Salesforce Analytics Cloud (Tableau CRM)

Salesforce Analytics Cloud (Tableau CRM) Training || Salesforce Analytics Cloud (Tableau CRM) certification Training || Salesforce Analytics Cloud (Tableau CRM) Online training || Salesforce Analytics Cloud (Tableau CRM) self-paced training || Salesforce Analytics Cloud (Tableau CRM) Instructor-Led training

Key Features of Training:

30 Hrs Instructor-led Training
Mock Interview Session
Project Work & Exercises
Flexible Schedule
24 x 7 Lifetime Support & Access
Certification and Job Assistance

Salesforce Analytics Cloud (Tableau CRM):

Salesforce Analytics Cloud (Tableau CRM) is an AI-powered analytics platform built directly into Salesforce, enabling businesses to turn data into actionable insights. It combines CRM data with external sources to deliver interactive dashboards, predictive analytics, and automated insights. Users can explore data visually, track KPIs, and make data-driven decisions without leaving Salesforce. With Einstein Discovery, it provides machine learning–based predictions and recommendations. Designed for sales, service, marketing, and operations teams, Tableau CRM supports secure, scalable, mobile-friendly analytics. It helps organizations uncover trends, forecast outcomes, and optimize performance by integrating analysis seamlessly into everyday workflows and processes.

Prerequisites: Who can attend Salesforce Analytics Cloud (Tableau CRM) Training?

Salesforce Users – Admins, developers, or business users familiar with Salesforce basics.
Business Analysts & Data Analysts – Anyone working with reports, dashboards, or data insights.
CRM & BI Professionals – Those looking to enhance analytics and visualization skills.
Technical Background Preferred – Basic understanding of data models, reports, or SQL concepts is helpful.
Beginners Welcome – Anyone interested in learning Salesforce analytics; prior coding knowledge is not mandatory.

Responsibilities of Salesforce Analytics Cloud (Tableau CRM) Consultant:

Design and develop Tableau CRM dashboards aligned with business requirements.
Build dataflows, recipes, and datasets for modeling, cleansing, and transforming data.
Integrate Salesforce and external data sources to create unified analytics.
Implement security and user access models for datasets and dashboards.
Provide insights, recommendations, and ongoing support to stakeholders for data-driven decisions.

Course Benefits

Job opportunities:
Promotion opportunities (Salary Hike):
Increased productivity:
Improved decision-making
Gain in-demand skills

What is future of Salesforce Analytics Cloud (Tableau CRM) Consultant?

Rising Demand for AI-Driven Analytics – As organizations adopt predictive analytics, Tableau CRM skills become increasingly valuable.
Growth in Salesforce Ecosystem – Expansion of Salesforce products ensures continuous opportunities for analytics specialists.
Integration with Tableau & Einstein – Deeper integration creates advanced roles in data science, visualization, and automation.
High Value in Digital Transformation Projects – Consultants play a key role in driving data-led decision-making across industries.
Pathway to Senior Roles – Opportunities to grow into Solution Architect, Analytics Lead, or Data Strategy Consultant roles.

Salesforce Analytics Cloud (Tableau CRM) Certification FAQ's:

What is the Tableau CRM (Einstein Analytics) certification?
It validates your skills in building dashboards, datasets, dataflows, and analytics solutions in Salesforce.
Who should take this certification?
Salesforce admins, analysts, consultants, developers, and BI professionals.
Are there any prerequisites?
No mandatory prerequisites, but Salesforce basics and analytics experience are helpful.
Which exam should I take?
The most common is Tableau CRM and Einstein Discovery Consultant certification.
What topics are covered in the exam?
Data modeling, dataflows, recipes, dashboards, lenses, security, Einstein Discovery, and integrations.
How many questions and exam duration?
Typically 60 multiple-choice questions with 105 minutes.
What is the passing score?
Around 68% (may vary slightly).
Is the exam tough?
Moderate difficulty; hands-on practice with datasets, dashboards, and Salesforce org helps.
How can I prepare for the exam?
Use Trailhead modules, hands-on practice, exam guides, and sample questions.
Is the certification in demand?
Yes, high demand as companies adopt AI-driven and integrated analytics with Salesforce.

