-Automatically clean customer data on spreadsheets 😃 -Clean names, addresses, company, and dates 📅 -Easy to use; just drag, drop, edit, and download! 🎉
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?makers this is really something many companies have an issue with... Which countries do you support for address and company or is it 'generic' cleaning.
Hi Product Hunt! I am Adhaar, one of the makers behind Clean Spreadsheets. A little story behind this tool. I used to work as a Consultant (the kind that all Tech people hate 😂) and a lot of my work was cleaning dates, addresses and company names. I used to hate it. I still do.
But when I learned how to build Web Apps I promised myself that I will build an easy way to clean customer data types. This is my solution.
My design idea for this tool was to offload cleaning on the back-end and require no input from the user. Except the data type and column they are cleaning. Cleaning various formats for all these data types is a non-trivial task, so I would love any help from the community to test out the tool and give any feedback. 😊
A couple of things people always ask:
1. What about sensitive data? - We use encryption in transit and rest but if your data is super super confidential and you still want to use the tool, get in touch, adhaar@lovespreadsheets.com and we will figure out a way.
2. Does this actually work automatically? - Yes, for most cases. That's why we provide 250 free credits. We suggest cleaning a sample first to see if this works for you. If it does, great. If it doesn't, we have a feedback button just for this purpose.
Thanks so much and happy data cleaning!
@rolandpolzin for a marketing person, there are a few benefits we have seen. You can more easily create a unique customer view (deduplicate) if their attributes are cleaned. You can run a geographic segmentation analysis or something similar by geocoding customer addresses. Once we add email in v2.0 you can ensure emails are accurately delivered.
Feel free to email me at adhaar[at]lovespreadsheets[dot]com if you want to talk further! Would love to chat about data cleaning in marketing.
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