How to analyse your Spotify listening history with AI

You can use an AI assistant to help analyse a Spotify data export. It can calculate which artists account for the most listening time or compare your listening across different periods, provided the records cover the question you are asking.

Start with one question. “Which artists did I spend the most time listening to last month?” is easier to answer well than “Tell me everything about my personality from my music.” The first asks for a calculation. The second invites a fairly elaborate guess.

1. Request the Spotify data you need

Sign in to your Spotify account and open the Privacy page. Use the Download your data tool.

Spotify distinguishes ordinary account data from extended streaming history. The extended package is intended for account-lifetime listening history. Choose the package that covers your question and follow the download instructions in your account.

Spotify’s data rights guide explains the available requests. Once the download arrives, keep the original archive privately and read the accompanying explanation of its files.

Do not give an assistant your Spotify password to obtain the export. You can download it yourself and choose what to share afterwards.

2. Make a smaller copy for the analysis

For a ranking by artist listening time, you need the artist and playback duration. Add the date if you are comparing periods. Keep the information needed to distinguish music from podcasts.

You do not need account identifiers or network information for that calculation. Extended history can include fields unrelated to your question, so avoid uploading the whole archive without looking inside it.

Spotify’s guide to understanding your data explains the supplied fields. Use the names and definitions in your actual export rather than forcing it into an old example format.

If you need help preparing the file, first give the assistant the field names and a few fictional rows. Ask how to make a reduced copy. You can then follow those instructions in a spreadsheet or a local tool you understand.

An even smaller option is to calculate artist totals locally and share only those totals. The assistant can help explain or visualise them without seeing your detailed listening routine.

3. Tell the assistant what to calculate

Choose an assistant that can actually process your file. Ask for the calculation and a results table before requesting a chart.

Use this reduced Spotify table to rank music artists by total playback time during the supplied period. Keep podcasts separate. Sum duration before rounding. If duration is in milliseconds, divide by 60,000 to report minutes. Show the number of included records and explain any rows you leave out.

Add a check that makes mistakes easier to spot.

Show the total listening time before breaking it down by artist. The artist totals should add up to the included music total. Do not infer my mood or personality from the results.

Ask for the calculation steps or code used. A confident paragraph saying the assistant reviewed the file does not show that it processed every record correctly.

4. Check the units with a small example

Imagine two fictional records for the same artist. One contains 180,000 milliseconds of playback and the other contains 120,000.

Together they total 300,000 milliseconds, or five minutes. The calculation is 300,000 divided by 60,000.

If your result treats those numbers as seconds, the chart will be wildly wrong. Check a few rows whose totals you can calculate yourself before trusting the full ranking.

Also distinguish time listened from play count. A long podcast can account for more minutes than several songs while producing fewer records. A playback record does not automatically establish that someone completed a track or enjoyed it.

5. Compare periods that are actually covered

Before comparing months, check the earliest and latest dates in the files. Half a month will usually look quieter than a full month.

Do not remove every repeated track as a duplicate. Listening to a song again is a real event. If you combined overlapping exports, investigate repeated event records instead of deleting matching song names.

For daily patterns, tell the assistant which timezone you want to use. A late-night play can fall on a different date depending on the timezone applied.

Keep useful music preferences in AI Passport

AI Passport holds selected personal context that connected assistants can use with your permission. After analysing your history, you can choose a useful preference to carry into later recommendations.

You might notice that you often choose instrumental music while reading. If that reflects what you actually want, ask your connected assistant to propose a Passport preference for instrumental reading recommendations.

Review the proposal before approving it. A permitted assistant can retrieve the approved preference later without needing your complete Spotify export. Approval of the memory and app access to the relevant context are separate decisions.

Do not turn a pattern in the data into a claim about your health or beliefs. A shared account or background listening can also affect the results. Keep the preference you recognise yourself rather than saving the assistant’s speculation as biography.

Read about AI Passport’s memory controls.

Use the analysis to answer the question you started with. A labelled chart of listening time can be interesting on its own. It does not need to announce who you are as a person.