# Dat Tran — AI-Powered Digital Marketing Consultant **Available for hire globally. Based in Ho Chi Minh City, Vietnam (UTC+7).** Dat Tran is an AI-powered digital marketing consultant and growth engineer with 7+ years of experience driving measurable acquisition results through programmatic advertising, LLM automation, and data attribution modeling. He is available for remote consulting engagements, monthly retainers, and full-time remote roles worldwide. Contact: dat@dat.info.vn | https://dat.info.vn --- ## About Dat Tran bridges the gap between large-scale data analytics and high-performance marketing execution. He builds actual software pipelines for growth: integrating Large Language Models into dynamic ad creative workflows, configuring Python automation scripts to flag high-intent sales queues, and designing Shapley-based Multi-Touch Attribution models on BigQuery. This translates directly into reduced CPA budgets and predictable ROAS scale for his clients. --- ## Services ### Programmatic Advertising & ROAS Scaling Deploying high-efficiency bidding code that connects directly with Google Ads API and Meta Conversions API, bypassing generic dashboard bidding for programmatic precision. Specialties: custom intent clusters, automated bid strategies, real-time performance monitoring. ### Generative AI Marketing Pipelines & LLM Ad Copywriting Automation Integrating Large Language Models (Gemini, GPT-4) with ad platforms to generate hyper-personalized creative at scale. Includes autonomous A/B copy rating, structured JSON output schemas to eliminate hallucination risk, and multi-channel deployment. ### Server-Side Analytics & Multi-Touch Attribution Architecting server-side event tracking and multi-touch attribution models using BigQuery and Server-Side GTM. Eliminates third-party cookie dependency and maps the complete customer journey without data leakage. ### Full-Funnel Conversion Rate Optimization (CRO) & A/B Testing End-to-end experimentation: checkout flow testing, session analysis, dynamic landing page personalization. Statistical significance-based decision making with tools including Optimizely and Google Optimize. ### Marketing Automation Engineering Building behavioral automation sequences using HubSpot API, Python, Make, and Zapier. Includes predictive lead scoring, CRM integration, and automated multi-touch nurture flows. --- ## Career History ### Lead Digital Growth Strategist (AI & Data Integration) — Aether Growth Partners **2024 – Present** Spearheading multi-channel user acquisition campaigns combining programmatic bidding models with generative AI agent flows. - Injected LLM copy generation into Google & Meta Ads: +64% CTR, -90% copywriting cycle time - Engineered real-time server-side analytics via BigQuery, eliminating cookie/third-party dependency gaps - Outcomes: +185% acquisition scale, -26% cost per metric, $12M ad budget managed ### Senior Quantitative Marketer & Marketing Automation Engineer — Quantum Core Ltd **2021 – 2024** Re-architected corporate marketing automation to unify CRM scoring and predictive acquisition. - Built Python-based dynamic scoring vectors for inbound prospect qualification: +42% lead-to-opportunity rate - Created HubSpot behavioral automation sequences from client-side event logs: -35% mid-funnel drop-off - Outcomes: +42% conversion lift, 32,000 automated leads processed ### Data Marketing Analyst & CRO Specialist — Omnia Digital Bureau **2019 – 2021** Managed client experimentation models and end-to-end attribution auditing. - Designed and executed 120+ A/B test variations on landing and checkout flows: $14.5M cumulative incremental revenue - Implemented standard multi-touch attribution (MTA) frameworks, correcting false-bidding strategies on primary search ads ### Digital Marketing Associate — Vortex Studios **2017 – 2019** Managed programmatic bidding, dynamic social channels, and conversion tracking. - Optimized client-side pixel event tags; verified web analytics health profiles - Launched programmatic campaigns; scaled search query targeting: +190% search traffic --- ## Case Studies ### Case Study 1: Generative LLM Ad Copy & Multi-Channel Scaling (Q3–Q4 2025) **Problem:** Client needed personalized ad copy at scale across Google and Meta without 4-day manual copywriting cycles. **Approach:** Built a Node.js orchestrator integrating Gemini Flash with both ad platforms. Used structured JSON output