Enterprise product management Delivered product work
Scaling enterprise content targeting
Rebuilding the foundation of device metadata to support enterprise complexity and scale.
Delivered 2023, iterated through 2025
Overview
As enterprise customers expanded their digital signage deployments, existing metadata functionality struggled to support increasingly complex targeting needs. The challenge was not to add more functionality, but to build a foundation that could scale across different enterprise use cases.
- Context
- Existing metadata targeting struggled to support the scale and complexity of growing enterprise deployments.
- My contribution
- I owned the product strategy and phased roadmap, and advocated for refactoring the underlying data architecture first.
Problem
The platform initially supported about 6,000 devices using metadata. Existing workflows and underlying data structures created performance limitations, reliability issues, and friction for customers managing large quantities of metadata and values.
This was a multi-phase initiative that launched in 2023, with iterations through 2025.
What established the problem
- Enterprise customer needs for more complex targeting.
- Performance and reliability issues in the existing data structures.
- Volume of metadata-related feature requests and bugs.
- A strategic enterprise customer's scale plan that the platform could not yet support.
My contribution
What I did
- Owned product strategy, prioritization, and phased roadmap sequencing.
- Led discovery and translated enterprise customer needs into prioritized requirements.
- Partnered with Engineering early to assess architectural limitations and define the refactoring approach.
- Facilitated alignment around the broader scope required to address foundational problems.
- Used RICE and severity-based prioritization to sequence delivery.
- Made scope tradeoffs to derisk implementation.
- Established post-launch measurement using product analytics and customer feedback.
Who I worked with
- Engineering
- Design
- Analytics
- Test
Decisions & tradeoffs
Delivery phases
Backend foundation
- Refactor underlying data structures.
- Resolve known performance and reliability issues.
- Establish a scalable foundation for enterprise use cases.
Device-level metadata management
- Improve assigning, editing, searching, filtering, and sorting metadata and values.
- Manage metadata more efficiently across large device deployments.
Administrative metadata management
- Improve metadata visibility and management across the platform.
- More efficient search, sorting, filtering, and navigation between metadata and associated devices.
Bulk metadata management
- Bulk creation, CSV upload, and multi-select deletion.
- Help customers manage large metadata libraries more efficiently.
Key decisions
- Engaged Engineering early and dedicated an entire initiative to backend refactoring. There were differing opinions about its necessity and scope; I advocated for fixing the data architecture rather than continuing to build on a foundation that would not scale.
- Sequenced the roadmap around distinct customer use cases instead of delivering everything in one release, using RICE, customer impact, and issue severity.
- The refactor addressed data usability, performance, and scalability, and has continued to serve the product as requirements and scale have grown.
Tradeoffs
- Prioritized foundational improvements before expanding functionality.
- Addressed some capabilities through more efficient alternative approaches.
- Descoped lower-priority features to reduce delivery risk.
Outcomes
Results of team work. Measured outcomes are kept separate from expected benefits.
+233%
Scaled support for content targeting from 6K to 20K devices
- Metadata-related feature requests, 2024 to 2026
- 18 → 2
- Metadata-related feature requests, 2024 to 2026
- Metadata-related bugs, 2024 to 2025
- 16 → 3
- Metadata-related bugs, 2024 to 2025
- Critical metadata-related escalations in 2025
- 0
- Critical metadata-related escalations in 2025
- Annual-revenue opportunity enabled
- $3.8M
- Annual-revenue opportunity enabled
How it was measured
- Device scale, feature requests, bugs, and escalations are measured results.
- The capabilities were necessary to support a strategic enterprise customer's scale plan; without them, the $3.8M deal would not have closed. This is business opportunity enablement, not revenue generated by the feature alone.
Lessons
- Foundational technical investments can be essential product strategy.
- Engaging Engineering early improves the quality of product decisions.
- A multi-phase roadmap can balance long-term architectural needs with incremental customer value.
- The right scope is not necessarily the largest. It is the scope that solves the most important problem while preserving future flexibility.