Salesforce Analytics Cloud (Tableau CRM) Certification:

What is the certification called?
Tableau CRM & Einstein Discovery Consultant Certification.
Who should take this certification?
Consultants, Salesforce admins, analysts, developers, and BI professionals.
Are there any prerequisites?
No mandatory prerequisites, but Salesforce admin knowledge is recommended.
What skills are tested?
Data modeling, dataset creation, dashboards, security, recipes, dataflows, and Einstein Discovery.
What is the exam format?
60 multiple-choice questions, 105 minutes.
Passing score?
Typically around 68%.
Is the exam proctored?
Yes, online or onsite options are available.
How much does the exam cost?
Standard Salesforce certification fee (usually $200 + taxes).
Is hands-on experience required?
Highly recommended—especially with dataflows, recipes, lenses, and dashboards.
Does certification expire?
No traditional expiries, but maintenance modules must be completed each release.

The fee for Salesforce Analytics Cloud (Tableau CRM) training can vary depending on several factors such as the location, duration of the course, training format, and level of expertise. SAP offers various training options for Salesforce Analytics Cloud (Tableau CRM), including instructor-led courses, e-learning courses, and virtual live classrooms.

For More details you can Register

Salesforce Analytics Cloud (Tableau CRM) Curriculum:

Introduction & Setup

What is CRM Analytics / Tableau CRM / Einstein Analytics; its history and purpose (why use it instead of standard Salesforce Reports & Dashboards).
Fundamental terminology: datasets, lenses, apps, dashboards, dataflows, recipes, permissions, etc.
How to enable CRM Analytics in a Salesforce org — org creation/setup, platform setup, license types and permission sets.

2. Data Layer: Preparing & Importing Data

Data ingestion: importing data from various sources — internal Salesforce objects, external data (CSV, external systems) or remote data.
Data preparation and transformation: creating dataflows / recipes (e.g. transformations like digest, augment, computeExpression, flatten, filter, etc.), scheduling, incremental loads.
Dataset building: understanding dataset properties, metadata (XMD), time-intelligence, and preparing clean datasets.

3. Explore & Visualize: Lenses, Dashboards & Apps

Lenses: how to explore datasets, slice & dice data, apply filters/groups/measures; quick explorations and analysis.
Dashboards and Apps: designing dashboards — charts, KPI tiles, filters, layout; building interactive dashboards and content-rich apps for business use cases (Sales, Service, RevOps etc.).
Dashboard features: advanced editor, bindings & selectors, custom actions (e.g. open record, run flows), static/step elements, layout & UI customization.
Sharing & embedding: share dashboards and apps with users and embed dashboards into Salesforce (Lightning pages, Communities), manage access.

4. Security & Governance

Permissions management: assign permission sets, manage licenses, user types.
Data-level security: row-level security (ownership-based), role-based security, security predicates / filters, mass sharing and access controls to control who sees what.
Governance best practices: naming conventions, folder structure, dataset / dashboard governance, scalability and maintainability.

5. Advanced Analytics & Customization

Advanced query: using SAQL (Salesforce Analytics Query Language) for custom aggregations, top-N, windowing functions, advanced calculations beyond UI capabilities.
Dashboard JSON, XMD metadata, bindings & dynamic behavior: customizing dashboards programmatically (JSON), using extended metadata definitions for datasets, and dynamic interactions.
Performance tuning & debugging: using tools (e.g. Dashboard Inspector) to optimize performance, manage caching, query performance, efficient data model & design for large orgs/data volumes.
Mobile & embedded analytics: building and using dashboards for mobile clients, embedding dashboards into Salesforce Lightning, Communities, or external sites — providing analytics across devices.

