Brand Concierge conversational experience
This guide provides a comprehensive implementation reference for AI-powered conversational experiences using Adobe Brand Concierge, integrated with Adobe Experience Platform (AEP) and Real-Time Customer Data Platform (RT-CDP). It is designed for solution architects, marketing technologists, and implementation engineers who need to deploy brand-safe conversational agents across digital properties.
It covers all viable approaches for deploying conversational experiences, from product advisory chatbots to full site navigation assistants, with guidance on when to choose each option. The plan addresses agent configuration, brand governance, content integration, deployment strategies, profile enrichment from conversation signals, and analytics optimization.
Brand Concierge enables brands to deploy intelligent conversational agents that understand brand voice, access approved product catalogs and content, deliver personalized recommendations based on real-time profile data, and capture intent and sentiment signals back into the unified customer profile. The result is a conversational experience that feels natural and on-brand while enriching the organization’s understanding of each customer.
Use case overview
Organizations increasingly seek to transform static digital experiences into dynamic, AI-powered conversations that guide customers through discovery, product selection, and purchase decisions. Adobe Brand Concierge addresses this by providing an orchestrated conversational AI layer that sits atop existing digital properties, powered by AEP Agent Orchestrator.
This pattern is distinct from traditional chatbot implementations because it is natively integrated with AEP’s unified profile, uses brand governance guardrails to ensure every response aligns with brand standards, and feeds conversational signals back into the customer data platform for downstream personalization and activation.
The target audience includes digital experience teams, e-commerce managers, content strategists, and marketing technologists who need to deploy intelligent conversational experiences that drive engagement, conversion, and profile enrichment.
Key business objectives
The following business objectives are supported by this use case pattern.
Deliver personalized customer experiences
Tailor content, offers, and messaging to individual preferences, behaviors, and lifecycle stage.
KPIs: Engagement, Conversion Rates, Customer Satisfaction (CSAT)
Learn more about delivering personalized customer experiences
Improve customer engagement
Increase interaction frequency and depth across all digital and physical touchpoints.
KPIs: Engagement, Time On (web) Page, Open Rates
Learn more about improving customer engagement
Increase conversion rates
Improve the percentage of visitors and prospects who complete desired actions such as purchases, sign-ups, or form submissions.
KPIs: Conversion Rates, Lead Conversion, Cost Per Lead
Learn more about increasing conversion rates
Acquire new customers
Expand the customer base through targeted acquisition campaigns, lookalike audiences, and paid media optimization.
KPIs: New Customers, Customer Acquisition Cost, Prospect/Lead Conversion
Learn more about acquiring new customers
Example tactical use cases
The following scenarios illustrate how this pattern can be applied in practice.
- Product discovery assistant – Deploy a conversational agent on product listing pages that asks qualifying questions and narrows product recommendations based on customer needs, preferences, and budget
- Guided comparison advisor – Help customers compare products side-by-side through natural dialogue, highlighting differences relevant to their stated priorities
- Size and fit concierge – Guide apparel or footwear shoppers through size selection using conversational Q&A, reducing returns and increasing purchase confidence
- Subscription or plan selector – Walk customers through service tier or subscription plan options with personalized recommendations based on usage patterns and stated needs
- Site navigation assistant – Help visitors find relevant content, support resources, or product categories based on their stated intent, reducing bounce rates on complex sites
- Pre-purchase consultation – Provide high-consideration purchase guidance (for example, electronics, financial products, insurance) through multi-turn conversations that build toward a recommendation
- Loyalty program concierge – Help loyalty members discover rewards, understand tier benefits, and find redemption opportunities through conversational interaction
- Re-engagement conversation – Initiate proactive conversational outreach to returning visitors based on previous browse history or abandoned cart items
- Live agent escalation with context – Seamlessly hand off complex inquiries to live sales or support agents while preserving full conversation context and customer profile data
- Post-purchase support and upsell – Engage customers after purchase with setup assistance, complementary product suggestions, and satisfaction check-ins through conversational channels
Key performance indicators
The following KPIs help measure the success of this use case pattern.
Use case pattern
Brand Concierge conversational experience
Transform digital properties into AI-powered, brand-safe conversational experiences that guide customer discovery through natural dialogue, enrich profiles with intent and sentiment signals, and deliver personalized product recommendations.
