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Agentforce Sales Consultant

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Agentforce Sales Consultant Study Checklist
  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Architect
  • Slack Administrator

Checklist progressProgress

0/195Learned

  • Goal-first SMART success metrics for an Agentforce Sales rollout
  • Activity measures versus outcome measures in sales KPIs
  • One key sales metric, pipeline viability and monthly measurement
  • Matching business needs to core Agentforce Sales capabilities
  • Reducing rep data entry and manual activity logging
  • Supporting sales teams that work from anywhere
  • Sales process versus sales methodology
  • Common sales cycle stages from research to relationship building
  • B2B versus B2C selling models and Person Accounts considerations
  • Lead capture channels and auto-response planning
  • Lead qualification criteria and lead picklist design
  • Marketing-to-sales lead handoff and marketing automation alignment
  • Lead sharing model choices for controlling lead visibility
  • Opportunity stage design with exit criteria and Path guidance
  • Campaign attribution and ROI tied to opportunities
  • Aligning sales, marketing and finance stakeholders on process changes
  • Rollout success team roles and early end-user involvement
  • Salesforce Inbox versus Einstein Activity Capture versus base email integration
  • When to recommend Sales Engagement for high-volume outreach
  • Sales Engagement standard cadences, quick cadences and automated actions
  • Sales Territories versus role hierarchy and account teams for territory-aligned access
  • Territory planning, models and account assignment rules
  • Salesforce Forecasting versus pipeline reports for revenue projection
  • Choosing Salesforce Forecasting forecast types for how the business sells
  • Matching a sales need to predictive, generative or agentic AI
  • Choosing among Agentforce Sales agents for prospecting, deal management and coaching
  • Agentforce Engagement agent versus rep-worked Sales Engagement cadences
  • Mapping business questions to trackable sales metrics
  • Designing data capture so sales KPIs are reportable
  • Reports versus dashboards for sales KPI visibility
  • Core dashboards sales leaders rely on
  • Report types and cross filters for relationship-based KPIs
  • Report formats and summary formulas for calculated sales KPIs
  • Dynamic dashboards and running user for role-based KPI views
  • Embedding report charts and dashboards where reps work
  • Reporting pipeline change over time
  • Forecast and quota attainment reporting
  • Activity KPI reporting with Einstein Activity Capture
  • AppExchange dashboard packs to accelerate KPI reporting
  • Reviewing and iterating KPI dashboards with the business
  • Assessing the current sales process and user experience before build
  • Executive sponsorship and stakeholder alignment before build
  • Defining success metrics from key business questions
  • Systems change management planning and governance
  • Anticipating user resistance and designing for acceptance
  • Super users and change champions in adoption planning
  • Communication plan timing and launch message content
  • Training strategy goals and user expectations
  • Training methods and role-based content for a sales rollout
  • Placing readiness activities in the implementation lifecycle
  • Building in a sandbox versus changing production directly
  • User acceptance testing and sandbox choice before go-live
  • Change sets versus other tools for moving configuration between orgs
  • Application lifecycle management and production change governance
  • Deployment timing and release scheduling
  • Pilot groups versus a single big-bang launch
  • Placing deployment activities in the implementation lifecycle
  • Adoption usage metrics after go-live
  • Data quality patterns as signals of adoption problems
  • Post-launch feedback collection and user satisfaction
  • Matching the remedy to the root cause of low adoption
  • Recognition and peer success stories to sustain adoption
  • Validating business outcomes against pre-launch benchmarks
  • Managing user access changes after go-live
  • Handling changing requirements and enhancement requests after go-live
  • Long-term systems roadmap for continuous improvement
  • Evaluating Salesforce seasonal releases for new value
