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1 - Basic Terms - Tableau CRM
Salesforce Releases
Overview
This video introduces Tableau CRM (formerly Einstein Analytics) as an intelligent analytics platform within the Salesforce ecosystem. It covers the product's evolution, its core purpose of bringing intelligent insights to end-users, and its distinction from standard Salesforce reports and dashboards. The video outlines the product's simplified architecture, emphasizing its ability to connect to various data sources and process them. It then details the three main layers: Data, Design, and Intelligence, explaining key components within each. Finally, it presents six crucial steps for successful analytics implementation: Intelligence, Collaboration, Actionability, Self-Service, Embedding, and Smart Design, along with resources for further learning.
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Chapters
- Tableau CRM (Einstein Analytics) aims to provide intelligent analytics to end-users, especially within CRM contexts.
- The training series will cover features from basic data integration and dashboard creation to advanced topics and Einstein Discovery.
- A reminder about 'Safe Harbor' is given for any mention of future product features.
- Instructions are provided for setting up a free Tableau CRM training environment (dev org) via a specific Salesforce trailhead link, emphasizing the use of a valid email and selecting the correct country for language settings.
- Crucial steps for verifying the org creation email include using incognito browsers and understanding potential link corruption or security wrapper issues.
Understanding how to set up a dedicated training environment is essential for hands-on learning and practicing the concepts presented in the video series without affecting live production data.
Using developer.salesforce.com/promotions/orgs/analytics-trainings-de to register for a training org and the importance of right-clicking and copying the verification URL to an incognito browser.
- Tableau CRM's goal since its 2015 launch (as Wave) is to deliver intelligent analytics and experiences to users.
- It complements transactional CRM data by providing historical trends and predictions.
- An 'intelligent experience' integrates predictions with explanations and actionable insights directly within the user's workflow, going beyond simple numbers.
- Tableau CRM is distinct from standard Salesforce reports and dashboards, which are designed for live, operational data and can be slower for historical analysis.
- It acts as a powerful analytics platform, enhancing the Salesforce ecosystem with business intelligence capabilities.
This chapter clarifies the unique value proposition of Tableau CRM by differentiating it from existing Salesforce features and highlighting its focus on actionable, AI-driven insights.
A Salesforce user seeing not just transactional data but also a prediction score (e.g., 77) with explanations ('why it's 77') and suggestions on how to improve it, all within their familiar interface.
- Tableau CRM resides within the Salesforce cloud and connects natively to Salesforce objects.
- It supports data ingestion from various sources including other Salesforce clouds, external clouds, CSV uploads, ETL tools, and custom API calls.
- Data is processed through a Data Prep layer, landing in datasets, which can then be combined or used to build dashboards, stories, or prediction models.
- Live connections (direct query) are available, allowing data to be accessed without necessarily copying it into Tableau CRM datasets.
- Data can also be pushed back to external systems like AWS or Snowflake, and live connections from Snowflake are supported.
Understanding the architecture helps learners grasp how Tableau CRM integrates with their existing data landscape and the flexibility it offers in data management.
Bringing data from Salesforce objects, uploading a CSV file, and using an ETL tool to push data into Tableau CRM, which then lands as datasets for analysis.
- The Data Layer includes Storage (for datasets) and Data Prep (ETL tools like Data Sync/Connect, Data Flows, and Recipes).
- Data Sync/Connect is for initial data import, while Data Flows (older) and Recipes (newer, UI-friendly) are for transformation and combination.
- Datasets are the core storage units for processed data, optimized for high-speed querying.
- The Design Layer includes Lenses (for quick data exploration), Dashboards (visualizations combining metrics), and Apps (folders to organize and control access to assets).
- The Intelligence Layer focuses on Einstein, enabling the creation of Stories (insights, explanations, models), deployment of predictions, and monitoring of model performance.
This breakdown provides a foundational understanding of the platform's structure and the purpose of its core components, enabling users to navigate and utilize the tool effectively.
Using a Lens to quickly explore fields in a dataset, then building a Dashboard that combines multiple charts, and organizing these assets within a Sales App.
