Analytics and attribution

Boom does not decide what a good outcome is for you. You define it, per initiative, and everything else is measured against that definition: a rate, a value, a funnel, and the list of individual conversations that counted.

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Defining success

An initiative can carry more than one named success metric. Each one says what signal counts as success, and how to aggregate it. Three kinds of signal: Each metric aggregates as a count, a sum, an average or a max, so “how many came back” and “how much revenue came back” are the same mechanism with a different aggregation.

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The attribution window

A conversation’s window opens when Boom sends its first outbound message and closes 48 hours after the journey ends. That grace period is fixed and not configurable today, so a purchase two weeks later is not attributed, by design. A person Boom never messaged has no window at all, and is excluded from both the numerator and the denominator rather than counted as a failure.

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What you get

Per node, from the messages themselves with nothing to configure: sent, delivered, read and replied counts, how each branch split, and median wait times. Useful for finding the step where people fall out. A funnel that starts at Sent rather than at enrolled, with each stage showing its share of the previous stage and of the first, ending in a shared succeeded stage. A success rate whose denominator is the people Boom actually messaged, not everyone who entered. Worth knowing before comparing it to a number from another tool, which may well count differently. Successes over time, bucketed by day, switchable between count and value when the metric aggregates a value. The evidence behind the number: the individual successes that matched, each linking to that participant and to the run that produced it. This is the part people ask for when they do not believe the dashboard.

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Reading the raw data instead

Everything the dashboard builds on is available over the API and MCP, covered in extraction:

• the aggregate summary for an initiative, with coverage and distributions per field
• each participant’s extracted values, including the quote the value came from and a confidence
• full transcripts, in batches, so you can pull every conversation without looping one at a time
• the step by step run timeline for a participant, when you need to know what the engine did rather than what was said

CSV and XLSX exports live in the app, including one row per participant with one column per extracted field.

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What this is not

There is no experiment primitive: no randomized allocation, no significance testing. You can compare variants by branching a journey on an attribute and reading each path’s success metric, which answers most questions, but the split is deterministic and the statistics are yours to do. There are also no outbound webhooks and no warehouse sync. Reads are polling.

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