From "how did customers get here?" to "how much did it matter?"
Two Adobe applications work together on Adobe Experience Platform — one focused on the customer journey, the other on marketing effectiveness.
Customer Journey Analytics (CJA)
Adobe's customer intelligence application built on Adobe Experience Platform. Unlike traditional web analytics, CJA analyzes journeys across online and offline channels using person-level identity resolution and data stitching — connecting website, mobile, CRM, POS, loyalty, call center, and warehouse data into a unified view.
Marketing Campaign Analytics (MCA)
Adobe's marketing measurement and campaign intelligence solution, providing both customer-level and aggregate-level perspectives on marketing performance across the full funnel — through multi-touch attribution, marketing mix modeling, and incrementality.
Customer Journey Analytics
Person-level intelligence that connects every interaction — online and offline — into a single view of the customer.
Four core capabilities that power person-level intelligence
Identity Stitching
Connects interactions across devices and channels into a single journey — e.g., a customer discovers Carhartt on mobile, researches on desktop, receives an email, and purchases in-store.
Journey Analysis
Visualizes how customers move through experiences before conversion. What paths lead to purchase? Where do customers abandon? How do audiences behave?
Cross-Channel Attribution
Measures the contribution of multiple touchpoints throughout a journey — visibility into how channels work together, not just the final interaction.
Omnichannel Analysis
Analyzes digital and offline interactions together — including ecommerce activity, retail transactions, loyalty activity, and customer service interactions.
Marketing Campaign Analytics
Both customer-level and aggregate-level perspectives on marketing performance across the full funnel — four measurement approaches within one framework.
Multi-Touch Attribution (MTA)
Customer-level analysis of how paid search, social, email, and retail touchpoints contribute to conversions and acquisition journeys.
Marketing Mix Modeling (MMM)
Statistical modeling of media spend, promotions, pricing, seasonality, weather, and competitive activity — ideal for Carhartt's seasonal, weather-driven demand.
Incrementality Measurement
Measures causal impact — did brand campaigns, loyalty initiatives, or partnership programs generate outcomes that would not otherwise have occurred?
Unified Measurement
Combines MTA and MMM within a common framework for both tactical and strategic marketing analysis.
Understanding the difference between CJA and MCA
CJA answers "How did customers get here?" — MCA answers "How much impact did our marketing have?"
| Customer Journey Analytics (CJA) | Marketing Campaign Analytics (MCA) | |
|---|---|---|
| Focus | Customer journeys | Marketing performance |
| Level | Person-level analysis | Campaign-level analysis |
| Attribution | Attribution across touchpoints | Attribution, MMM, and incrementality |
| Question | "What happened?" | "What happened and how much did it matter?" |
| Analyzes | Journeys, audiences, products, content, experiences | Campaigns, channels, spend, ROI, business outcomes |
| Designed for | Understanding customer behavior | Understanding marketing effectiveness |
Measuring the impact of brand marketing
CJA Customer Journey Analytics
CJA analyzes how customers interacted with campaign content, which channels influenced conversion, how customers moved from awareness to purchase, and differences between audience segments.
A customer engages with campaign content, later visits the website, subscribes to email, and eventually purchases outerwear. CJA visualizes and attributes those interactions.
MCA Marketing Campaign Analytics
MCA analyzes overall campaign performance, incremental revenue generated, marketing effectiveness, and impact relative to seasonality and other factors.
The campaign generated measurable business impact beyond baseline purchasing behavior.
Attribution and incrementality, together
CJA helps organizations understand customer behavior and attribution across channels and touchpoints. MCA helps organizations understand campaign performance through MTA, MMM, and incrementality measurement.
Attribution
Measures contribution. Customer Journey Analytics provides attribution across online and offline customer journeys — connecting ecommerce, retail, loyalty, WIP, and Reworked into a single customer view.
Incrementality
Measures causation. Marketing Campaign Analytics provides incrementality measurement to understand the true business impact of marketing investments — what actually drove outcomes beyond baseline behavior.
Together, they provide both a customer-level understanding of how people buy and a marketing-level understanding of how campaigns influence business outcomes.
AI capabilities and data foundations
Coworker, MCP & LLM Insights, and Data Mirror — the AI layer and data plumbing that power person-level intelligence.
Adobe CX Enterprise Coworker
An AI-powered assistant that lets users interact with analytics, audiences, journeys, and workflows through natural language — no complex query building required.
Natural Language Analytics
Ask business questions directly: "Why did conversion rates change last week?"
Root Cause Analysis
Investigates changes in traffic, conversion, campaigns, geography, or product performance automatically.
Audience Creation
"Create an audience of customers who purchased workwear in the last 90 days but have not purchased outerwear."
Executive Summaries
Stakeholder-ready performance summaries — e.g., monthly ecommerce performance reports — generated automatically.
MCP, LLM Insights & AI across Adobe
AI capabilities spanning Adobe Analytics, CJA, and MCA — from secure connectivity to measuring how AI interactions shape journeys.
Model Context Protocol (MCP)
Adobe's framework for securely connecting AI systems to analytics platforms and business applications. Capabilities include schema discovery, governed reporting, secure tool execution, and permission-aware AI interactions.
LLM Insights
Measures how AI interactions influence customer journeys and business outcomes — AI-driven engagement, AI-assisted conversion analysis, and AI touchpoints within the customer journey.
Data Mirror
Synchronizes data between Adobe Experience Platform and cloud data warehouses — including Snowflake, Databricks, and BigQuery — with inserts, updates, and deletions via Change Data Capture (CDC), which automatically identifies and syncs changed records without requiring full dataset reloads.
Data Mirror enables Carhartt's existing warehouse investments to feed directly into CJA for person-based analysis — connecting offline retail, loyalty, and Reworked data with digital engagement without rebuilding data infrastructure.