Product / Outcomes
Define what productive means.
Create canonical success metrics, map them to the systems your company already runs, and deliver realized outcomes back into recommendations and capital decisions.
Outcome ontology
Arbitir provides the graph. The customer owns the definition.
A metric becomes useful for allocation only when its logic becomes explicit and versioned.
- 01
Success metrics
Create shared entities for revenue, pipeline, meetings, retention, expansion, product delivery, and other outcomes.
- 02
Definition builder
Map metrics to CRM or operating-system fields, filters, ownership rules, dates, lags, and exclusions.
- 03
Outcome graph
Connect proximal work output to customer outcomes and longer-horizon results.
- 04
Actions
Use realized outcome data to improve the next recommendation, policy, playbook, or test.
Definition builder
Make the denominator explicit.
Store success metrics as reviewable product objects rather than burying them in dashboard logic.
Closed-won revenue
- Source
- HubSpot deals
- Condition
- Stage is Closed won
- Value
- ARR
- Attribution
- Deal owner
- Exclude
- Partner channel, $0 deals
Definition preview
427 deals
and $18.2m of pipeline over the selected period.
Evidence ladder
Build stronger evidence instead of hiding uncertainty.
Every figure that puts spend beside an outcome carries a confidence tag, low, medium or high, with a plain sentence saying why. None of them is proof of cause.
01
Low confidence
Spend beside results
- Says
- This team spent $96,240 and created $11.4m of pipeline.
- Does not say
- Nothing about what produced what.
- What raises it
- Record a change and read the same team after.
02
Medium confidence
Before and after, beside peers
- Says
- Pipeline per AI dollar went $1,140 to $1,870. Peers went $1,160 to $1,210.
- Does not say
- That the budget produced it. Peers are a baseline, not a control.
- What raises it
- Match peers on covariates. Check the pre-period.
03
High: matched peers
Closely matched peers, checked
- Says
- Consistent with an effect under stated assumptions.
- Does not say
- Cause. The assumptions are printed with it.
- What raises it
- Randomize who is offered the capacity.
04
High: randomized
A randomized test
- Says
- An estimated lift, with a permutation range.
- Does not say
- Still an estimate. Never proof.
- What raises it
- Nothing. The top is still not certainty.
Start allocating AI spend toward what works.
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