The operational side of how a creator platform manages portfolios at scale. This includes segmentation and the thresholds required for management, and setting ideal portfolio ratios. It also covers incentive design for partner-facing teams, OKR tracking, headcount planning, and the management model for how the portfolio is run. I built and operated this across multiple APAC markets.
How creators, audiences and advertisers depend on each other, and which of those relationships matters most at a given stage. That drives the sequencing decisions, such as when to lead with creator supply, when the audience has to come first, and when monetization proof needs to come before either. I also know what stalls a platform in one market when the same approach works in another.
GTM design for new product feature rollouts across creator and partner ecosystems. This covers market sequencing, partner readiness assessment, creator adoption playbook, and feedback loop to product. I did this for five years at YouTube, across multiple feature launches in APAC.
How to tell which agencies in a given market can actually close commercial commitments, and which can only source creators. This varies a lot by market, in areas like execution capability, coverage, and how their incentives are aligned. Understanding those differences before you contract is what keeps the program from stalling later, and the contract itself is where that understanding has to show up, in what an agency is held to and what happens when it misses.
I have designed and launched programs across a range of verticals, from brand deals and creator acquisition to feature rollouts and product feedback systems. Each came with its own OKRs, governance, and scoring that was adjusted as the program learned. This is the full range of what a creator platform needs to build.
Before YouTube, I worked in B2B SaaS GTM at LinkedIn and Cloudflare, on territory planning, quota-setting, account segmentation and scoring, the channel versus direct trade-off, and predictive churn modelling. The structural GTM problems SaaS companies face in APAC, such as ICP clarity, commercial structure and pipeline conversion, are ones I have worked on directly from the inside.
Designed and scaled the creator service model across multiple APAC markets, including partner segmentation, criteria, onboarding and how partner-facing teams were incentivised and measured. Took a brand partnership program from two pilot markets to eight. Launched a new creator vertical in India from scratch, and designed programs for the podcast and music verticals. Ran the regional process for routing creator and partner feedback into global product priorities. In charge of regional rollout of feature launches across priority markets, tracked and adapted as the program ran.
Built account prioritisation and territory coverage model for APAC sales: scoring, segmentation, channel vs direct trade-off analysis. Partnered with customer success to build predictive churn models that surfaced at-risk accounts before renewal conversations.
Territory planning and quota-setting for APAC sales. Competitive intelligence infrastructure for Learning and Talent Solutions across the region.
Strategy engagements for PE and corporate clients across ASEAN: post-merger integration, market entry, due diligence, and financial modelling across TMT, pharma, and financial services.
If the problem is not what you think it is, I will tell you directly, and if the fix you are proposing will not work, I will explain why. The point of an engagement is to find what is actually breaking and build the system that fixes it.
Every missing data point is flagged, along with what cannot be diagnosed without it. A confident-looking diagnosis built on estimated numbers is more dangerous than one that names its gaps and tells you exactly what to go and find.
Every deliverable goes through an APAC review pass that asks whether the diagnosis holds up against how each market actually behaves. Those market-level specifics change the recommendation, so they are built into the work itself rather than added as a footnote.
AI handles the research synthesis, the data structuring and the first-draft analysis. The APAC market judgment, the diagnosis and the recommendation stay mine, including the call on what will not work in a given market and why. Working this way makes the engagement faster without compromising the judgment behind it.