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Castled – Marketing automation using customer data from Snowflake

Hacker News

Castled – Marketing automation using customer data from Snowflake

Hi HN! We are excited to share something we have been building for the last 12 months. Castled is a warehouse-native marketing platform built directly on cloud data warehouses like Snowflake, BigQuery, Redshift, and Postgres. Castled allows you to directly use the customer data in your data warehouse and engage them across different channels like Email, Sms, WhatsApp, push, and In-app - without having to copy the data to another tool. We started our journey by building an open-source Reverse ETL solution to make the warehouse data actionable to marketers. However, after talking to 100s of marketers, we realised that modern B2C marketers needed to use billions of customer data points from the data warehouse to run personalised marketing campaigns. While Reverse ETL could sync all this data to marketing tools like Braze, Iterable, etc., the traditional tools were fundamentally not designed to store and process so much data. The restrictions surfaced in the form of illogical data volume-based pricing and restrictive data retention - usually just a few months. Moreover, copying the data to multiple external tools created more complications w.r.t security audits(e.g., SOC2, GDPR, etc.) and other data privacy protocols. The data engineers also faced numerous issues maintaining the Reverse ETL pipelines, which often failed due to API timeouts, rate limits, etc. That's when we changed course to build a warehouse-native marketing solution. Since we do not store any customer data with us, marketers could finally use all the data from the data warehouse to engage their customers without any tradeoffs. I have been an avid follower of HN for years now. So we look forward to hearing any thoughts, insights, questions, and concerns about our product in the comments below. Also, feel free to reach out to me personally at abhilash@castled.io.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, using, open · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, pipe, io · Missing: https docs, just released, exist
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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