Does discount size affect order value? Do customers in some regions churn more? Questions like these are about relationships between columns. Checking every pair by hand is slow; a dataset with 30 columns has 435 pairs.
Plumb is a privacy-first data platform. We think AI should do the tedious parts of analysis, like reading every row, spotting patterns and drafting charts, while you keep control of the questions and the conclusions. Every answer Plumb gives points back to the rows and columns it came from, so you can check the work instead of taking it on faith.
How Plumb helps: Correlation analysis
Plumb's correlation analysis checks relationships across all your columns: numeric correlations, differences between categories, and columns that predict others. It ranks the strongest relationships, shows them as charts, and reminds you when a relationship may be driven by a third factor.
How It Works
How Plumb finds relationships.
- Numeric vs numeric: correlation (Pearson for linear, Spearman for ranked relationships).
- Category vs numeric: compares averages across groups, such as order value by region.
- Category vs category: checks whether two categories are linked, such as plan type and churn.
- Ranks by strength and sample size, and filters out trivial links like ID columns.
- Shows each relationship as a chart with a plain-English note, including when correlation does not prove cause.
Where This Helps
Pricing
See how discounts relate to order size and repeat purchases.
Churn analysis
Find which customer attributes go with higher churn.
Marketing
Check which channels relate to higher-value customers.
Try Plumb in early access
Plumb is in early access. Join the waitlist on tryplumb.pro to get an invite. Early access covers Excel (.xlsx), CSV and JSON files, and we are adding more sources as people ask for them. Your files are processed privately and are never used to train models.
Find what moves together
Now you have a repeatable way to do it, by hand or with Plumb. Join the waitlist and tell us what your data looks like; we read every reply.
Join the waitlist