Purchase Despite Error
The Purchase Despite Error metric tracks how many users completed a purchase after encountering an error in the same session. This highlights resilience in user behavior and may uncover hidden UX or performance issues that don’t prevent conversion but should still be addressed.
Metric model
- Name
metric
- Type
- string
- Description
Always
despite_errors
for this metric.
- Name
value
- Type
- integer
- Description
Number of purchases made despite encountering errors.
- Name
previous
- Type
- integer
- Description
Value for the same metric in the previous period.
- Name
change
- Type
- float
- Description
Percent change between periods.
- Name
formattedValue
- Type
- string
- Description
Display-friendly value for current period.
- Name
formattedPrevious
- Type
- string
- Description
Display-friendly value for previous period.
- Name
period.id
- Type
- string
- Description
Time period ID.
- Name
period.name
- Type
- string
- Description
Human-readable name for the time period.
- Name
periodRange.current.start
- Type
- string
- Description
Start timestamp for the current period.
- Name
periodRange.current.end
- Type
- string
- Description
End timestamp for the current period.
- Name
periodRange.previous.start
- Type
- string
- Description
Start timestamp for the previous period.
- Name
periodRange.previous.end
- Type
- string
- Description
End timestamp for the previous period.
- Name
performance.duration
- Type
- float
- Description
Time taken to compute metric in milliseconds.
- Name
performance.formatted
- Type
- string
- Description
Human-readable duration string.
Retrieve this metric
Returns the number of purchases that happened after an error occurred in the same session.
Optional query parameters
- Name
period
- Type
- string
- Description
Period over which to measure the metric. Examples:
today
yesterday
7d
30d
this_month
Request
curl -G http://localhost:3000/api/metrics/despite_errors \
-H "Authorization: Bearer {token}" \
-d period=7d
Response
{
"metric": "despite_errors",
"value": 21,
"previous": 19,
"change": 10.53,
"formattedValue": "21",
"formattedPrevious": "19",
"period": {
"id": "7d",
"name": "Last 7 days"
},
"periodRange": {
"current": {
"start": "2025-03-16T08:50:55.031Z",
"end": "2025-03-23T08:50:55.030Z"
},
"previous": {
"start": "2025-03-09T08:50:55.031Z",
"end": "2025-03-16T08:50:55.030Z"
}
},
"performance": {
"duration": 981.91,
"formatted": "982 ms"
}
}
Use cases
- Identify errors that don’t block purchases but still affect UX
- Track user persistence or friction points
- Correlate error occurrences with drop-offs and successful conversions