Function chain: Agent Configuration > Brand Governance Setup > Content Integration > Conversational Experience Deployment > Profile Enrichment > Analytics & Optimization
Applications
The following applications are used to implement this use case pattern.
- Brand Concierge – AI-powered conversational experience application providing the agent orchestrator, Product Advisor Agent, Site Advisory Agent, brand governance, and conversational analytics
- Adobe Experience Platform (AEP) – Unified data foundation providing XDM schemas, identity resolution, real-time customer profiles, and data collection infrastructure for conversational signals
- Real-Time CDP (RT-CDP) – Customer data platform providing real-time profile lookup for personalized conversations, audience segmentation from conversational signals, and profile enrichment with intent and sentiment data
Foundational functions
The following foundational capabilities must be in place for this use case pattern. For each function, the status indicates whether it is typically required, assumed to be pre-configured, or not applicable.
Supporting functions
The following capabilities augment this use case pattern but are not required for core execution.
Application functions
This plan exercises the following functions from the Application Function Catalog. Functions are mapped to implementation phases rather than numbered steps.
Brand Concierge
Real-Time CDP
Prerequisites
The following items must be in place before implementation begins.
- [ ] Adobe Brand Concierge entitlement is active for the organization
- [ ] AEP and RT-CDP licenses are provisioned with sufficient profile and event volume entitlements
- [ ] Brand guidelines document available defining voice, tone, approved messaging, and prohibited topics
- [ ] Product catalog or content repository prepared for integration (AEM assets, PIM data, or structured product feed)
- [ ] Web properties identified for conversational experience deployment with technical access for SDK integration
- [ ] Live agent infrastructure available if handoff is required (contact center platform, CRM integration)
- [ ] Consent management framework in place for conversational data capture and profiling
- [ ] Web SDK or Mobile SDK already deployed on target properties (or planned for concurrent deployment)
- [ ] Stakeholder alignment on conversation scope (product advisory only, site navigation, or both)
- [ ] Privacy and legal review completed for AI-powered conversational data capture and usage
Implementation options
The following sections describe different approaches for implementing this use case pattern.
Option A: Product Advisor deployment
Best for: E-commerce and retail organizations focused on guided product discovery, comparison, and recommendation experiences that drive conversion and average order value.
How it works:
The Product Advisor Agent is configured as the primary conversational specialization. It connects to the product catalog, understands product attributes and relationships, and guides customers through natural dialogue to arrive at personalized recommendations. The agent uses brand governance guardrails to ensure recommendations align with business priorities (for example, promoting in-stock items, highlighting margin-favorable products).
Product Advisor integrates with the real-time customer profile to access purchase history, browse behavior, and preference data, enabling recommendations that account for what the customer already owns, has previously considered, or is likely to need based on their profile. Conversations are captured as experience events and intent signals flow back into the profile for downstream use.
Key considerations:
- Requires a well-structured product catalog with rich attribute data for effective recommendations
- Product data must be kept current to avoid recommending out-of-stock or discontinued items
- Brand governance must define how the agent handles competitive product mentions and price comparisons
Advantages:
- Directly drives measurable revenue impact through guided purchase conversion
- Reduces product return rates through better-informed purchase decisions
- Captures high-value product affinity and intent signals for downstream personalization
- Natural extension of existing e-commerce experiences
Limitations:
- Requires ongoing product catalog maintenance and synchronization
- Limited to product-centric conversations; site navigation questions may go unaddressed
- Effectiveness depends on catalog data quality and completeness
Experience League:
Option B: Site Advisory deployment
Best for: Organizations with complex digital properties (media, financial services, healthcare, technology) where visitors need navigation assistance to find relevant content, resources, or self-service tools.
How it works:
The Site Advisory Agent is configured as the primary conversational specialization. It indexes the site content structure, understands page relationships and content categories, and adapts its guidance based on visitor behavior signals and stated intent. When a visitor engages, the agent interprets their needs and directs them to the most relevant content, tools, or resources.
Site Advisory uses real-time behavioral signals (current page, referral source, navigation path) combined with profile data (previous visits, content preferences, customer tier) to provide contextually relevant navigation assistance. This is particularly valuable on sites with deep content hierarchies, multiple product lines, or complex self-service workflows.
