Description
Generate nonincreasing synthetic funnel counts under per-stage drop caps, with null conversion when the prior stage is empty.
A chart dataset contains values plus the labels, grouping, totals, or missing-value conventions needed to interpret them. Separating the numeric construction from plotting makes conservation rules and deliberate edge cases visible before a chart is rendered.
Generate nonincreasing synthetic funnel counts under per-stage drop caps, with null conversion when the prior stage is empty.
When to use this tool
Use this generator to create reproducible funnel stage count dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.
How to use the tool and how it works
- Supply stages, opening, maximumDrops in the field shapes below. Set seed when you need an explicit replay.
- Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
- Read counts, drops, conversions, stages. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Start at opening and draw each integer drop from zero through the smaller of its cap and the preceding count. Subtract drops to form nonincreasing stages and delegate each next-to-previous conversion ratio, using null when the preceding count is zero.1
Delegated operations used for this result: divide two numbers. Their calculation or parsing conventions apply to the corresponding result fields.2
| Input field | Meaning and units | Accepted input |
|---|---|---|
| stages | Stages | Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 16. |
| opening | Opening | Required. Safe integer; minimum 0; maximum 9007199254740990. |
| maximumDrops | Maximum Drops | Required. Array of Safe integer; minimum 0; maximum 9007199254740990.; minimum items 1; maximum items 15. |
| seed | Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed. | Optional. Safe integer. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| counts | Counts | Required. Array of Safe integer.; minimum items 2; maximum items 16. |
| drops | Drops | Required. Array of Safe integer.; minimum items 1; maximum items 15. |
| conversions | Conversions | Required. Array of Finite number. or Null.; minimum items 1; maximum items 15. |
| stages | Stages | Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 2; maximum items 16. |
| randomness | Replay source and normalized 32-bit seed, or null seed for an advancing context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional safe-integer replay seed; normalization and precedence are explained below. | Required. Safe integer. or Null. |
| randomness.source | Source | Required. Text; one of "context", "seed". |
An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.
{
"input": {
"stages": [
"visit",
"cart",
"paid"
],
"opening": 10,
"maximumDrops": [
4,
3
],
"seed": 42
},
"output": {
"counts": [
10,
7,
6
],
"drops": [
3,
1
],
"conversions": [
0.7,
0.8571428571428571
],
"stages": [
"visit",
"cart",
"paid"
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- Stage IDs must be distinct and there must be one drop cap per transition. Zero counts remain zero. This constructs a conserved synthetic funnel rather than measured conversion behavior or causal evidence.
- A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
- The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.
Alternative or Complementary methods
For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.
References
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