For Nonprofit Program Managers ·
What you'll accomplish
A community needs survey or a season's worth of case management exports usually ends up as a spreadsheet nobody has time to properly analyze, so it sits there while program decisions get made on instinct instead of data. Julius AI reads a CSV or Excel export, lets you ask questions about it in plain English, and hands back real charts and cross-tabs, the kind of analysis that would otherwise mean pivot tables and formulas you don't have time to build.
What you'll need
Open your export and confirm two things: it's aggregated or anonymized (no client names, no case numbers that map back to a specific person you could identify), and the column headers are clear enough to understand without the original database's internal codes. Rename any cryptic column headers ("q7_resp" becomes "afterschool_interest") before uploading. This step happens in your spreadsheet program, not in Julius.
What you should see: A confirmation that your file uploaded, often with a quick preview of the columns and row count. Troubleshooting: If Julius reports an error reading the file, check for merged cells or multiple header rows in the original spreadsheet. A clean, single-header-row export works most reliably.
Before jumping to your real question, ask it to summarize what it's looking at. This catches upload problems early and confirms Julius understood the columns correctly.
Example prompt:
Before analyzing anything, describe this dataset: how many responses, what columns are present, and whether any columns have a lot of missing data.
What you should see: A short written summary plus row and column counts. Compare it against what you know about the actual survey, like the number of responses you collected.
Ask a specific question rather than a vague one. "Analyze this" produces a generic overview; a specific question produces a specific, usable chart.
Example prompt:
What are the top 3 needs mentioned in the "primary_need" column, broken down by the "age_group" column? Show this as a bar chart.
What you should see: A chart appears in the chat, along with a short written explanation of what it shows. Julius writes and runs real code behind the scenes to produce this, not a guess.
Pick one number from the output, like a total response count or a specific category's percentage, and check it against a number you can calculate yourself or that you already know, like the total number of surveys collected. This takes thirty seconds and catches the rare case where a column got misread or a filter applied incorrectly.
Troubleshooting: If a total doesn't match what you expect, ask Julius directly: "Show me exactly how you calculated that number." It will show its work, including the actual code it ran, which usually reveals the mismatch (a filtered-out blank row, a miscounted category, and so on).
Once the first chart checks out, keep asking. A static spreadsheet cannot answer a follow-up question, but Julius can, and you can follow a thread of curiosity without building a new pivot table each time.
Example follow-up prompts:
Are there any age groups where the top need is different from the overall top need?
Cross-tab the "primary_need" column against the "how_did_you_hear" column. Does the need people mention vary by referral source?
Download the charts you'll reuse in a report or presentation, and copy the written explanations you'll want to paraphrase into your own needs summary document.
Dataset sanity check:
Describe this dataset before we analyze it: row count, column names, and any columns with significant missing data.
Top themes by group:
What are the top [N] values in the [column name] column, broken down by [grouping column]? Show as a chart.
Cross-tab exploration:
Cross-tab [column A] against [column B]. Are there any groups where the pattern looks different from the overall average?
Show the work:
Show me exactly how you calculated that number, including the code you ran.