Enterprise Adobe Experience Platform
Adobe Experience Platform - Unified Customer Data and Journey Orchestration
We implement Adobe Experience Platform as the unified customer data backbone for enterprise organisations - from XDM schema design and Real-Time CDP audience segmentation to Adobe Journey Optimizer cross-channel journeys, Customer AI propensity models, and enterprise-grade privacy and consent management.
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The Challenge
Siloed Customer Data Prevents Personalisation at Enterprise Scale
Enterprises with customer data fragmented across dozens of systems cannot build accurate audience segments, cannot personalise experiences in real time, and cannot measure customer journeys across channels. The gap between the personalised experience customers expect and what disconnected data infrastructure can deliver grows wider every year, costing revenue and customer loyalty.
Why QuickHire
Why Enterprises Choose QuickHire
XDM Schema Architecture
Every AEP implementation begins with a dedicated schema design workshop. We design XDM schemas for all data sources before ingestion begins, preventing the downstream re-ingestion and profile corruption that incorrect schema design causes.
Identity Graph Design
Our identity resolution architects design deterministic and probabilistic linking rules that maximise profile completeness while preventing incorrect identity collapse from shared device scenarios - the most common source of Real-Time CDP data quality failure.
Journey Optimizer Expertise
We implement Adobe Journey Optimizer for real-time triggered journeys across email, push, SMS, in-app, and web channels, using Real-Time Customer Profile data to personalise every message at the individual level within seconds of triggering events.
Customer AI Implementation
Customer AI churn and conversion propensity models are configured, trained, and validated against historical event data, then integrated into Real-Time CDP segments and AJO journey decisioning for AI-driven audience targeting.
Privacy and Governance Framework
We implement the full AEP data governance framework including field-level usage labels, policy enforcement for destination activation, consent management CMP integration, and Privacy Service API configuration for data subject request processing.
Multi-Source Data Engineering
Our data engineering team builds XDM-compliant ETL pipelines, source connector configurations, and streaming ingestion for any source system - CRM, analytics, mobile, call centre, POS, or offline files - with monitoring and data quality validation from day one.
Challenges
Common Enterprise Pain Points
Schema Design Irreversibility
XDM schema choices made at the beginning of an AEP implementation are very difficult to change once data has been ingested at scale. Incorrect field type choices, missing identity fields, or poorly designed custom field groups require full dataset re-ingestion - a costly and disruptive operation that makes upfront schema design workshops non-negotiable.
Identity Graph Accuracy
Incorrect identity linking - caused by shared device identifiers, missing namespace prioritisation rules, or aggressive probabilistic linking - produces inaccurate unified profiles that undermine the accuracy of every audience segment built on top of them. Identity graph design requires deep expertise in identity namespace hierarchy and linking rule logic.
Streaming Ingestion Costs
Real-time streaming ingestion into AEP is significantly more expensive per event than batch ingestion. Organisations that default to streaming ingestion for all data sources often face unexpectedly high AEP consumption costs. We design ingestion strategies that use streaming only for the real-time use cases that require it, and batch for all other data types.
Data Governance Complexity
Enterprise organisations with sensitive customer data must configure AEP data usage labels and policies to prevent regulated data from being activated to inappropriate destinations. Without governance configuration, AEP provides no automatic enforcement, creating compliance risk that is often not visible until an audit.
Organisational Adoption of CDP Model
Adopting AEP requires marketing, analytics, and data engineering teams to change how they think about audience management, campaign targeting, and personalisation. Without structured change management and training, AEP is often underutilised after implementation, with teams reverting to familiar but less capable legacy tools.
Our Approach
AEP Implemented for Real Business Outcomes, Not Platform Completion
Our AEP implementations are structured around specific business use cases - reducing churn, increasing conversion, improving customer lifetime value - rather than generic platform activation checklists. Each use case has a defined data requirement, segment definition, journey design, and success metric agreed before implementation begins, ensuring every workstream delivers measurable value.
Data Foundation Design
XDM schema workshop, identity namespace strategy, source connector architecture, and ingestion pattern design documented and signed off before any ingestion begins.
Real-Time CDP Activation
Segment taxonomy design, streaming and batch segment evaluation configuration, and destination connector setup for all required advertising and engagement platform activations.
Journey Optimizer Implementation
AJO journey design for triggered, transactional, and campaign use cases across email, push, SMS, and in-app channels with Real-Time Customer Profile personalisation.
Customer AI and Decision Management
Propensity model configuration, training data validation, score integration into segments and journeys, and Decision Management offer catalogue setup for 1:1 personalisation.
Delivery Models
How We Deliver
AEP data foundation covering XDM schema, identity graph, source connectors, Real-Time Customer Profile, initial segment library, and 2 to 4 destination activations for advertising and email platforms.