Key considerations:
- Requires comprehensive content indexing and regular re-crawling as site content changes
- Most effective on sites with significant content breadth where visitors commonly struggle to find what they need
- Brand governance should define scope boundaries (which site areas the agent can navigate to)
Advantages:
- Reduces bounce rates and improves content discoverability on complex sites
- Captures navigation intent signals that reveal content gaps and user experience issues
- Lower implementation complexity than Product Advisor (no product catalog integration required)
- Provides analytics insights into what visitors are looking for but cannot find
Limitations:
- Less directly tied to revenue conversion than product-focused conversations
- Requires content to be well-structured and regularly updated for accurate guidance
- May need frequent retraining as site structure evolves
Experience League:
Option C: Combined Product Advisor + Site Advisory deployment
Best for: Organizations that want a comprehensive conversational experience covering both product discovery and site navigation, typically large retail or B2C brands with extensive digital properties and diverse visitor intents.
How it works:
Both the Product Advisor Agent and Site Advisory Agent are configured within the Brand Concierge orchestrator. The agent orchestrator uses intent detection to route conversations to the appropriate specialization – product-related queries go to Product Advisor while navigation and content-finding queries go to Site Advisory. The orchestrator manages seamless transitions between specializations within a single conversation.
This approach provides the most complete conversational experience, handling the full range of visitor needs from “Help me find a product” to “Where can I check my order status?” Brand governance guardrails apply uniformly across both specializations, ensuring consistent brand voice regardless of conversation topic.
Key considerations:
- Higher implementation complexity requiring both product catalog and content integration
- Intent routing between specializations must be well-tuned to avoid misdirected conversations
- Brand governance setup is more extensive to cover both product and navigation contexts
Advantages:
- Provides the most comprehensive conversational experience for visitors
- Single entry point handles diverse visitor intents without requiring separate interfaces
- Cross-specialization conversations (for example, product question that leads to support navigation) handled naturally
- Richest profile enrichment from diverse conversation signals
Limitations:
- Highest implementation effort and ongoing maintenance
- Requires coordination between product catalog and content teams
- More complex testing and quality assurance requirements
- Brand governance configuration is more involved
Experience League:
Option comparison
Choose the right option
Start by assessing your primary business objective and digital property characteristics:
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If your primary goal is driving product conversion and your digital property is commerce-focused, choose Option A (Product Advisor). This is the most common starting point for retail and e-commerce brands.
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If your primary goal is improving content discoverability and your site has deep content hierarchies or complex self-service workflows, choose Option B (Site Advisory). This is ideal for media, financial services, healthcare, and technology companies.
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If you need comprehensive coverage and have both product commerce and content navigation needs, choose Option C (Combined). Consider starting with one specialization and adding the second after the first is stable and optimized.
A phased approach is recommended for most organizations: deploy one specialization first, validate performance and gather learnings, then expand to the combined deployment.
Implementation phases
The following phases outline the recommended implementation sequence.
Phase 1: Agent configuration
Application function: Brand Concierge: Agent Configuration
Configure the core Brand Concierge agent orchestrator, including selecting agent specializations (Product Advisor, Site Advisory, or both), configuring base agent behavior, and establishing the connection between Brand Concierge and AEP for profile access and event capture.
Decision: Agent specialization selection
Determine which agent specializations should be activated for this deployment.
Decision: Conversation initiation model
Determine how conversations should begin on the digital property.
Configure the agent
UI navigation: Experience Platform > AI Assistant > Brand Concierge > Agent Configuration
Key configuration details:
- Define the agent name and description that will appear in the conversational interface
- Select which AEP sandbox contains the customer profile and event data the agent should access
- Configure the agent orchestrator to route queries between specializations based on intent detection
- Set conversation session parameters (timeout duration, maximum conversation length, concurrent session limits)
- Enable real-time profile lookup integration so the agent can access visitor profile data during conversations
Where options diverge:
For Option A (Product Advisor):
Enable the Product Advisor specialization and configure its connection to the product catalog data source. Set product recommendation parameters including maximum recommendations per response, product attribute display preferences, and comparison handling rules.
For Option B (Site Advisory):
Enable the Site Advisory specialization and configure its connection to the site content index. Set navigation parameters including content scope boundaries, page category handling, and deep-link generation preferences.
For Option C (Combined):
Enable both specializations and configure the orchestrator’s intent routing logic. Define routing rules that determine when a conversation should be handled by Product Advisor versus Site Advisory, and how transitions between specializations should be managed within a single conversation.
