THE RESULT
What the data shows
The largest computed metric is 0.8013 for grocery / detergents_paper; the smallest is -0.2072 for frozen / detergents_paper. Metric: spearman_rho (Spearman correlation).
Customer scale and channel can induce correlations across categories.
| pair | spearman rho |
|---|---|
| grocery / detergents_paper | 0.8013 |
| milk / grocery | 0.773 |
| milk / detergents_paper | 0.68 |
| fresh / frozen | 0.3844 |
| milk / delicassen | 0.3728 |
| grocery / delicassen | 0.3043 |
| fresh / delicassen | 0.2384 |
| frozen / delicassen | 0.233 |
| detergents_paper / delicassen | 0.1833 |
| fresh / milk | -0.08392 |
| milk / frozen | -0.09297 |
| fresh / grocery | -0.12 |
THE METHOD
From source to answer
Pairwise Spearman correlations across all six categories.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Identify bundle hypotheses and test incremental response instead of assuming co-spend implies bundle demand.
Where the conclusion stops
Annual customer spending uses source monetary units, not an assumed currency. There are no margins, transactions or dates. Customer segments are descriptive and do not establish promotion response.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.