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 Older articles in the QuickFrame help center had accumulated editorial debt, with outdated copy, inconsistent terminology, and gaps in coverage lowering the accuracy rate of the AI-powered support chatbot and driving avoidable ticket volume.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 that backlog, improving content quality day to day, and contributing improvements to the governance processes already in place, the operational base that several more specialized efforts grew out of.

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. Where the specialized work lives

    • Several distinct efforts grew out of this operational base and now have their own projects: the QuickFrame Marketplace help center launch (QuickFrame Marketplace Launch), the AI-assisted drafting pipeline that raised chatbot resolution (AI Implementation Strategy), the companion Slack KPI reporting agent (Slack-Connected KPI Tracking Agent), and the Integrations content buildout (Technical Writing for Partner Integrations).

  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.

Collaboration

Worked closely with six cross-functional SME teams including Product, Engineering, Customer Success, Legal, Platform Experience, and Marketing 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 32 through merging, rewriting, and governance review: a 47% 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.