Experience League documentation:
Phase 2: Brand governance setup
Application function: Brand Concierge: Brand Governance Setup
Configure the brand governance guardrails that shape all conversational interactions. This includes brand voice and tone definitions, approved content boundaries, prohibited topics, response style guidelines, and escalation rules. Brand governance ensures every AI-generated response aligns with brand standards.
Decision: Governance strictness level
Determine how tightly the brand governance guardrails should constrain conversational responses.
Decision: Off-topic handling strategy
Determine how the agent should handle questions outside its configured scope.
Configure brand governance
UI navigation: Experience Platform > AI Assistant > Brand Concierge > Brand Governance
Key configuration details:
- Define brand attributes: brand name, tagline, mission, values, and personality traits that inform conversational tone
- Set tone parameters: formality level, humor tolerance, empathy level, and assertiveness for product recommendations
- Configure approved content boundaries: topics the agent is authorized to discuss and topics that are explicitly prohibited
- Define response format guidelines: maximum response length, use of lists versus prose, emoji policy, and link formatting
- Set escalation triggers: conditions that should automatically route a conversation to a live agent (for example, complaint detection, repeated dissatisfaction signals, high-value customer identification)
- Configure competitive mention handling: how the agent should respond when visitors ask about competitor products
- Define disclaimer and legal notice requirements: mandatory disclosures for regulated industries
Experience League documentation:
Phase 3: Content integration
Application function: Brand Concierge: Content Integration, Product Advisor Configuration, Site Advisory Configuration
Configure the content sources that ground conversational responses in accurate, brand-approved information. This includes product catalog integration, AEM content connections, knowledge base imports, and content refresh schedules.
Decision: Product catalog integration method
Determine how product data should be provided to the Product Advisor Agent. (Option A and C only)
Decision: Content refresh frequency
Determine how often the agent’s content knowledge base should be updated.
Configure content sources
UI navigation: Experience Platform > AI Assistant > Brand Concierge > Content Sources
Key configuration details:
- Connect product catalog data sources with field mapping for product name, description, attributes, pricing, availability, images, and category hierarchy
- Configure content indexing for site pages, knowledge base articles, FAQ content, and support documentation
- Set content scope boundaries defining which content the agent can reference and which is excluded
- Configure content fallback behavior when the agent cannot find relevant content to answer a question
- Set up content quality rules: minimum content confidence threshold for inclusion in responses, citation requirements, and source attribution
Where options diverge:
For Option A (Product Advisor):
Focus on product catalog integration with rich product attribute mapping. Configure the Product Advisor Agent’s recommendation logic including how many products to suggest, how to handle out-of-stock items, how to present product comparisons, and how to incorporate customer profile data (purchase history, browse behavior) into recommendation ranking.
For Option B (Site Advisory):
Focus on site content indexing with page hierarchy mapping. Configure the Site Advisory Agent’s navigation logic including how to interpret visitor intent, which content categories to prioritize, how to handle ambiguous navigation requests, and how to adapt suggestions based on the visitor’s current page context and session behavior.
For Option C (Combined):
Configure both product catalog and site content sources. Ensure the content routing logic correctly assigns content to the appropriate specialization and that cross-references between product content and site navigation content are properly mapped.
Experience League documentation:
Phase 4: Conversational experience deployment
Application function: Brand Concierge: Conversational Experience Deployment, Low-Code Flow Management, Live Agent Handoff; RT-CDP: Real-Time Profile Lookup
Deploy the conversational experience on target digital properties, including channel configuration, widget customization, profile lookup integration for personalization, live agent handoff rules, and low-code tools for ongoing content management.
Decision: Deployment channel
Determine which channel(s) the conversational experience should be deployed to.
Decision: Personalization depth for conversations
Determine how much customer profile data the agent should use to personalize conversations.
Decision: Live agent handoff configuration
Determine whether conversations should be escalable to live human agents.
