One-click data pipeline for modelling e-commerce profitability

Hacker News

Hi HN, Here is the open source repo of the data pipelines built in dbt to model profitability at the level of an order. Covers sources like: Shopify, Facebook Ads, Fedex, Paypal, Manufacturing Costs, VAT etc. Benefits for ecomm - Precise Granular Revenue and Profit analysis at the level of orders. - Template to automate Profits calculation saving you 100s hours of manual work in Excel or GSheet. - Increase ROI on Paid Marketing: By understanding hidden costs like VAT and shipping, you can make smarter decisions on ad spend across different countries. Hope this is useful!

Visit product View on Hacker NewsJun 14, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% 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: model, open · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, pipe, io · Missing: https docs, excited, just released

52%52% 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 · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: revenue, profit, shopify · Missing: arr, mrr, saas

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, smart · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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