Analyse Minds Results in SPSS, R and Excel | Minds

An insights team does not want a new place to analyse things. The crosstab conventions, the weighting scheme, the chart template the client recognises — all of that lives in a tool the team already trusts, and a research layer that cannot feed it is a research layer that gets abandoned after the pilot.

Minds exports study results as a plain long-format table, which is the shape every one of those tools already expects.

The shape of the export

Raw data comes out with one row per persona answer and six columns:

Long format matters. It is already what a pivot table, a crosstab engine, or a tidyverse pipeline wants, so nothing has to be reshaped before the first analysis.

Getting it into each tool

Excel and Google Sheets. Open the CSV directly. The file carries a UTF-8 byte order mark, so accented characters and non-Latin scripts survive the round trip. Pivot on Question against Group for the standard crosstab.

SPSS. Export the study as .sav and open it directly — variables and labels arrive already defined, with no import wizard. If you would rather keep everything in one format across tools, the CSV works too: File → Import Data → Text Data, comma delimiter, first row as variable names.

R. read.csv("export.csv") or readr::read_csv() for a tibble. The long shape means dplyr::count(Question, Group, Answer) gives you the distribution without any reshaping, and Full response stays available for a qualitative pass.

Q, Displayr, Power BI, Tableau. Add the CSV as a flat file source. Question becomes the row variable, Group the column variable.

Python. pandas.read_csv(), then crosstab or groupby. Useful when the same script already handles your survey exports.

When to skip the file

If the analysis is recurring rather than one-off, pull from the API instead. Study, panel, and analytics endpoints authenticate with a bearer key, so a scheduled job can refresh a dashboard without a human exporting anything. Panel analytics can also be read into Looker Studio through the Panel Analytics connector, and a spreadsheet-first team can run questions straight from a sheet with the Google Sheets add-on.

An honest note on what you are analysing

The export makes synthetic results as easy to handle as survey data, which makes it easy to forget they are not survey data. Counts from a synthetic panel describe the panel, not a population. Use them to compare conditions, find the objection, and rank hypotheses — and keep prevalence claims for a real sample.

Everything else about the workflow stays yours: your weighting, your significance conventions, your chart template, your client's expectations. The synthetic layer just gets there faster and hands you the table.

Sample prompt

Ask the same question across every audience, request a short structured answer plus the reason, then export the raw data and crosstab answer by group to see which segments diverge.