On this page: Discover the CX Enterprise Coworker skills available for Decisioning in Adobe Journey Optimizer — understanding why an offer was or wasn’t shown to a profile or segment, and creating, explaining, simulating, and optimizing eligibility rules and ranking formulas — with detailed guidance, example prompts, and best practices.
Decisioning capabilities in Adobe Journey Optimizer help you explain offer decisions and create, test, and optimize eligibility rules and ranking formulas through natural-language prompts. Use Decisioning Explainer to understand why an offer was or was not shown to a profile or segment. Use Rules & Ranking to create and simulate eligibility rules and ranking formulas before applying them to your decisioning strategy.
Learn more:
- Coworker skills for Journey Optimizer — overview of Coworker skills across Journeys, Loyalty, Content Management, and Decisioning in Journey Optimizer.
- Coworker documentation — overview of Coworker’s Campaigns, Chat, and Projects capabilities.
- Coworker Chat UI guide — how to access and navigate Coworker Chat.
Decisioning Explainer decisioning-explainer
Decisioning Explainer answers, in natural language, why a specific offer was or wasn’t shown to a given profile — or, more broadly, why a segment of profiles isn’t seeing an offer. It walks the full decisioning stack for the requested profile (or segment) and time window: which offers were eligible, which eligibility rule included or excluded each one, whether frequency or fatigue capping suppressed the offer, the final ranking scores and which strategy or AI model produced them, and which candidate pool (item collection) the profile was evaluated against.
This addresses a common challenge for marketers: explaining why one offer ranked above another, or why a specific offer decision happened the way it did. Decisioning Explainer is read-only — it explains decisions but does not modify rules, ranking formulas, or selection strategies.
Key use cases
Prompting best practices
- Reference IDs when known: Provide the profile ID, offer name, or segment name to get a precise trace rather than a general answer.
- Include a time window: Specify a date or date range when asking why an offer’s visibility changed, so Coworker can scope the trace correctly.
- Ask for the ranking breakdown directly: If you want scoring detail, ask explicitly for the ranking score or the factors that influenced the outcome.
- Use segment-level questions for trends: When investigating why a group of profiles isn’t seeing an offer, ask about the segment rather than a single profile to get the dominant reason.
Rules & Ranking rules-ranking
Rules & Ranking gives marketers AI-powered assistance for creating, understanding, and testing decisioning logic, without needing to write or manually validate PQL syntax. It covers four core capabilities: natural language rule creation, plain-English rule and ranking formula explanation, simulation against up to 3 test profiles, and PQL optimization. It’s scoped to eligibility rules and ranking formulas — it doesn’t create or edit selection strategies or decision policies.
Key use cases
Prompting best practices
- Provide the target condition explicitly: When creating or modifying a rule, state the exact audience, attribute, or exclusion condition you want.
- Reference the rule or formula directly: When asking for an explanation, simulation, or optimization, make sure the rule or formula you mean is open or clearly identified.
- Ask for edge cases: When simulating, ask Coworker to generate edge-case profiles to stress-test a condition, not just typical ones.
- Review before publishing: Check a generated or optimized rule’s logic and simulation results before publishing it.
This section contains structured knowledge intended to support interpretation, retrieval, and question answering related to this topic.
For complete understanding, this information should be combined with the documentation on this page. Neither source is intended to stand alone; the page describes the feature, while this section provides additional context that helps disambiguate terminology, intent, applicability, and constraints.
- 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)