p202 attribution journey

One conversion's journey: its touches, the identity signals that linked them, and every model's credit

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p202 CLI reference

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01Run it

Replace the <placeholders> with your own values, or use the builder below.

p202 attribution journey <conv_id>

02What you get

Captured from the real binary, run against a demo store with 30 days of traffic. Switch tabs to see the same run as JSON, the shape scripts and agents parse.

p202 attribution journey 31
$ p202 attribution journey 31
amount:           208.00000
click_id:         801
conv_id:          31
conv_time:        1790089761
counted:          true
credits:          [{"model_id":1,"model_name":"Last touch","model_type":"last_touch","touches":[{"click_id":801,"credit":"1.00000000","position":1,"revenue":"208.00000"}]},{"model_id":2,"model_name":"First touch","model_type":"first_touch","touches":[{"click_id":668,"credit":"1.00000000","position":0,"revenue":"208.00000"}]},{"model_id":3,"model_name":"Position based (40/20/40)","model_type":"position_based","touches":[{"click_id":668,"credit":"0.50000000","position":0,"revenue":"104.00000"},{"click_id":801,"credit":"0.50000000","position":1,"revenue":"104.00000"}]}]
journey:          {"built_at":1791539316,"built_lookback_days":30,"identified":true,"touches":2,"truncated":false}
pending:          
recorded_amount:  208.00000
touches:          [{"campaign_id":1,"campaign_name":"Spring Collection / US","click_id":668,"click_time":1790013494,"position":0,"ppc_account_id":2,"ppc_account_name":"Search Ads - Shopping","signals":["vid"]},{"campaign_id":1,"campaign_name":"Spring Collection / US","click_id":801,"click_time":1790085190,"position":1,"ppc_account_id":3,"ppc_account_name":"Social Ads - Prospecting","signals":["cust","vid"]}]
Real output · p202 1.9.77 · demo dataexit 0

03Build your command

Pick values and the command line writes itself, quoted and ready to paste.

p202 attribution journey

Set flags below; the command updates as you type.

04For agents

Running this from an agent

  • Read the same facts as JSON: p202 commands attribution journey --json.
  • With AI_AGENT, CLAUDECODE or another agent variable set, output is compact JSON and errors arrive on stderr as a JSON envelope with a hint.
  • Exit codes: 0 ok, 1 bad input, 2 auth, 3 network, 4 server error, 5 partial failure.
p202 commands attribution journey --json
{
  "path": "p202 attribution journey",
  "use": "journey <conv_id>",
  "short": "One conversion's journey: its touches, the identity signals that linked them, and every model's credit",
  "runnable": true
}