Combined Real-Time CDP and Adobe Journey Optimizer implementation covering the data foundation plus triggered journey orchestration across email, push, SMS, and in-app channels with Customer AI integration.
Ongoing AEP platform operations covering ingestion monitoring, segment maintenance, journey optimisation, Customer AI model refreshes, and quarterly strategic advisory to evolve the platform with business priorities.
Capabilities
Technical Capability Matrix
Engagement Models
How We Engage
Choose the model that fits your programme governance, budget cycle, and team structure.
Our Process
From Discovery to Delivery
Use Case and Data Discovery
Weeks 1-3Business use case prioritisation, data source inventory, identity namespace mapping, data readiness assessment, and AEP sandbox governance design with technology and marketing stakeholders.
Schema and Identity Design
Weeks 3-5XDM schema workshop, field group design, identity namespace priority rules, source-to-XDM field mapping specifications, and governance label schema documented with stakeholder sign-off.
Data Ingestion and Profile Build
Weeks 6-14Source connector configuration, ETL pipeline development, streaming ingestion setup, identity graph population, and Real-Time Customer Profile validation across all connected data sources.
Segmentation, Journeys, and AI
Weeks 15-24Segment library build, destination activation configuration, AJO journey design and testing, Customer AI model training and validation, and Decision Management offer catalogue setup.
Go-Live, Measurement, and Optimisation
OngoingProduction segment activation, AJO journey activation, Customer AI score integration, privacy framework validation, ROI baseline measurement, and managed services handover.
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Security & Compliance
Enterprise-Grade Security by Default
Governance
Programme Governance
AEP Governance Board
Monthly review of schema evolution requests, new dataset additions, destination connector additions, and policy enforcement configuration changes, ensuring all changes are impact-assessed before Production promotion.
Data Quality Monitoring
Automated ingestion pipeline monitoring with alerting for batch job failures, streaming latency breaches, identity graph anomalies, and segment evaluation errors with documented SLA response times.
Privacy Compliance Review
Quarterly review of consent management configuration accuracy, Privacy Service request processing logs, data governance label completeness across all active schemas, and policy enforcement test results.
Sandbox Promotion Process
Formal schema and configuration change promotion process from Development to Staging to Production, with documented testing evidence required at each stage before Production promotion is authorised.
Use Case ROI Reviews
Quarterly business impact reviews assessing segment activation performance, journey conversion metrics, Customer AI model accuracy, and Decision Management offer engagement rates against the business case targets.
Team Structure
Your Enterprise Team
AEP implementations are staffed with Adobe Experience Platform Solution Architects, XDM data engineers, Real-Time CDP specialists, Adobe Journey Optimizer developers, Customer AI analysts, and privacy and governance consultants. Our team holds Adobe Experience Platform certified practitioner credentials and has delivered AEP programmes across retail, financial services, healthcare, and telecommunications globally.
Project Lifecycle
From Kickoff to Production
Discovery and Use Cases
Use case priority list, data source inventory, identity namespace map, sandbox governance model, data readiness report.
Schema and Identity Design
XDM schema specifications, field group designs, identity priority rules, source-to-XDM field mapping, governance label schema.
Ingestion and Profile
Source connectors, ETL pipelines, streaming ingestion, identity graph, Real-Time Customer Profile validation report.
Activation and AI
Segment library, destination activations, AJO journeys, Customer AI models, Decision Management offer catalogue.
Go-Live and Operations
Production activation, privacy framework sign-off, ROI baseline, monthly operational reports, managed services handover.
Case Studies
Enterprise Outcomes
A global fashion retailer with 28 million customers had loyalty, e-commerce, and in-store purchase data in four separate systems with no unified customer view, preventing accurate churn prediction and personalised retention campaigns.
We implemented AEP with XDM schema design for all four source systems, identity graph linking loyalty card to digital and in-store purchase behaviour, Customer AI churn model, and AJO retention journeys activated to email and push channels.
A global bank needed to activate first-party customer data for digital advertising without violating financial services data privacy regulations, as third-party cookie deprecation eliminated their previous audience targeting capability.
We implemented Real-Time CDP with data governance policies enforcing financial services usage restrictions, activated consented customer segments to Google Customer Match and Meta Custom Audiences using hashed first-party identifiers within the regulatory permitted use framework.
A national telecoms operator was losing 18 percent of postpaid subscribers annually to competitors, with no predictive capability to identify at-risk subscribers before they initiated porting requests.
We implemented Customer AI churn propensity on AEP, training on 36 months of billing, usage, service interaction, and complaint history data, activating high-propensity churn segments to AJO retention journeys with personalised offer decisioning.
FAQ
Frequently Asked Questions
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