THE RESULT
What the data shows
The largest computed metric is 0.2782 for 4.0; the smallest is 0.03601 for 1.0. Metric: rate (Subscription share).
Source order is documented as chronological, but block sizes are counts, not equally spaced calendar durations.
| block | events | n | rate | wilson lower | wilson upper | mean campaign contacts | previously contacted share |
|---|---|---|---|---|---|---|---|
| 1 | 407 | 11,303 | 0.03601 | 0.03273 | 0.0396 | 2.695 | 0 |
| 2 | 681 | 11,303 | 0.06025 | 0.05601 | 0.06479 | 3.639 | 0 |
| 3 | 1,057 | 11,303 | 0.09351 | 0.08828 | 0.09902 | 2.644 | 0.2543 |
| 4 | 3,144 | 11,302 | 0.2782 | 0.27 | 0.2865 | 2.077 | 0.4763 |
THE METHOD
From source to answer
Four contiguous observation blocks with response intervals and contact-count summaries.
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
Test temporal stability rather than assuming a pooled response rate persists.
Where the conclusion stops
Observational marketing records do not identify campaign uplift. Month and row order are not precise timestamps; repeated-client identifiers are unavailable. Models are research diagnostics, not financial eligibility or automated contact decisions.
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.