Podcast
How to Build a Data-Led Growth Stack: Event Tracking, Tracking Plans & Reverse ETL
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How to Build a Data-Led Growth Stack: Event Tracking, Tracking Plans & Reverse ETL
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Episode Overview
How do you design a data-led growth stack that reliably powers personalization, activation, and operational workflows? In this episode, Arpit Choudhury, founder of Data-led Academy, walks through the practical steps of building a data-led growth stack focused on event tracking, documented tracking plans, and reverse ETL.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 2:21 - DataLed Academy: free learning, repository & podcast
- 5:06 - Career trajectory: integrations, Integromat & community growth
- 7:21 - Growth marketing: A/B testing, personalization & product data
- 9:46 - Marketer tooling: visual queries and self-serve data access
- 10:45 - Definition: data-led professional — source awareness & data skepticism
- 11:33 - Data-led vs. data-driven: balancing data, intuition & bias
- 13:34 - Tracking plan & instrumentation: documenting events, properties & ownership
- 18:27 - Anomaly investigation: tracing event origins and fake signups
- 20:47 - Collaborative tracking tools: AVO, Iteratively, TrackPlan
- 22:50 - Data flow overview: collection, storage, analysis and activation
- 24:43 - Event examples for SaaS: signup, project created, invite, invoice
- 27:00 - Client-side vs. server-side events: timing, accuracy and use cases
- 28:52 - Data warehousing & transformation: warehouses, DBT and BI analysis
- 30:03 - Data activation: sending event data to support, sales and engagement tools
- 33:41 - Data collection platforms: Segment, RudderStack, MetaRouter, Freshpaint
- 35:27 - Warehouse-centric analytics: Snowflake, BigQuery, Redshift & warehouse-first
- 37:25 - Reverse ETL / operational analytics: Census, HighTouch, Grouparoo
- 38:20 - Customer Data Platforms (CDP): all-in-one trade-offs for marketers
- 41:30 - Modern data stack for growth: CDI, product analytics, warehouse & reverse
- 43:50 - Buy vs. build: cost, maintenance and open-source alternatives
- 46:13 - Team composition: data engineer, analyst, analytics engineer & product ops
- 51:40 - Data democratization: data literacy, documentation & self-serve analytics
- 53:48 - Motivating documentation: culture, early habits & catalog tools
- 56:08 - Product-led vs. data-led: activation events and personalized onboarding