THE RESULT
What the data shows
The largest computed metric is 0.2535 for Nov; the smallest is 0.0163 for Feb. Metric: rate (Purchase-session share).
The file does not provide exact dates or a complete calendar-year panel. Different traffic mixes may explain the differences.
| month | events | n | rate | wilson lower | wilson upper |
|---|---|---|---|---|---|
| Aug | 76 | 433 | 0.1755 | 0.1426 | 0.2142 |
| Dec | 216 | 1,727 | 0.1251 | 0.1103 | 0.1415 |
| Feb | 3 | 184 | 0.0163 | 0.00556 | 0.04683 |
| Jul | 66 | 432 | 0.1528 | 0.1219 | 0.1898 |
| June | 29 | 288 | 0.1007 | 0.07103 | 0.1409 |
| Mar | 192 | 1,907 | 0.1007 | 0.08797 | 0.115 |
| May | 365 | 3,364 | 0.1085 | 0.09843 | 0.1195 |
| Nov | 760 | 2,998 | 0.2535 | 0.2383 | 0.2694 |
| Oct | 115 | 549 | 0.2095 | 0.1775 | 0.2455 |
| Sep | 86 | 448 | 0.192 | 0.1582 | 0.231 |
THE METHOD
From source to answer
Session conversion rates with sample sizes and Wilson intervals.
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
Use monthly differences to form a seasonal experiment or data-collection question.
Where the conclusion stops
One observation is a completed session. End-of-session behavior cannot support an early-session prediction claim. PageValues is excluded from purchase models because it can encode downstream purchase information. Associations are not experiment results.
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.