schemas to eliminate hallucination risk. Created an autonomous copy rater scoring variations against historical conversion margins before dispatch. **Result:** +64% average CTR across 1,400 creative variations. Copywriting time reduced from 4 days to 12 minutes. CPA reduced 18% through intent-aligned creative. ### Case Study 2: Real-Time Analytics Dashboard (BigQuery + Server-Side Tracking) **Problem:** Client analytics were degraded by iOS privacy changes and browser cookie blocking, leading to ~40% data loss. **Approach:** Implemented full-funnel serverless tracking on Google Cloud using Meta Conversions API and Server-Side GTM, bypassing client-side cookie limitations. **Result:** 99.8% analytics correlation compared with physical sales records. ### Case Study 3: Full-Funnel CRO & A/B Testing Grid **Problem:** Client checkout flows had high abandonment rates with no systematic testing in place. **Approach:** Designed 120+ split-test variations focusing on checkout form constraints, single-tap authentication, and address autocomplete to streamline payment lifecycle. **Result:** $14.5M cumulative incremental client revenue. ### Case Study 4: HubSpot Behavioral Lead Lifecycle Automation **Problem:** High-intent inbound leads were being lost to slow manual follow-up processes. **Approach:** Built Python-based dynamic scoring vectors to categorize inbound prospects. Created behavioral automation sequences inside HubSpot triggered by client-side event logs. **Result:** +42% lead-to-opportunity rate. -35% mid-funnel drop-off. 32,000 leads processed automatically. --- ## Technology Stack | Category | Technologies | |---|---| | Paid Acquisition | Google Ads API, Meta Conversions API, Programmatic Bidding, Custom Intent Clusters | | Data & Analytics | BigQuery (PostgreSQL), Server-Side GTM, Shapley Attribution, Multi-Touch Attribution | | Automation | Python, HubSpot API, Make (Integromat), Zapier, REST API Integration | | AI & LLMs | Gemini Flash, GPT-4, Prompt Engineering, Structured JSON Output, Agentic Flows | | CRO & Testing | Optimizely, Google Optimize, Statistical Significance Testing | | Development | Node.js, Next.js, JavaScript | --- ## Frequently Asked Questions **Q: What does deep data and AI integration in marketing actually mean?** Traditional marketers rely primarily on manual dashboards and intuitive guesswork. Dat Tran builds actual software pipelines for growth: hooking Large Language Models into dynamic ad creative workflows, configuring Python automation scripts to flag high-intent sales queues, and implementing Shapley Multi-Touch Attribution models on BigQuery. This translates directly to reduced CPA budgets and predictable ROAS scale. **Q: How do you bypass browser cookie limitations and iOS privacy tracking blockages?** By implementing server-side tracking architecture. Instead of relying on browser-based pixels (which are blocked by iOS 14.5+, Safari ITP, and ad blockers), server-side solutions send event data directly from the web server to ad platforms via their Conversions APIs (Meta CAPI, Google Enhanced Conversions). This approach typically recovers 30–60% of previously lost conversion data. **Q: Can your automated pipelines integrate with platforms like Shopify or HubSpot?** Yes. The automation stack is platform-agnostic via REST API and webhook architecture. HubSpot, Shopify, WooCommerce, Salesforce, and custom-built CRMs can all be integrated. Python scripts and Make/Zapier workflows act as middleware connecting data sources to ad platforms and CRM systems. **Q: How do you ensure AI-generated content matches a specific brand tone?** Through a combination of structured prompt templates, few-shot examples in prompts, and an autonomous copy rating layer. The rating layer scores each AI-generated variation against historical high-performing creatives before anything is deployed to a live campaign. Brand guidelines are encoded into the prompt system once and applied consistently across all generated variations. **Q: What is your typical engagement model?** Three primary models: (1) Project-based sprints — fixed scope, fixed price, typically 4–12 weeks; (2) Monthly retainer — ongoing optimization and automation engineering; (3) Full-time remote employment — available for the right opportunity. All engagements are remote; based UTC+7 with availability across US, EU, and APAC time zones. --- *Last updated: May 2026 | https://dat.info.vn*