Newsletter Send Time Open Rate Optimum Lookup
Compare actual human-open/send-slot cohorts with caller minimum delivered evidence and stable best observed rate; machine opens must already be removed, no causal optimum send time, confidence-adjusted experimental winner or future open prediction.
Description
Observed newsletter open rates depend on delivered cohorts and filtered human opens.
Observed newsletter open rates depend on delivered cohorts and filtered human opens. A minimum evidence floor can exclude small cohorts.1
Enter aligned nonempty unique slot labels, integer delivered1–1,000,000,000, opens0–delivered and integer minimum in the same range. Return rates/eligibility and selected label or empty with evidence flag.
When to use Newsletter Send Time Open Rate Optimum Lookup
Use Newsletter Send Time Open Rate Optimum Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. Machine opens must already be removed. No causal optimum, adjusted experimental winner or future prediction is inferred.
Prepare the actual editorial, source, layout, timing or audience records requested. Supply explicit house-policy limits and preserve missing evidence.
How to use Newsletter Send Time Open Rate Optimum Lookup and understand its results
- Prepare the actual editorial, source, layout, timing or audience records requested. Supply explicit house-policy limits and preserve missing evidence.
- Enter callerSendSlotLabels, observedDeliveredBySlot, observedHumanUniqueOpensBySlot, callerMinimumDeliveredEvidence using the field table and calculation rules, then run the tool.
- Read observedSlotOpenRatePercents, eligibleEvidenceSlotFlags, highestObservedEligibleSlotLabel, eligibleSlotEvidenceAvailable. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| callerSendSlotLabels | Caller send slot labels | list of string | Required |
| observedDeliveredBySlot | Observed delivered by slot | list of number | Required |
| observedHumanUniqueOpensBySlot | Observed human unique opens by slot | list of number | Required |
| callerMinimumDeliveredEvidence | Caller minimum delivered evidence | number | Required |
Enter aligned nonempty unique slot labels, integer delivered1–1,000,000,000, opens0–delivered and integer minimum in the same range. Return rates/eligibility and selected label or empty with evidence flag.
rate%=100uniqueHumanOpens/delivered; choose first eligible highest rate Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| observedSlotOpenRatePercents | Observed slot open rate percents | list of number |
| eligibleEvidenceSlotFlags | Eligible evidence slot flags | list of number |
| highestObservedEligibleSlotLabel | Highest observed eligible slot label | string |
| eligibleSlotEvidenceAvailable | Eligible slot evidence available | boolean |
{
"input": {
"callerSendSlotLabels": [
"morning",
"evening"
],
"observedDeliveredBySlot": [
100,
10
],
"observedHumanUniqueOpensBySlot": [
20,
5
],
"callerMinimumDeliveredEvidence": 50
},
"output": {
"observedSlotOpenRatePercents": [
20,
50
],
"eligibleEvidenceSlotFlags": [
1,
0
],
"highestObservedEligibleSlotLabel": "morning",
"eligibleSlotEvidenceAvailable": true
}
}Limitations and assumptions
Machine opens must already be removed. No causal optimum, adjusted experimental winner or future prediction is inferred.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Numeric lists are limited to 10,000 entries; string lists to 128 strings of at most 128 characters each. The rules above may impose smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Small Denominator Instability Warning Checker: A single numerator event changes a rate more when its denominator is small. This sensitivity is different from a confidence interval. For a complementary task, use Subscription Churn After Price Rise Percent: Cohort churn uses a fixed exposed subscriber set. An observation window needs clear inclusive and exclusive endpoints.
References
Similar or alternative tools
- Small Denominator Instability Warning Checker
A single numerator event changes a rate more when its denominator is small. This sensitivity is different from a confidence interval.
- Subscription Churn After Price Rise Percent
Cohort churn uses a fixed exposed subscriber set. An observation window needs clear inclusive and exclusive endpoints.