Christian Limon

VC, Chief Growth Officer, board member | Led early mobile ecomm, games, streaming with $28 Bn+ exits

Christian Limon is a seasoned professional with a strong background in growth strategies and corporate development within the tech and investment banking sectors. Christian's education includes a BA in Economics from the University of Southern California, an MBA in Finance & Entrepreneurship from Loyola Marymount University, and he also attended St. John Bosco High School.

Throughout his career, Christian has held key roles at prominent organizations such as Gemini, Nifty Gateway, Tubi, Wish, Glu Mobile, and Tapjoy. He has led growth initiatives and served in executive positions, showcasing his expertise in driving business expansion and partnerships.

Christian's notable achievements include being part of significant exit and valuation updates like Gemini's private valuation of $7.1 billion in 2021, Tubi's acquisition by Fox Corp for $500 million in 2020, Wish's IPO with a $17 billion market cap debut in 2020, Glu's acquisition by EA for $2.1 billion in 2021, and Tapjoy's acquisition by IronSource for $400 million in 2021.

Highlights

Nov 29 · twitter

Comments on the paper that Eric shared on MMMs @eric_seufert - H/t to Eric for keeping us informed on ads, attribution, and mobile.

In the paper, I see a weak issue and a strong issue. The weak issue can be improved by addressing the strong issue.

Weak issue: Too much Noise - there are too many moving parts and data quality is all over the place.

Any luck I've had experimenting, I attribute to structured discovery of novelty: Minimize & isolate. Religiously.

Simply the questions
Limiting variables down to the bare minimum.
Isolate

Strong issue: Low volume (of everything). Not enough budget, not enough advertisers, not enough campaigns. Too much of everything you don't want.

1000x the volume and focus on 1 advertiser. And simplify the questions.

-- For reference, Meta has a paper asking a question most would find silly. It's the only important question, though.

why do advertiser outcomes vary? What explains the differences? It's not obvious that performance should vary, at all.

This is the sample volume:

3.94 billion ad opportunities
200,000 advertisers across 25 industries
700,000 ad campaigns
100% of observations focused on Facebook/meta ads

This is hard. Full stop. Hard for the same reasons there's no ultimate marketing platform "to rule them all".

This is the No Free Lunch theorum in action. It states that no algorithm is universally optimal across all problems, as performance depends on the specific context.

Adapted to ads, no single combination of ads platform, campaign setup or attribution model works best across all advertisers.