  • Placing post-go-live activities in the implementation lifecycle
  • Project lifecycle phases and where artifacts and activities belong
  • Waterfall versus Agile for an Agentforce Sales implementation
  • Salesforce Adaptive Methodology stages, roles and kickoff
  • Statement of work content and accurate project scoping
  • Project management plan purpose, contents and template sizing
  • Defining in-scope and out-of-scope requirements to prevent scope creep
  • Scope changes, phased delivery and schedule-budget trade-offs
  • Identifying and responding to project risks
  • Monitoring project progress, governance and issue escalation
  • Implementation schedule and go-live milestones
  • Researching a customer's business before discovery
  • Stakeholder analysis and discovery workshop participants
  • Choosing elicitation techniques for sales requirements
  • Uncovering the business need behind a requested feature
  • Process maps, journey maps, and system overview diagrams in discovery
  • Reviewing an existing Salesforce org's usage and constraints in discovery
  • Current-state versus future-state gap analysis
  • User stories and acceptance criteria for sales requirements
  • Fit-gap analysis when translating requirements into an Agentforce Sales design
  • Co-creating a solution vision through storyboarding
  • Prioritizing use cases by business value and effort
  • Business analysis activities across the implementation lifecycle
  • End-to-end lead-to-close process design from a process map
  • Lead conversion design and custom field mapping
  • Translating a sales methodology into opportunity stages
  • Placing sales activities in the lead-to-close stages
  • Path guidance and key fields for stage-by-stage coaching
  • Enforcing required information at opportunity stage transitions
  • Opportunity contact roles for the buying committee
  • Discount approval routing before quote delivery
  • Kanban and Pipeline Inspection for opportunity pipeline reviews
  • Standard features, then configuration, then customization for sales requirements
  • Requirements that justify custom development over declarative tools
  • AppExchange solutions versus custom-built functionality
  • Another Salesforce product versus custom build or third-party app
  • Experience Cloud partner sites for channel selling and deal registration
  • Email and calendar integration options for Outlook and Gmail users
  • Inside-sales productivity tools for cadences, calling and in-browser prospecting
  • Slack integration for deal collaboration and sales team communication
  • Salesforce mobile app for field reps versus custom mobile development
  • Org-wide defaults as the baseline for sales record access
  • Role hierarchy for sales management visibility
  • Owner-based versus criteria-based sharing rules
  • Account Teams versus Opportunity Teams for collaborative selling
  • Account and opportunity team access considerations
  • Matching a sales record-access requirement to the right sharing feature
  • Permission sets versus profiles for sales object and field access
  • Permission set groups and muting for sales job personas
  • Permissions that override sharing for delegated sales operations
  • Products, the standard price book and price book entries
  • Custom price books for segment-specific pricing and access
  • Opportunity products, sales price and opportunity amount
  • Product schedules for revenue and quantity over time
  • Multiple quotes per opportunity and quote syncing
  • Quote templates and customer-facing quote PDFs
  • Retiring products and price books: deactivate, archive or delete
  • Corporate, personal and record currency with conversion rates
  • Multi-currency enablement implications for an existing org
  • Advanced currency management and dated exchange rates
  • Currency-specific price book entries on multi-currency opportunities
  • Campaign hierarchy and campaign types for performance roll-up
  • Campaign member statuses for tracking responses
  • Campaign Influence versus primary campaign source attribution
  • When to use leads versus direct contacts and opportunities
  • Lead capture with Web-to-Lead and lead imports
  • Lead routing with assignment rules and queues
  • Lead Status values and lead processes
  • Enforcing qualification criteria before lead conversion
  • Multiple sales processes with opportunity record types
  • Opportunity splits for shared revenue credit