6. Predictive & AI-powered Analytics (Optionally with Einstein Discovery)

Understanding what Einstein Discovery is — a no-/low-code predictive analytics solution integrated with CRM Analytics that helps build models, generate predictions, and surface insights.
Steps to build predictive models: choosing model type (regression, classification), evaluating model metrics, interpreting results, and deploying predictions into Salesforce / CRM Analytics workflows or dashboards.
Using predictions and insights: integrating predicted outcomes into dashboards, using them in record pages, automations, decision-making, or action flows inside Salesforce.
Using AI-assisted analytics: augmenting standard data exploration with AI — insights, forecasts, “what-if” analyses based on historic data.

Note: The specific curriculum for Salesforce Analytics Cloud (Tableau CRM) training may vary depending on the needs of the trainees/Corporate Client and the objectives of the training program.

Salesforce Analytics Cloud (Tableau CRM) Projects:

1. Sales Performance Dashboard

Objective: Help Sales Leaders monitor pipeline, performance, forecasting.
Key Features:

Opportunity pipeline (by stage, region, owner)
Quota vs Achievement tracking
Win/Loss analysis insights
Forecasting trends using analytics
Dynamic filters for region, product, sales reps
Skills Used: Dataset creation, dataflow, bindings, SAQL (optional), security predicates

2. Customer Service Analytics (Support Dashboard)

Objective: Improve Service KPIs using CRM Analytics.
Key Features:

Case volume by priority, channel, product
Agent performance dashboard
SLA breach predictions
Customer satisfaction score trends
Interactive actions (open case record, escalate)
Skills Used: Recipes, bindings, custom actions, mobile layout

3. Revenue Analytics for Leadership

Objective: Provide executives a complete view of revenue drivers.
Key Features:

YoY revenue comparison
Top-performing accounts/products
Bookings vs Billings dashboard
Key growth indicators (ARR/MRR for subscription businesses)
Drill-down into account-level performance
Skills Used: Dataset joins (augment), SAQL windowing, dashboard JSON

4. Marketing Campaign Effectiveness Dashboard

Objective: Analyze campaign ROI & lead performance.
Key Features:

Leads generated per campaign
Conversion funnel (Lead → MQL → SQL → Opportunity → Win)
Cost per lead / Cost per opportunity metrics
Multi-touch attribution modeling (advanced)
Skills Used: Data blending, transformations, calculated fields

5. Sales Manager Performance Cockpit

Objective: Provide Sales Managers team-level insights.
Key Features:

Rep-wise pipeline health
Coaching opportunities (low activity alerts)
Territory insights
Account engagement score
Skills Used: Row-level security, dashboards for role hierarchy

6. Einstein Discovery Predictive Project

Objective: Predict Customer Churn or Deal Win Probability.
Key Features:

Build predictive model
Interpret predictions (top factors → score impact)
Add prediction scores to dashboards
Use “What-If” analysis
Skills Used: Einstein Discovery, model deployment, predictions in dashboards

7. Finance Analytics (Cashflow + Expense Analysis)

Objective: Provide Finance team insights from Salesforce + external sources.
Key Features:

Expenses vs Revenue comparison
Aging analysis
Cashflow trend visualization
Vendor performance insights
Skills Used: CSV ingestion, external source integration, dashboard inspector

8. Field Service Analytics

Objective: Improve field operations and technician performance.
Key Features:

Technician productivity
Job completion time
Distance traveled (geo analytics)
First-time fix rate
Skills Used: Geospatial charts, filters, conditional formatting

9. Partner Performance Analytics (PRM)

Objective: Monitor partner sales & performance in a partner portal.
Key Features:

Partner pipeline tracking
Deal registration analytics
Partner comparison charts
Access-controlled dashboards for partner users
Skills Used: Community embedding, row-level security, partner API data

10. Subscription Business Analytics

Objective: Track subscription metrics (SaaS businesses).
Key Features:

ARR/MRR dashboard
Churn & expansion analytics
Cohort analysis for customer retention
Forecast future revenue using trends
Skills Used: Advanced SAQL, computed fields, date transformations

Salesforce Analytics Cloud (Tableau CRM) Interview Questions and Answers:

1. What is Salesforce Analytics Cloud (Tableau CRM)?

It is Salesforce’s advanced analytics platform used to build datasets, lenses, dashboards, and predictive insights using connected Salesforce + external data.

