Customer analytics & insight generation
This guide describes the customer analytics and insight generation use case pattern, which connects Adobe Experience Platform datasets to Customer Journey Analytics to build data views, freeform analysis workspaces, computed metrics, dashboards, and mobile scorecards, and to optionally publish CJA-defined audiences back to Adobe Experience Platform for activation.
It is designed for solution architects, marketing technologists, and implementation engineers who need to understand what this pattern does, the business objectives it supports, the tactical use cases it enables, and the Adobe applications involved.
Unlike the other patterns in the taxonomy which focus on activation and engagement (sending messages, personalizing content, activating audiences), this pattern focuses on understanding – analyzing customer behavior, measuring campaign performance, identifying trends, and generating insights that inform strategy and optimization decisions.
Use case pattern
Customer analytics & insight generation
Build cross-channel analysis workspaces, computed metrics, and dashboards to understand customer behavior and campaign performance.
Execution plan: Data Connection > Data View Configuration > Workspace Analysis > Dashboard Publishing
Use case overview
Organizations need to understand how customers behave across channels, how campaigns perform, where customers drop off in their journeys, which content resonates, and how different segments retain over time. Customer analytics and insight generation addresses this need by connecting the rich cross-channel data in Adobe Experience Platform to Customer Journey Analytics, where analysts can build freeform workspaces, create custom metrics, configure attribution models, and publish dashboards for stakeholder consumption.
The pattern serves multiple audiences: marketing analysts who need deep exploratory analysis, campaign managers who need performance dashboards, product managers who need engagement and retention insights, and executives who need at-a-glance KPI scorecards. The implementation approach varies based on the primary analytical focus – campaign performance measurement, cross-channel journey analysis, analysis-driven audience activation, or guided product insights.
Key business objectives
The following business objectives are supported by this use case pattern.
Improve analytics & reporting
Enhance reporting capabilities for faster, more actionable marketing insights through unified dashboards and self-service tools.
- KPIs: Efficiency, Productivity
See Improve Analytics & Reporting for more information on this business objective.
Enable data-driven decision making
Empower teams with self-service analytics, real-time customer insights, and AI-powered predictions to guide strategy.
- KPIs: Efficiency, Productivity
See Enable Data-Driven Decision Making for more information on this business objective.
Improve marketing attribution
Accurately measure the impact of marketing touchpoints, channels, and campaigns on conversion and revenue outcomes.
- KPIs: Efficiency, Incremental Revenue
See Improve Marketing Attribution for more information on this business objective.
Optimize marketing spend & ROI
Optimize marketing budget allocation by understanding which channels and campaigns deliver the highest return.
- KPIs: Efficiency, Incremental Revenue
See Optimize Marketing Spend & ROI for more information on this business objective.
Example tactical use cases
The following are examples of tactical use cases that can be implemented with this pattern.
- Campaign performance dashboard – delivery metrics, engagement rates, conversion, and revenue attribution across email, SMS, push, and paid media campaigns
- Customer journey fallout analysis – identify where customers drop off in purchase, registration, or onboarding funnels
- Cohort retention analysis – measure how well different acquisition cohorts retain over weeks, months, and quarters
- Channel attribution modeling – compare first-touch, last-touch, linear, and time-decay attribution to understand which channels drive conversions
- Content performance analysis – identify which content resonates most by segment, channel, and lifecycle stage
- Product usage and adoption analytics – track feature adoption, engagement frequency, and user growth trends
- Customer lifecycle stage analysis – segment and analyze customers by lifecycle stage (new, active, at-risk, lapsed)
- Marketing mix optimization dashboard – compare channel investment against revenue contribution
- Cross-channel engagement scoring and reporting – build composite engagement scores from web, app, email, and campaign interactions
Key performance indicators
The following KPIs help measure the success of this use case pattern.
Applications
The following applications are used in this use case pattern.
- Customer Journey Analytics (CJA) – Connections, data views, workspace analysis, guided analysis, computed metrics, dashboards, audience publishing, and content analytics
- Adobe Experience Platform (AEP) – Data lake, datasets, XDM schemas, profile and event data that feed CJA connections
Related documentation
The following resources provide additional information for this use case pattern.