2021–2025 @ Garena Free Fire (~100M DAU) · Specialized in causal inference, A/B testing, and monetization strategy across 15 global markets.
Data analysis is not just about reporting what happened; it's about reducing uncertainty for critical decisions.
A small, clean, unbiased sample is infinitely more valuable than a massive, confounded dataset. I prioritize causal clarity over sheer data scale.
I start with the decision that needs to be made, then work backwards to the metrics and models required to inform it.
One-off analyses are expensive. I focus on building frameworks and monitoring systems that turn a single insight into a standing capability.
Four deep dives into how analytical pivots moved the needle on product strategy.
From reactive banning to structural deterrence in Trust & Safety
The Trust & Safety team was stuck in an "arms race" — banning cheaters only for them to return with new accounts. The volume was overwhelming, and the reactive approach was failing to protect the player experience. We needed to move from chasing individuals to breaking the cheat economy.
I shifted the focus from banning to deterrence via friction. By analyzing the network patterns of high-frequency cheaters, I identified a specific infrastructure bottleneck they relied on. Instead of just banning, we introduced dynamic verification gates triggered by these network signals, making it economically unviable to run cheat-services at scale.
The shift to structural deterrence reduced cheater impact on network quality by ~30% in target regions. More importantly, it produced a scalable SOP for infrastructure-level intervention that the engineering team now deploys automatically when certain network anomalies are detected, rather than waiting for manual analysis.
Why "better" network metrics led to worse player sentiment
Engineering had optimized for average latency (Ping) in LATAM, successfully reducing it by ~15ms. Paradoxically, player sentiment data showed a spike in "lag" complaints and a 2% increase in churn among competitive players. The "better" the metrics got, the worse the experience felt.
Using causal inference (PSM), I proved that players are far more sensitive to latency variance (jitter) and packet loss than to absolute ping. A stable 100ms connection is better than a 50ms connection that spikes to 200ms every ten seconds. We pivoted the engineering KPI from "Average Ping" to "95th Percentile Jitter," which finally aligned technical metrics with player reality.
The KPI pivot led to a routing change that prioritized stability over raw speed. Lag-related churn stabilized, and the "Lag Paradox" was solved. This project established variance-based monitoring as the standard for all 15 global markets, ensuring we never again optimize for a metric that doesn't reflect player experience.
AUG controversy & Wukong Skill Skin launch strategy
The design team believed AUG was balanced based on aggregate KD stats. Community pressure said otherwise. These two positions were both right in their own frame — aggregate metrics are confounded by player-skill selection bias, so they can't settle a balance dispute.
For the Wukong Skill Skin launch, the risk was different: monetization and fairness were in direct tension. The question was how to set pre-launch guardrails that let us capture revenue without crossing into pay-to-win territory.
For AUG: I proposed a same-player controlled comparison — comparing each individual's KD with AUG versus a neutral AR baseline — to remove the skill confound entirely. AUG outperformed the baseline by ~28%, moving the design team from "community perception" to an evidence-backed fix.
For Wukong: I set fairness guardrails before launch — a hard ceiling on per-player KD uplift. Monetization success that violates that ceiling is not a win. This prevented a "pay-to-win" crisis while maximizing revenue.
These two cases produced a reusable pre-launch evaluation template: quantify gameplay impact via controlled comparison, define acceptable outcome ranges before release, and treat fairness guardrails as a launch gate rather than a post-hoc audit.
Vietnam market: revenue falling despite stable activity
Vietnam showed a multi-month revenue decline while DAU and session time stayed stable. The revenue team's initial read was weak player sentiment or market softness. I pushed back: a DAU–revenue divergence of this magnitude rules out an engagement problem. The cause had to be structural, inside the monetization system itself.
I introduced an item ownership-rate metric — measuring what share of featured items were already owned by active high-value payers. The data showed that 80%+ of re-featured content was already in their inventories. The problem was a content supply gap, not a demand problem. We pivoted from "how to re-engage payers" to "how to ensure high-value payers always have something new to buy."
The content team adopted a revised pacing policy: enforced cooldown periods on re-featured items and a guaranteed monthly new SKU for the high-value segment. After the change, new-content revenue recovered to ~50% of total Vietnam revenue. The ownership-rate metric is now a standing leading indicator in market monitoring.
Methods and tools used to drive decision-making across global markets.