Explain the use cases and considerations for data migrations and integrations

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Scoping which legacy sales data to migrate

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Cleansing and mapping legacy data before migration

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Choosing a data import tool for a sales data migration

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Migration load sequence and preserving record relationships

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Handling automation and validation rules during bulk data loads

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Test loads, backups and cutover for a data migration

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System of record and master data across integrated systems

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Real-time versus batch integration with back-office systems

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Data quality across integration extraction, transformation and consumption

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Given a scenario, outline the scalability implications of a solution

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  • Workload and design factors that affect org scalability
  • Report and list view performance on large data volumes
  • Data tiering with archiving and external objects
  • Account data skew from overloaded parent records
  • Lookup skew and lock contention on heavily referenced records
  • Ownership skew from a single generic record owner
  • Role hierarchy design for scalable record access
  • Sharing recalculation impact of ownership and hierarchy changes
  • Large data load planning to avoid lock contention and slow sharing
  • Automation complexity and batch timing at scale

Discuss approaches for managing sales data quality in Salesforce

0/10

  • Business impact of poor sales data quality
  • Assessing sales data quality across its core dimensions
  • Data management plan standards and data ownership
  • Validation rules versus required fields for enforcing sales data standards
  • Streamlining sales data entry to improve completeness and consistency
  • Matching rules and fuzzy matching for duplicate detection
  • Duplicate rule actions when reps create sales records
  • Resolving existing duplicates across the org
  • Data enrichment from trusted third-party sources
  • Ongoing data quality monitoring and stewardship after go-live
  • Assistive Agentforce actions versus autonomous sales agents
  • Record summaries and daily briefings for seller preparation
  • Sales Emails versus Agentforce for drafting customer emails
  • Agentforce actions for closing and prioritizing deals
  • Buying committee and relationship insight actions
  • Get Forecast Guidance for sales manager pipeline reviews
  • Agentforce Sales Management for pipeline and account work
  • Generative features within Einstein Conversation Insights
  • Agentforce Engagement for lead generation and nurturing
  • Agentforce Sales Coach for rep role-play and feedback
  • Standard sales prompt templates versus custom Prompt Builder templates
  • Prerequisite Sales features behind standard Agentforce sales actions
  • Einstein Lead Scoring for lead prioritization
  • Einstein Opportunity Scoring for deal prioritization
  • Einstein Activity Capture and its AI insights
  • Einstein Deal Insights and AI in Pipeline Inspection
  • Einstein Forecasting predictions for forecast accuracy
  • Einstein Conversation Insights for call analysis
  • Data readiness for Einstein predictive features
  • Ethical risk categories of generative AI in sales
  • Applying Salesforce Trusted AI Principles to sales AI use cases
  • Salesforce guidelines for responsible generative AI
  • Safeguards against inaccurate AI-generated sales content
  • Einstein Trust Layer protections for sales data
  • Human oversight and judgment with AI sales agents
  • Honesty and transparency in AI-driven sales outreach
  • Bias and fairness in predictive lead and opportunity scoring
  • Explainability of Einstein predictions for sales users

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  1. Agentforce Sales Consultant Checklist
  3. Data Management

Data quality across integration extraction, transformation and consumption

Explainer

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Practice Question

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Next conceptWorkload and design factors that affect org scalability

Checklist progress

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  1. Agentforce Sales Consultant Checklist
  3. Data Management
Data quality across integration extraction, transformation and consumption

Explainer

Learn More

Practice Question

Keep going

Next conceptWorkload and design factors that affect org scalability

Explainer

When data moves between systems through an integration, its quality can be gained or lost at three steps: when it is extracted from the source, when it is transformed, and when the target application consumes it. This can be framed as a matrix that checks six data traits (completeness, accuracy, integrity/consistency, timeliness, uniqueness, validity) at each of those steps. The matrix gives a consultant a structured set of questions to ask on any data migration or integration project.
Core information
  • Data and integrations depend on each other: good data leads to better integration quality, and an efficient integration leads to better data quality. Data that is not integrated properly cannot give a holistic picture, and an integration that does not process data properly affects the quality of insights and analysis.
More details and nuances
  • Some transformation losses cannot be reversed. Summarizing or rolling up data can remove the ability to drill down later unless the details are kept explicitly. A date transformation that keeps only the month and year loses the day, and the day cannot be retrieved in the future.

Explainer

When data moves between systems through an integration, its quality can be gained or lost at three steps: when it is extracted from the source, when it is transformed, and when the target application consumes it. This can be framed as a matrix that checks six data traits (completeness, accuracy, integrity/consistency, timeliness, uniqueness, validity) at each of those steps. The matrix gives a consultant a structured set of questions to ask on any data migration or integration project.
Core information
  • Data and integrations depend on each other: good data leads to better integration quality, and an efficient integration leads to better data quality. Data that is not integrated properly cannot give a holistic picture, and an integration that does not process data properly affects the quality of insights and analysis.
More details and nuances
  • Some transformation losses cannot be reversed. Summarizing or rolling up data can remove the ability to drill down later unless the details are kept explicitly. A date transformation that keeps only the month and year loses the day, and the day cannot be retrieved in the future.