Propensity-Based Audience Segmentation

Uses propensity scoring in Adobe Experience Platform to segment audiences by predicted likelihood to act, enabling more precise targeting than past-behavior or rule-based segmentation alone.

The Problem

“Propensity-Based Audience Segmentation for Precision Marketing”

Organizations face these key challenges:

1

Static rule-based segments do not capture future likelihood to act

2

High-value prospects may be missed if they lack obvious historical signals

3

Audience definitions are slow to update as customer behavior changes

4

Analysts spend significant time exporting data and recalibrating thresholds

Impact When Solved

Higher conversion and response rates from targeting likely respondersLower media and campaign waste by suppressing low-propensity usersFaster audience creation with reusable predictive score pipelinesBetter personalization by combining scores with eligibility and policy rules

The Shift

Before AI~85% Manual

Human Does

  • •Define audience rules using recency, frequency, demographics, and engagement filters
  • •Export customer data and review campaign results to adjust segment thresholds
  • •Build batch audiences for campaigns and manually refresh them over time
  • •Decide which customers to target or suppress based on past behavior patterns

Automation

    With AI~75% Automated

    Human Does

    • •Choose target outcomes, score cutoffs, and campaign eligibility rules
    • •Approve audience strategies, suppressions, and channel prioritization decisions
    • •Review performance exceptions and adjust business rules when results shift

    AI Handles

    • •Score customers by predicted likelihood to convert, click, churn, or purchase
    • •Refresh audience membership as new customer behavior changes predicted intent
    • •Combine propensity scores with eligibility and suppression rules to create audiences
    • •Monitor score and audience performance and flag meaningful changes for review

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence91%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Technologies

    Technologies commonly used in Propensity-Based Audience Segmentation implementations:

    DestinationsOther
    4 mentions
    Merge policyOther
    4 mentions
    Real-Time Customer ProfileOther
    4 mentions
    Adobe Experience Platform Customer AIOther
    3 mentions
    XDM Individual ProfileOther
    3 mentions
    +1 more technologies(sign up to see all)

    Key Players

    Companies actively working on Propensity-Based Audience Segmentation solutions:

    Oracle UnitySalesforce Data Cloud/Einstein

    Real-World Use Cases

    Propensity-score-based audience segmentation in Adobe Experience Platform

    Adobe predicts how likely each customer is to do something, then lets marketers build audience lists using those scores.

    Predictive scoring followed by rule-based audience selectionproduction-ready workflow documented as part of adobe experience platform customer ai and segment builder.
    10.0

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