Decisioning용 Coworker experience-decisioning-coworker-skills

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• TL;DR: This page documents two CX Coworker skills for Decisioning in Adobe Journey Optimizer: Decisioning Explainer, which explains in natural language why a specific offer was or wasn’t shown to a profile or segment; and Decisioning Rule Workflow, which creates, explains, simulates, and optimizes eligibility rules and ranking formulas in natural language, without requiring PQL syntax.

Intents

• Understand why a specific offer was or wasn’t shown to a profile.
• Understand why an offer’s visibility to a customer or segment changed over time.
• Understand how an offer was ranked or selected over other eligible offers.
• Get an aggregated explanation of why a segment of profiles isn’t seeing an offer.
• Create a new eligibility rule, or edit an existing one, from a plain-language description.
• Get a plain-English explanation of what an existing eligibility rule or ranking formula does.
• Simulate an eligibility rule or ranking formula against test profiles.
• Rewrite a rule or formula to fit within PQL size limits without changing its logic.

Glossary

• Decisioning Explainer (product-specific): CX Coworker skill that explains, in natural language, why an offer was or wasn’t shown to a profile or segment, by tracing eligibility, capping, ranking, and candidate pool evaluation.
• Decisioning Rule Workflow (product-specific): CX Coworker skill that creates, explains, simulates, and optimizes Decisioning eligibility rules and ranking formulas using natural language, without requiring the marketer to write or read PQL syntax directly.
• Eligibility rule: a condition that includes or excludes an offer as a candidate for a given profile; Decisioning Explainer identifies which rule included or excluded each candidate offer, and Decisioning Rule Workflow can create, explain, simulate, or optimize the rule itself.
• Ranking formula: the logic used to score and order eligible offers; Decisioning Rule Workflow can create, explain, simulate, or optimize a ranking formula, the same way it does for eligibility rules.
• Capping: frequency or fatigue suppression logic that can prevent an otherwise-eligible offer from being shown; Decisioning Explainer can identify when capping suppressed an offer.
• Candidate pool (item collection): the set of offers a profile is evaluated against during a decisioning event; Decisioning Explainer reports which candidate pool was used.
• PQL (Profile Query Language): the expression syntax underlying Decisioning eligibility rules and ranking formulas; Decisioning Rule Workflow generates, explains, and optimizes PQL without the marketer needing to write or validate it manually.

Guardrails

• Decisioning Explainer and Decisioning Rule Workflow are both available for all customers who have access to Coworker and Decisioning.
• Decisioning Explainer is read-only: it explains decisions but does not modify rules, ranking formulas, or selection strategies.
• Decisioning Rule Workflow simulation supports up to 3 test profiles at a time, manually entered or AI-generated.
• Decisioning Rule Workflow is scoped to eligibility rules and ranking formulas; it does not create or edit selection strategies or decision policies.

Terminology

• Do not confuse: “eligibility” (whether an offer qualifies as a candidate) is distinct from “ranking” (how qualifying candidates are ordered) and “capping” (frequency/fatigue suppression applied after eligibility and ranking) — Decisioning Explainer reports on all three separately.
• Do not confuse: Decisioning Explainer explains why a decision already made turned out the way it did for a real profile or segment; Decisioning Rule Workflow explains, creates, simulates, or optimizes the rule or formula configuration itself, independent of any specific real-world decisioning event.

FAQ

• Can Decisioning Explainer explain a decision for a single profile? Yes, ask why a specific profile did or didn’t see a specific offer, on a specific date.
• Can Decisioning Explainer explain decisions across a segment? Yes, it can aggregate the explanation across a segment to surface the dominant reason a group of profiles isn’t seeing an offer.
• Does Decisioning Explainer show ranking scores? Yes, it can return the final ranking score for each offer and which strategy or AI model produced it.
• Can Decisioning Explainer change a rule or ranking formula? No, it is read-only and does not modify decisioning configuration.
• Can Decisioning Rule Workflow create a brand-new eligibility rule from scratch? Yes, describe the target audience or condition in plain language and it generates the PQL rule.
• Can Decisioning Rule Workflow explain a rule someone else built? Yes, it can explain any existing eligibility rule or ranking formula in plain English, including what it includes, excludes, and each condition’s meaning.
• How many test profiles can Decisioning Rule Workflow simulate against at once? Up to 3, manually entered or AI-generated, including edge cases.
• Does Decisioning Rule Workflow change a rule’s logic when optimizing it? No, PQL optimization only makes the syntax more concise to fit size limits; it preserves the original logic and outcome.

Personas:

• marketer: Can create an eligibility rule or ranking formula from a plain-language description, without writing PQL syntax. Can simulate the rule or formula against up to three test profiles, including AI-generated edge cases, to see pass-fail results or ranking scores. The page states that Rules & Ranking does not create or edit selection strategies or decision policies. Where: “Rules & Ranking”.

Onboarding:

• Level: Beginner
• First minimal step: Describe the target audience or condition in plain language to create a new eligibility rule.
• Success check: Coworker generates the PQL rule from the plain-language description.
• Previous: Learn about Coworker skills across Journey Optimizer before using the Decisioning-specific skills: Work with AI (start/ai-features.md)