Deploy the conversational experience
UI navigation: Experience Platform > AI Assistant > Brand Concierge > Deployment
Key configuration details:
- Configure the conversational widget appearance: position, color scheme, avatar, welcome message, and interaction style (text, voice, or both)
- Integrate with Web SDK or Mobile SDK for event capture and profile resolution
- Configure real-time profile lookup to access customer attributes, segment memberships, and recent activity during conversations
- Set up live agent handoff integration with the contact center platform, including context transfer protocol, queue routing, and agent notification
- Enable low-code flow management tools for marketing teams to update conversation starters, promotional messaging, seasonal content, and flow variations without developer involvement
- Configure conversation session persistence rules: how long conversation history is retained, whether conversations can resume across sessions, and cross-device conversation continuity
Experience League documentation:
Phase 5: Profile enrichment
Application function: Brand Concierge: Conversational Profile Enrichment; RT-CDP: Profile Enrichment, Audience Evaluation
Configure the capture and enrichment pipeline that feeds conversational signals back into the AEP unified customer profile. This includes mapping conversation events to XDM, extracting intent and sentiment signals, creating computed attributes from conversational data, and building audiences based on conversational behaviors.
Decision: Conversational signal capture scope
Determine which conversational signals should be captured and written to the customer profile.
Decision: Audience creation from conversational data
Determine whether audiences should be created based on conversational behaviors for downstream activation.
Configure profile enrichment
UI navigation: Experience Platform > Customer > Profiles > Computed attributes (for derived signals); Customer > Audiences > Create audience (for conversational audiences)
Key configuration details:
- Map conversational events to XDM ExperienceEvent schema fields capturing conversation ID, message count, topics discussed, products referenced, sentiment scores, and resolution status
- Configure Brand Concierge profile enrichment to write intent and preference signals to the unified profile
- Create computed attributes from conversational event data: total conversations (lifetime), dominant product category interest (30 days), average sentiment score (90 days), conversation-to-purchase conversion rate
- Define streaming or batch audience segments based on conversational signals for downstream activation (for example, “Visitors who discussed Product Category X in the last 7 days but did not purchase”)
- Validate profile enrichment by looking up sample profiles to confirm conversational attributes are populated
Experience League documentation:
Phase 6: Analytics and optimization
Application function: Brand Concierge: Conversational Analytics
Set up analytics dashboards and reporting for measuring conversational experience performance, identifying optimization opportunities, and tracking KPIs. This includes Brand Concierge built-in analytics, optional CJA integration for cross-channel conversation impact analysis, and ongoing optimization workflows.
Decision: Analytics depth
Determine what level of conversational analytics is needed.
Configure analytics and optimization
UI navigation: Experience Platform > AI Assistant > Brand Concierge > Analytics; Analytics Platform > Workspace (for CJA)
Key configuration details:
- Review Brand Concierge built-in analytics dashboards: conversation volume trends, engagement rate, completion rate, CSAT scores, recommendation acceptance rate, and handoff frequency
- Configure CJA connection to include conversational event datasets for cross-channel analysis (if choosing CJA integration)
- Build CJA workspace analysis for conversation-to-conversion attribution, identifying which conversation topics correlate with purchase behavior
- Set up conversation quality monitoring: track topics where the agent struggles, common unanswered questions, and sentiment trends over time
- Define optimization workflows: regular review cadence for brand governance updates, content refresh triggers, and conversation flow improvements based on analytics insights
Experience League documentation:
Implementation considerations
The following sections cover guardrails, common pitfalls, best practices, and trade-off decisions to keep in mind during implementation.
Guardrails and limits
- Brand Concierge conversational experiences are subject to AI response generation rate limits; concurrent conversation capacity depends on entitlement tier
- Real-time profile lookup during conversations is subject to Profile API rate limits per sandbox – Real-Time Customer Profile guardrails
- Conversational event data ingestion follows standard AEP streaming ingestion limits – Ingestion guardrails
- Product catalog size and content index volume are subject to Brand Concierge content integration limits
- Maximum of 25 computed attributes per sandbox applies to conversational signal aggregations – Computed attributes guardrails
- Maximum of 4,000 segment definitions per sandbox applies to conversational audiences – Segmentation guardrails
Common pitfalls
- Insufficient brand governance definition: Deploying without thorough brand governance configuration results in off-brand responses that damage customer trust. Invest significant time in Phase 2 defining tone, boundaries, and escalation rules before deployment.
- Stale product catalog data: Product Advisor recommendations based on outdated inventory, pricing, or availability data frustrate customers and erode confidence. Establish automated content refresh pipelines with validation checks.
- Overaggressive proactive engagement triggers: Setting behavioral triggers too aggressively (for example, triggering conversation after 3 seconds on page) annoys visitors and increases bounce rates. Start with conservative triggers and tune based on engagement data.