2. What is a Dataset?

A dataset is a collection of structured, denormalized data stored in CRM Analytics and used for analysis, visualizations, and dashboards.

3. What is a Lens in Tableau CRM?

A lens is an exploration view of a dataset that allows users to slice, group, filter, and analyze data interactively.

4. What is a Dataflow?

A Dataflow is a sequence of data transformations (digest, augment, compute, filter, etc.) used to extract, transform, and load data into datasets.

5. What is a Recipe?

Recipes are a UI-based tool for transforming, cleaning, joining, and preparing data without complex coding; simpler and more powerful than dataflows.

6. Difference between Dataflows and Recipes?

Dataflows: Node-based, JSON-based, complex transformations, old method.
Recipes: Drag-and-drop UI, more features, recommended for most use cases.

7. What is SAQL?

SAQL (Salesforce Analytics Query Language) is used for advanced queries inside dashboards or lenses that UI cannot support.

8. What are Security Predicates?

Security predicates are row-level security filters applied at the dataset level to restrict access based on user criteria.

9. What is an App in Tableau CRM?

An App is a container that holds datasets, lenses, and dashboards, allowing easy organization and access management.

10. What is XMD?

Extended Metadata (XMD) is used to customize dataset appearance—formatting, colors, number styles, and field-level enhancements.

11. What is Binding in Dashboards?

Bindings connect steps dynamically so filters, values, or selections in one widget change another widget’s behavior.

12. What is a Step in a Dashboard?

A step is a query within a dashboard — pulling data from a dataset to create a chart, table, or metric.

13. What are Compare Tables?

Compare Tables allow users to build pivot-style analytics, add calculations, change metrics, and compare values quickly.

14. What is a Connected Dataset?

A connected dataset updates automatically through dataflows or recipes and is linked to Salesforce objects or external systems.

15. What is the use of the Dashboard JSON?

Dashboard JSON is used for advanced control over layouts, widgets, bindings, interactions, and customization beyond the GUI.

16. What are Data Manager Features?

Data Manager provides a centralized interface for recipes, dataflows, data monitoring, connections, and scheduling.

17. What is a Connection in Analytics Cloud?

A connection links external data sources (CSV, database, Snowflake, AWS, Heroku, etc.) to CRM Analytics for ingestion.

18. How do you schedule a Dataflow/Recipe?

Use Data Manager → Monitor → Schedule to run dataflows/recipes at specific intervals.

19. What is Event Monitoring Analytics?

It visualizes user activity (logins, API calls, Lightning performance) with prebuilt dashboards for org security & performance.

20. What is Einstein Discovery?

Einstein Discovery is the predictive analytics engine used to build ML models (churn, win probability, forecasting).

21. How do you embed a Dashboard in Salesforce?

Use the Analytics Lightning Component on a Lightning page or community page.

22. What is a Registered Dataset?

A dataset created using REST APIs or external systems instead of dataflows or recipes.

23. What is a Salesforce Local Connector?

It allows importing Salesforce data directly without building extract nodes manually in dataflows.

24. How do you handle row-level security?

By applying security predicates that restrict dataset rows based on user attributes (e.g., OwnerId = $User.Id).

25. What are the types of Charts available?

Bar, line, donut, pie, scatter, compare table, values table, timeline charts, pivot tables, KPI widgets, maps, etc.

26. What is the Dashboard Inspector?

A tool to debug dashboards — shows query performance, step queries, load time, and SAQL.

27. What is a Data Sync?

It automatically extracts Salesforce object data using replication — the foundational step before using the data in dataflows.

28. What is a Co-Group in SAQL?

It joins two SAQL streams to perform combined aggregations like joins in SQL.

29. What is the difference between Analytics Studio and Data Manager?

Analytics Studio → Build dashboards, lenses, apps
Data Manager → Data ingestion, dataflows, recipes, connections, scheduling

30. What are best practices in CRM Analytics?

Minimize SAQL where UI can achieve results
Use row-level security for sensitive data
Optimize dataflows for performance
Maintain clean naming conventions
Use recipes for most transformations
Test dashboards for mobile