SheetMonitor – Track changes to Google Sheets over time

Hacker News

A lot of small businesses use spreadsheets as an effective DB + CRUD app, where the content of the sheet represent the current "state" of the data. An example could be a sheet full of orders, or customer queries, or current products for sale. While running analysis on the "current" data of the sheet is easy, its very hard to see how your data changes over time as rows are added/removed and cells altered. For example, in a sheet full of customer support issues, maybe you want to see how the number of "open" tickets changes over time, to see how your queue grows / shrinks. So I built this tool that allows you to simply select a google sheet you have access to and create trackers to monitor the columns you want to. It uses the google sheets api to poll the sheets daily, storing aggregated data in a time-series DB. Unfortunately it requires sign-in via google for obvious reasons, but it has a free trial if you want to test it out. Any feedback appreciated!

Visit product View on Hacker NewsSep 22, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% 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: google · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

31%31% 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.

Correct prediction on native model

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