- Neglecting anonymous visitor experience: Focusing personalization only on authenticated visitors ignores the majority of traffic. Design conversation flows that provide value to anonymous visitors using in-session behavioral signals.
- Skipping profile enrichment configuration: Deploying conversations without capturing signals back to the profile wastes valuable intent and preference data. Configure profile enrichment in parallel with deployment, not as an afterthought.
- Ignoring live agent handoff experience: Poor handoff experiences (lost context, repeated questions, long wait times) damage the overall conversational experience more than not offering handoff at all. Test the full handoff flow end-to-end before launch.
Best practices
- Start with a single agent specialization (Product Advisor or Site Advisory) and expand after establishing baseline performance.
- Conduct brand governance workshops with marketing, legal, and customer experience stakeholders before configuring guardrails.
- Use progressive personalization: start conversations with session-context-based responses and deepen personalization as the visitor provides information or authenticates.
- Implement A/B testing of conversation starters, prompts, and recommendation presentation formats using the low-code flow management tools.
- Schedule regular (weekly or biweekly) review of conversation analytics to identify content gaps, common failure points, and optimization opportunities.
- Create a feedback loop between conversational analytics and brand governance updates – use conversation data to refine tone, add new approved topics, and adjust escalation rules.
- Monitor conversation sentiment trends as an early warning system for product issues, site problems, or brand perception shifts.
- Design conversation flows that naturally capture profile-enriching signals without making the interaction feel like an interrogation.
Trade-off decisions
Conversation personalization depth vs. privacy simplicity
Deeper profile integration enables more personalized and effective conversations, but increases data collection complexity, consent requirements, and privacy compliance burden.
- Deep personalization favors: Higher conversion rates, better recommendation quality, richer profile enrichment, and more engaging conversations for returning customers
- Privacy simplicity favors: Faster deployment, simpler consent management, lower regulatory risk, and a privacy-first brand positioning
- Recommendation: Start with progressive personalization that works well for anonymous visitors and adds profile-based personalization for authenticated sessions. This provides value at all identification levels while keeping privacy compliance manageable. Implement consent capture for conversational profiling aligned with existing consent frameworks.
Brand governance strictness vs. conversational naturalness
Strict brand governance guardrails ensure every response aligns with brand standards, but overly rigid constraints make conversations feel robotic and reduce engagement.
- Strict governance favors: Brand safety, regulatory compliance, consistent messaging, and predictable agent behavior
- Flexible governance favors: Natural conversation flow, higher engagement, better customer satisfaction, and the ability to handle a wider range of queries
- Recommendation: Begin with moderate governance and tighten or loosen based on conversation analytics. Monitor the rate of “I cannot help with that” responses as an indicator of over-restriction. Use the low-code flow management tools to iterate on governance settings quickly without developer involvement.
Real-time content refresh vs. system performance
Real-time content synchronization ensures the agent always has current product and content data, but continuous refresh consumes more infrastructure resources and can introduce latency.
- Real-time refresh favors: Accuracy for inventory-sensitive recommendations, time-sensitive promotions, and rapidly changing content
- Scheduled refresh favors: System stability, predictable resource consumption, and lower infrastructure costs
- Recommendation: Use daily content refresh as the default, with near-real-time refresh only for inventory availability and pricing data that materially affects the customer experience. Monitor content accuracy metrics to determine if the refresh frequency is adequate.
Comprehensive signal capture vs. data management overhead
Capturing every conversational signal provides the richest profile enrichment and analytics, but increases data volume, storage costs, and governance complexity.
- Full signal capture favors: Advanced analytics, ML model training, comprehensive profile enrichment, and maximum downstream personalization value
- Selective capture favors: Lower storage costs, simpler data governance, faster profile lookup performance, and easier compliance with data minimization principles
- Recommendation: Start with intent and preference signal capture (the middle ground) and expand to full signal capture only after validating that the additional data creates measurable downstream value. Apply dataset expiration policies to conversational event data to manage storage growth.
Related documentation
The following resources provide additional information for implementing this use case pattern.
Brand Concierge
Adobe Experience Platform
Data collection and integration
Identity and profile
Audiences and segmentation
Data governance and privacy
Monitoring and observability
Analytics and reporting
Guardrails