Convert Keepa's minute-based timestamps into readable dates, and back again. Keepa stores time as the number of minutes since 1 January 2011 (UTC), which is why a raw CSV export shows large integers instead of dates. Everything below runs in your browser.
Paste a Keepa time value (the large minute number from a CSV export).
Pick a date and time to get the Keepa minute value.
Keepa records every data point (price, sales rank, Buy Box changes) against a timestamp measured in minutes since 1 January 2011 at 00:00 UTC. Most software counts time from 1 January 1970 (the Unix epoch), and exactly 21,564,000 minutes separate the two dates.
So to turn a Keepa minute value into a normal date you add 21,564,000 and convert minutes to milliseconds (× 60,000); to go the other way you subtract it. The converter above does both, and shows the result in UTC and your local time zone so you can line Keepa data up with your own records.
| Keepa time (minutes) | Same moment in UTC | Why it is worth knowing |
|---|---|---|
0 | Sat 1 Jan 2011, 00:00 | The epoch itself. A zero in a time column means the start of 2011, not an empty cell. |
525,600 | Sun 1 Jan 2012, 00:00 | Exactly one non-leap year. Handy for sanity-checking your own arithmetic. |
7,500,123 | Sat 5 Apr 2025, 10:03 | A realistic value from a recent export. |
8,000,000 | Wed 18 Mar 2026, 13:20 | Roughly where current exports sit, so you can eyeball whether a value looks sane. |
Most of the confusion around Keepa time is really a spreadsheet import problem. Open a Keepa CSV by double-clicking it and Excel guesses a type for every column. Long integers can flip to scientific notation, and identifiers that begin with a zero quietly lose it, which breaks the ASIN lookups you were going to do later.
Import the file instead of opening it. In Excel that is Data → Get Data → From Text/CSV; in Google Sheets, File → Import with automatic type detection switched off. Set the identifier and time columns to Text, then convert the time values deliberately using the arithmetic above.
Two other things about Keepa exports catch people out. Headers are verbose and sometimes carry emoji, so a column can arrive as Buy Box 🚚: Current rather than anything you would have guessed. And weights come through in grams, while most Amazon fee tables are written in pounds and ounces, so a size-tier calculation done straight off the raw column will be wrong. If you want the fuller picture of what each field means before you start, reading Keepa data for product sourcing covers how the rank, price and offer columns behave.
Converting a single timestamp is a five-second job, which is what the tool above is for. Converting the time columns across a few thousand exported rows is a different task, and it is rarely the only thing you need doing to that file. A Keepa export carries no cost column, so the numbers that decide whether a product is worth buying — landed cost, fees, ROI — are not in there at all and have to be layered on top of what Keepa gives you.
That is the job the bulk Keepa export analyzer does: it reads the whole export, normalises the timestamps and units, and runs fee and ROI math across every row. If what you are actually trying to work out is whether a rank is any good, the BSR to sales calculator answers that one directly, and is free in the same way this page is.
Gridwinner reads your full Keepa CSV locally and runs fee, true-cost, and ROI math across every product at once.
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