- Analytics Studio is the primary interface for designers and users to access and manage Tableau CRM assets.
- The landing page displays assets, notifications, and subscriptions, with potential future organization via 'Collections'.
- Apps are central to organizing assets (dashboards, lenses, datasets, stories) and managing user access.
- Users can create or edit dashboards, explore datasets via lenses, and view/manage their data sets within the Analytics Studio.
- The Data Manager section within Analytics Studio provides access to jobs, data flows, recipes, and lists all data sets (including synced, live, and connected data).
Familiarity with the Analytics Studio interface and asset organization is crucial for efficiently finding, creating, and managing analytics content.
Navigating to Analytics Studio, accessing a 'Sales App' to view its dashboards and datasets, and then going to Data Manager to check the status of a data sync job.
- Stories in Tableau CRM provide AI-driven insights, identify top influencers, and generate predictive models from a single dataset.
- A 'model' is intrinsically linked to a 'story'; running predictions uses the story's underlying model.
- Deployed predictions allow end-users to see scores and explanations without seeing the underlying complexity.
- Model monitoring tracks performance, accuracy, and alerts for deployed predictions.
- Einstein Discovery Insights (potentially renamed) can bring AI-powered analysis directly to standard Salesforce operational reports.
This chapter highlights the advanced intelligence capabilities of Tableau CRM, enabling users to not only analyze data but also generate actionable predictions and insights.
Opening an Einstein Story that reveals 'City' and 'Month' as top influencers for daily quantity, and then deploying the model to generate prediction scores for sales opportunities.
- Intelligence: Leverage Einstein Discovery for deeper understanding and predictive capabilities.
- Collaboration: Utilize features like annotations, subscriptions, and mentions to foster teamwork.
- Actionability: Design dashboards that enable users to take direct actions from insights, linking back to Salesforce records.
- Self-Service: Allow users, where appropriate, to explore data further beyond pre-built dashboards using tools like lenses.
- Embedding: Integrate analytics seamlessly into business applications (like Salesforce) to keep users in their workflow.
- Smart Design: Focus on creating visually appealing, readable, and direct dashboards that clearly answer business needs.
These six principles provide a strategic framework for implementing Tableau CRM projects, focusing on user adoption and maximizing the value derived from the analytics platform.
Embedding a dashboard showing sales forecasts directly onto a Salesforce account page, allowing reps to see predictions and immediately create a follow-up task for a lead.
Key takeaways
- Tableau CRM is an intelligent analytics platform designed to bring AI-driven insights and predictions into the daily workflow of business users, enhancing CRM capabilities.
- The platform is structured into three core layers: Data (ingestion, preparation, storage), Design (exploration, visualization, organization), and Intelligence (AI-driven insights and predictions).
- Understanding the difference between standard Salesforce reports/dashboards and Tableau CRM's analytical capabilities is crucial for choosing the right tool for the job.
- Setting up a dedicated training environment is a vital first step for hands-on learning and experimentation with Tableau CRM features.
- Tableau CRM offers flexible data integration options, connecting to Salesforce, external sources, and supporting both data loading and live connections.
- Einstein Stories and predictions provide advanced AI capabilities, offering explanations for insights and enabling proactive decision-making.
- Successful adoption of Tableau CRM relies on a strategic approach encompassing intelligence, collaboration, actionability, self-service, embedding, and smart design principles.
Key terms
Tableau CRMEinstein AnalyticsAnalytics StudioData LayerDesign LayerIntelligence LayerDatasetLensDashboardAppStoryModelPredictionData SyncData FlowRecipeSafe HarborTrailhead
Test your understanding
- How does Tableau CRM's 'intelligent experience' differ from standard Salesforce reports and dashboards?
- What are the three main layers of Tableau CRM, and what is the primary function of each?
- Describe the purpose of a 'Lens' versus a 'Dashboard' within Tableau CRM.
- What is the role of 'Stories' in Tableau CRM, and how do they relate to 'Models' and 'Predictions'?
- Explain the importance of the 'Embedding' and 'Actionability' principles in ensuring user adoption of Tableau CRM.