuracy
• Sudden spikes in negative signals that indicated rollout regressions
• Faster escalation paths to engineering and retail ops

This closed the loop between user sentiment, UX issues, and resolution, reducing lag between problem detection and fix.

4. Custom Local Experiences at Scale

I helped define and roll out customizable experience components that could scale nationally while remaining locally relevant, including:

• Store-specific availability logic
• Geo-aware messaging aligned to local inventory and promotions
• UX patterns optimized for “near me” and high-intent mobile users

This approach balanced centralized control with localized performance optimization.

Results & Impact

While specific metrics are confidential, the outcomes included:

• Improved Add to Cart conversion rates from store locator entry points
• Reduced UX friction across high-traffic store pages
• More consistent performance across thousands of locations
• Faster identification and remediation of UX and data issues
• Stronger alignment between local SEO, UX, and commerce outcomes

Most importantly, the store locator evolved from a passive directory into an active revenue-supporting product surface.

Why This Work Matters

This project demonstrates my ability to:

• Operate at enterprise scale
• Own ambiguous, cross-functional problem spaces
• Combine UX, analytics, SEO, and product thinking
• Translate qualitative behavior into quantitative business impact
• Build systems that scale, not one-off optimizations

It reflects how I approach product problems: holistically, data-driven, and execution-focused.

Key Skills Demonstrated

• Product-led UX optimization
• Large-scale location management
• Conversion rate optimization (CRO)
• Behavioral analytics (Quantum Metric)
• Feedback intelligence (Notify)
• Cross-functional leadership
• Enterprise SEO & local commerce strategy

If you want, I can:

• Tighten this for FAANG-style portfolios
• Rewrite it in a PM-focused or SEO-focused version
• Turn it into a one-page visual case study
• Quantify impact with proxy metrics that pass legal review