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Technology
Technology / SaaS
March 18, 2026
Technology / SaaS

Transitioning to AI-Led Full-Funnel Growth: Improving Performance Marketing and First-Party Data Economics in Southeast Asia

Analyzes AI-led marketing in Southeast Asia, highlighting limited transformation beyond analytics, declining first-party data advantage, top-funnel gains, and shift toward warehouse-centric MarTech architectures.

35 Mins
Senior Manager
Singapore
Public
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Companies Discussed
Adobe (ADBE), Amazon (AMZN), Braze (BRZE), Google (GOOGL), Grab (GRAB), Meta (META), PropertyGuru (PGRU), Salesforce (CRM), Shopify (SHOP), Snowflake (SNOW)
Executive Summary
Topics Covered
Methodology
Free Preview — Executive Summary

This transcript examines AI adoption in Southeast Asia marketing, where AI primarily enhances analytics and decision-making rather than fully transforming performance marketing systems. While AI improves top-of-funnel demand generation through scalable content and experimentation, conversion and retention remain manual and harder to automate. First-party data advantage is diminishing as platforms like Google and Meta leverage superior aggregated datasets. Organizations are shifting toward warehouse-centric architectures over CDPs, though adoption remains fragmented. Closed-loop data systems are critical for performance, while meaningful transformation is still in early stages across most enterprises.

Topics Covered
  • AI as incremental improvement vs full transformation in marketing
  • Impact of AI on LTV/CAC and performance marketing economics
  • Declining differentiation of first-party data vs platform data
  • Role of retail media and closed-loop ecosystems in monetization
  • AI effectiveness across funnel stages is strongest at the top of the funnel.
  • Challenges in scaling AI for conversion and retention use cases
  • Shift from CDPs to warehouse-centric composable MarTech stacks
  • Competitive dynamics across cloud, AI, and MarTech partnerships
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Q: Can you walk us through the current GPU allocation framework at your organisation? How are you deciding between internal AI workloads and enterprise customer commitments? A: Sure. So the fundamental tension right now is that our internal AI teams — the ones building our own foundation models and inference services — are consuming GPUs at a rate that nobody anticipated even 18 months ago. We're talking about 3-4x the original projections. And that creates a real squeeze on what's available for enterprise customers. The allocation committee meets weekly now, which tells you everything. It used to be quarterly. We have a scoring matrix that weighs revenue potential, strategic importance, and internal capability gaps. But honestly, internal teams almost always win because the economics of our own AI services are so compelling compared to renting compute to enterprises...

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Expert Profile
Senior Manager
Duration
35 Mins
Call Date
April 8, 2026
Geography
Singapore
Transcript Tier
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Companies Discussed
NVIDIA (NVDA)
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