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Analyzing Membership Exports

What This Guide Covers

  • How to export a fuller data file from your gym or studio platform for deeper analysis
  • How to upload that file directly to Claude and get real analysis, not just a summary
  • How to turn what you find into specific, actionable changes, not just interesting trivia

Why This Matters

The weekly attendance habit is good for staying current, but won't surface slower moving patterns, like which membership tier is quietly driving most of your revenue.

Case Study

Cinder Block CrossFit

Cinder Block CrossFit exported three months of membership and attendance data and uploaded it directly to Claude for analysis. One specific tier had meaningfully lower attendance and higher cancellation rates than the others.

Result: The owner used that finding to redesign the underperforming tier's onboarding process specifically.

Step-by-Step Guide

  1. 1

    Export a data file from your platform

  2. 2

    Upload the file directly to Claude

    AI Prompt
    I've uploaded my [membership/attendance] export covering [date 
range]. Analyze this data and tell me what stands out.
  3. 3

    Ask follow up questions based on what you see

    AI Prompt
    You mentioned [specific finding]. Can you dig deeper into that 
specifically?
  4. 4

    Compare membership tiers against each other

    AI Prompt
    Break down attendance and cancellation rates by membership tier. 
Which tiers are performing best, and which are showing warning 
signs?
  5. 5

    Look at class performance by coach or time slot

    AI Prompt
    Based on this data, which coaches or time slots consistently show 
the strongest attendance? Which are consistently underperforming?
  6. 6

    Turn findings into one or two concrete changes

Frequently Asked Questions

How often should I do this deeper export and analysis?

Quarterly is a reasonable cadence.

What file format works best for uploading to Claude?

CSV or spreadsheet formats like XLSX both work well.

Is my data safe uploading it to Claude for this kind of analysis?

Only upload what you need, and avoid member names if your platform allows excluding them.

What if the data reveals something uncomfortable, like an underperforming coach?

Treat it as a starting point for a specific, constructive conversation, not a verdict.

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