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In today’s data-driven world, understanding and leveraging customer data is essential for any business. CRM Analytics (CRMA), or Tableau CRM, is Salesforce's advanced analytics platform, enabling businesses to gain insights from their CRM data. It is designed for data analysts to dig deeper into customer data, generate actionable insights, and help drive business decisions. 

Key Takeaways

  • What CRMA is and how you can use it effectively.
  • Presents hands-on examples for data analysts.

What is CRMA (CRM Analytics)?

CRM Analytics (CRMA) is a powerful business intelligence (BI) tool built natively into the Salesforce ecosystem. It allows users to explore, visualize, and act on their Salesforce data directly within their CRM environment. CRMA is more than just dashboards—it's a full-fledged analytics platform that enables data analysts to combine external datasets, use AI for predictions, and even automate actions based on insights.

For data analysts, CRMA provides a range of advanced capabilities, from building interactive dashboards to conducting predictive analytics with Salesforce’s integrated AI, Einstein Analytics.

Key Features of CRMA for Data Analysts

1. Native Integration with Salesforce: CRMA is deeply integrated with Salesforce, making it easy to pull in customer data from Salesforce CRM without complex ETL processes. This integration allows data analysts to work with real-time data and ensure seamless analytics across sales, marketing, and service departments.

2. Data Exploration and Visualization: CRMA Salesforce provides an intuitive interface for data exploration, allowing analysts to build powerful, interactive dashboards and charts. It supports drag-and-drop functionalities, which makes it easy to visualize trends and relationships between data points.

3. AI-Powered Analytics with Einstein: CRMA has built-in AI capabilities through Salesforce Einstein. Data analysts can leverage these tools for predictive modeling, identifying trends, and providing recommendations based on customer behavior data.

4. Data Enrichment and Transformation: With Salesforce CRMA, analysts can merge Salesforce data with external datasets to build enriched customer profiles. This enables a more holistic view of the customer journey, providing greater context for decision-making.

5. Automated Workflows and Insights: CRMA enables users to create automated workflows based on analytics. For example, if a sales opportunity reaches a certain probability of success, CRMA can automatically notify sales reps or trigger a marketing campaign.

Getting Started with CRM Analytics

Step 1: Data Ingestion and Setup

The first step in working with CRMA is getting your data into the platform. Since CRMA is natively integrated with Salesforce, your CRM data is already available. However, to make your analysis more robust, you can also upload external data sources (e.g., CSV files or data from external databases) directly into CRMA.

Example: If you want to analyze how marketing campaigns affect sales, you could upload a CSV file containing marketing campaign data and merge it with Salesforce’s opportunity data to draw insights.

Step 2: Building Your First Dataset

After ingesting your data, you can start creating datasets, which are essentially the foundation of your analysis. A dataset combines multiple data sources and allows you to perform transformations or calculations on the data.

Example: Create a dataset that merges customer purchase history from Salesforce with marketing campaign data from an external source. This dataset can then be used to analyze how marketing efforts translate into actual revenue.

Step 3: Data Exploration with Lenses

In CRMA, Lenses allow you to explore your datasets interactively. You can filter, group, and drill down into the data to identify patterns or insights.

Hands-on Example: Imagine you want to understand which customer segments respond best to email marketing. Using a lens, you can group customers by demographic data, apply filters for those who received email campaigns, and visualize the relationship between customer engagement and campaign effectiveness.

Step 4: Creating Dashboards

Dashboards are where your data truly comes to life. CRMA allows you to create highly interactive, real-time dashboards that provide insights at a glance. You can easily drag and drop components like charts, tables, and KPIs onto the dashboard canvas.

Example: Build a sales performance dashboard that displays key metrics such as total revenue, sales pipeline status, win rates, and predicted sales trends based on historical data. This dashboard can be shared with sales leadership to help them make informed decisions.

Step 5: Leveraging Einstein for Predictive Analytics

Salesforce Einstein is an AI engine embedded within CRMA that can automatically analyze your data and generate predictions. As a data analyst, you can use Einstein Discovery to build predictive models without needing deep expertise in machine learning.

