MNTN - Help Center Ops & Content Governance

Content operations and governance support for a CTV ad-tech help center serving performance advertisers across two products.

Published 172 help center articles across two products, cut a legacy content backlog by 50%, and raised AI chatbot resolution from 51% to 62%.

Context

MNTN's Performance TV platform serves performance advertisers running CTV campaigns. When I joined, the help center supported two distinct products: the MNTN platform and the QuickFrame video production marketplace. Each had its own content backlog and SME network, and an existing content governance framework was already in place to support operations across both.

Content requests were coming in from six cross-functional teams at 8 to 15 per week. Older articles in the QuickFrame help center had accumulated editorial debt, with outdated copy, inconsistent terminology, and gaps in coverage lowering the accuracy rate of the AI-powered support chatbot and driving avoidable ticket volume. My work focused on executing against the backlog, improving content quality, and contributing improvements to the governance processes already in place.

My approach

  1. Intake audit & backlog triage

    • Mapped all open content requests against active SME coverage, product priority, and support ticket volume. Categorized requests into new articles, updates, and consolidations to establish a working backlog with clear priority ordering.

  2. SME coordination & review workflow

    • Established intake templates and a two-pass review workflow, with technical accuracy SME review first and editorial polish second, reducing back-and-forth and keeping an 8 to 15 request per week throughput without sacrificing quality.

  3. QuickFrame content audit

    • Ran a systematic audit of the QuickFrame Marketplace help center, identifying outdated, redundant, and gap articles. Prioritized remediation against support ticket data, the most direct path to improving chatbot resolution accuracy.

  4. Governance contributions

    • Contributed to the Content Governance Model, Annual Review Cycle charter, and Help Center Style Guide, three interconnected documents that supported content ownership, lifecycle management, and quality standards at MNTN. My role involved applying these frameworks day-to-day and surfacing improvements based on what I observed in practice.

  5. Integrations content buildout

    • Researched, wrote, and published 30+ articles covering MNTN's third-party integration ecosystem, from measurement platforms and attribution partners to pixel setup and API access. Introduced a consistent article template with Overview, How It Works, and Shared Data sections.

Collaboration

Worked closely with six cross-functional SME teams including Product, Engineering, Customer Success, and Sales to source accurate technical content and get articles through review cycles efficiently. Coordinated with the support team to align article priorities against live ticket volume and chatbot query data, ensuring content effort went where user need was highest.

Results

  • 30+ Integrations articles written from scratch, covering the full ecosystem of third-party CTV measurement partners and API connections, one of the most requested content gaps from the support team.

  • Led a full content audit and migration for the QuickFrame Marketplace help center launch, consolidating 60 existing articles into 30 through merging, rewriting, and governance review: a 50% reduction in content footprint while improving clarity and information architecture.

  • Supported the Annual Review Cycle charter with AQI (article quality index) scoring and a phased review cadence of 13 articles per sprint, giving the team a sustainable model for ongoing content maintenance at scale.

  • Help raise AI chatbot resolution rate from 51% to 62% across two quarters by systematically improving help center articles that directly feed knowledge to MNTN’s first-line customer service AI, reducing escalations to live support.