MOXIE STUDIO / EVALUATION 01

Primary Channel
Handoff Fixtures

Twelve synthetic observations for missing contacts, conflicting sources and preservation. For implementers who need a clear, repeatable handoff test, not another attribution claim.

Free evaluation sample. Not live-CRM validation.
Expected results apply only to the declared example policy. AI-assisted original fixtures; no customer data, vendor affiliation or measured time-savings claim.

One declared policy. Twelve edge cases.

Freeze channel at creation. Preserve populated opportunity source. Otherwise use a single distinct creation-contact source; use Unknown for no usable source and needs_decision for conflict. Blank original source stays blank.

IDObservationChannel / associationWhy this result
S01Paid inboundPaid
associated
Creation contact supplies Paid.
S02Event inboundEvent
associated
Creation contact supplies Event.
S03Manual outboundOutbound
missing
Explicit opportunity source wins even without a contact.
S04Import without contactUnknown
missing
No source at creation; Unknown is explicit, not blank.
S05Contact without sourceUnknown
associated
Associated does not imply a usable source.
S06Preserve populated sourceReferral
associated
Existing opportunity source wins; preserve Referral.
S07Opportunity/contact conflictPaid
associated
Explicit Paid wins over contact Event.
S08Conflicting contactsneeds_decision
multiple
Conflicting creation contacts require a policy decision.
S09Late associationUnknown
late
No contact at creation. Late Paid must not backfill frozen channel.
S10Re-enrollmentEvent
associated
Second enrollment preserves original Event and frozen Event channel.
S11Source changed after handoffEvent
associated
Contact changed Event to Paid after handoff; frozen channel remains Event.
S12Duplicate import replayPaid
associated
Replay same opportunity key; retain one output row and Paid source.

Before using the comparison

  1. Agree the creation boundary and source precedence. If policy is unresolved, record a decision instead of guessing.
  2. Map four unique observed headers in rule-map.json. Version any changed policy and expectations together; JSON descriptions do not execute rules.
  3. Stage lifecycle cases using synthetic inputs only. These CSV files are specifications, not vendor import templates.
  4. Run the passing control, then mutation-tests.py, then your own observed CSV. A pass proves equality, not actual workflow execution.

Desktop and small-screen matrix: scroll horizontally inside the bordered region. No scripts, external assets, tracking, network calls or input collection are required.

Did these cases improve your handoff?

Free synthetic evaluation only. Orders and payment are closed. Compare your usual preparation with the pack; count mapping time separately. No time saving or live-CRM reliability has been demonstrated.

  1. What kind of workflow do you implement?
  2. What was your baseline preparation time, mapping time and fixture preparation time?
  3. Which scenario IDs exposed a policy gap, if any?

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