Hands-on Example: Use Einstein to predict customer churn by analyzing past behaviors such as purchase frequency, customer support interactions, and product usage. Einstein will not only give you predictions but also explain the factors driving those predictions, making it easier to take action.

Step 6: Automating Actions Based on Insights

CRMA isn’t just about visualization; it’s about taking action. You can create Actionable Insights that trigger workflows within Salesforce based on specific conditions.

Example: Set up an automation that triggers when a high-value customer is identified as a potential churn risk. This can automatically notify the account manager, create a task in Salesforce, and send a personalized retention email to the customer—all based on CRMA’s insights.

Hands-On Use Case: Analyzing Sales Team Performance

Let’s go through a simple example of analyzing sales team performance using CRMA. Your objective is to create a dashboard that tracks key performance indicators (KPIs) for each sales representative and predicts future sales performance.

  • Step 1: Ingest Salesforce CRM data: Pull in data such as leads, opportunities, and closed deals from Salesforce CRM.
  • Step 2: Create a dataset: Merge the sales data with external data, such as territory assignments and marketing spend.
  • Step 3: Build lenses: Use lenses to explore the data and identify trends, such as which regions are performing well or which sales reps have the highest win rates.
  • Step 4: Develop a dashboard: Create a dashboard that displays KPIs like total deals closed, average deal size, and pipeline status by sales rep.
  • Step 5: Use Einstein for predictions: Apply Einstein to predict which reps are likely to close the most deals next quarter, based on historical performance.
  • Step 6: Automate workflows: Set up an alert that notifies sales managers if a rep's predicted performance falls below a certain threshold.

Best Practices for CRM Analytics

  • Leverage AI features: Salesforce Einstein is a powerful ally for data analysts. Use it for predictive modeling and trend identification to enhance your reports.
  • Incorporate External Data: While Salesforce CRM data is powerful, integrating external datasets such as marketing spend or customer satisfaction scores can provide a more comprehensive analysis.
  • Create Dynamic Dashboards: Interactive dashboards allow stakeholders to drill down into the data and make real-time decisions. Ensure that your dashboards are not only informative but also actionable.  
  • Automate Actions: Set up workflows that automate processes based on insights from your dashboards. This will help streamline operations and improve efficiency.

Conclusion

In this blog, we have walked you through what is CRMA Salesforce and what does a CRMA do. It is an incredibly powerful tool for data analysts working in Salesforce environments. From building dynamic dashboards to using AI for predictive insights, CRMA enables you to uncover deep insights from your data and turn them into actions. By following the hands-on steps and examples provided in this guide, you can harness the full power of CRMA to deliver actionable insights for your business. To get started with automating your Salesforce data, schedule a time to speak with one of our Solution Engineers here. 

Frequently Asked Questions (FAQs)

1. What is the difference between CRM Analytics and Tableau?  

CRM Analytics (formerly Tableau CRM) is built natively into Salesforce and is optimized for analyzing CRM data. Tableau, on the other hand, is a more general-purpose analytics tool that can connect to a wide range of data sources, including Salesforce.

2. Can I use external data sources in CRM Analytics?  

Yes, CRM Analytics allows you to upload and integrate external datasets such as CSV files, third-party databases, or other systems, which can be combined with Salesforce CRM data for a more comprehensive analysis.

3. Do I need to be a coding expert to use CRM Analytics?  

No, CRM Analytics is designed with an intuitive interface that allows data analysts to create datasets, lenses, and dashboards without requiring deep coding skills. However, some knowledge of data modeling and querying can be beneficial for more advanced use cases.

4. How does Einstein Analytics work in CRM Analytics?  

Einstein Analytics is integrated within CRM Analytics, providing AI-powered tools for predictive modeling and automated insights. You can use Einstein to predict outcomes like customer churn, future sales, or the likelihood of a lead converting into a customer.