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Jul 12, 2024
Tableau and Salesforce: a powerful combination– Troubleshooting, Tricks, and Best Practices

In the previous chapters, we delved into the incredible power the combination of Salesforce and Tableau offers to organizations. By pulling together these world-class solutions, businesses can not only access a wide array of features but also unlock unique synergies that boost their operational efficiency and decision-making prowess.

Let us take a moment to reflect on the insights we have gathered so far.

Tableau’s advanced visualization tools make the wealth of data stored in Salesforce readily understandable and actionable. By crafting intuitive dashboards, teams across your organization can leverage Salesforce’s rich customer data to gain insights previously hidden in the complexity of raw data. Merging Salesforce data with information from other business sources also allows for a more holistic view of your business and its customers, enabling you to make decisions with confidence and precision.

We also discussed how bringing Tableau dashboards into the Salesforce CRM environment enhances user efficiency. Having visualized data within the same workspace where business activities take place saves precious time and prevents potential information loss that can occur during context switching. The power of Tableau’s Dashboard Starters and the seamless integration within Salesforce CRM workflows translate into convenience and deeper understanding, equipping your teams with the tools to make smarter, more informed decisions.

We delved into the fascinating world of Einstein AI and how its advanced insights can propel your Tableau dashboards to another level. Einstein AI’s easy-to-set-up models connect your analytics to actionable next steps. Coupled with Data Cloud for Tableau, this enables your organization to tap into unified data sources, providing a wide field of view of your customer’s journey.

And finally, we explored the full integration of Tableau CRM (previously referred to as CRM Analytics) within the Salesforce platform. This native solution amalgamates Salesforce’s comprehensive sales, service, and marketing data with Tableau’s visualization prowess, allowing businesses to detect trends, anticipate outcomes, identify anomalies, and enhance customer experiences.

In short, the marriage of Salesforce and Tableau is more than just the sum of its parts. It is a potent blend that unlocks the full potential of customer data, empowers users to work more efficiently, and uncovers advanced insights. By leveraging this powerful combination, organizations can make data-driven decisions with greater ease and accuracy, thereby boosting their overall performance. And this is the power of harnessing two world-class solutions to enhance your business’s strategic decision-making capabilities.

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Jun 13, 2024
Introduction– Troubleshooting, Tricks, and Best Practices

This chapter will summarize why Tableau and Salesforce are such a powerful combination, with an emphasis on their capabilities for data analysis, visualization, and decision-making. Additionally, the chapter will provide tips on how to get started using Tableau and Salesforce in practice, with guidance on best practices and useful resources. It will also contain guidance on troubleshooting common issues. The chapter will also explain how to continue the learning journey, including recommended further reading. Lastly, the chapter will help you start using the knowledge gained to improve your data analysis and decision making skills.

Structure

The chapter covers the following topics:

  • Tableau and Salesforce: a powerful combination
  • Trouble shooting guidance
  • Troubleshooting guidance
  • Continuing the learning journey

Objectives

This chapter is designed to provide learners with a thorough understanding of the combined use of Tableau and Salesforce for data analysis and visualization, highlighting their unique strengths and synergies. It delves into the reasons why integrating Tableau with Salesforce can significantly enhance business decision-making capabilities.

Learners will also be equipped with essential tips and best practices for effectively starting with Tableau and Salesforce in practical, real-world scenarios. Furthermore, the chapter includes strategies for troubleshooting common issues that may arise in the Tableau and Salesforce environments.

It also guides learners to discover a range of useful resources, tools, and platforms that can support their journey in mastering these technologies. The importance of continuous learning is emphasized, with strategies provided for staying current with the latest trends and developments in Tableau and Salesforce.

Additionally, learners will find recommendations for further reading that can deepen their understanding and proficiency in using these tools. Finally, the chapter aims to encourage and empower learners to confidently apply the knowledge and skills acquired from this book in their data analysis and decision-making tasks.

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Apr 5, 2024
Combining Salesforce and Tableau for Advanced Analytics– Exploring Einstein AI and Advanced Analytics-1

To give an example of how you might combine Tableau and Salesforce to achieve advanced analytical use cases, we will bring the Einstein Discovery prediction model that you created in Chapter 8, Blending Tableau with Traditional CRM Analytics into Tableau Desktop. If you have not already completed the setup of this Einstein Discovery model, now is the time to do so.
We will employ the Tableau Einstein Discovery connector to bring the predictions into our Tableau environment, and thereby we will learn one of the most important ways of combining CRM Analytics with Tableau Desktop to get more out of both.
To start with, let us get the connection set up. Please follow the steps below to set up the connection between Tableau Desktop and Einstein Discovery:

  1. Go to your model from Chapter 8 in Analytics Studio. It can be reached via Browse |Models, as shown in Figure 9.1:

Figure 9.1: Deploy model screen in Analytics Studio

  1. Now open your model and deploy it by clicking Deploy Model; this will make it usable from Tableau Desktop, among other places. You can see this in the Figure 9.2:

Figure 9.2: Model deployment options

  1. Leave the defaults as is and ignore any model warnings; they are not relevant to the example. See the warning in the Figure 9.3:

Figure 9.3: Ignore warnings

  1. On the following screen, select Deploy without connecting to a Salesforce object, as we are not writing back to the CRM but using it from Tableau. You can see this in Figure 9.4:

Figure 9.4: Deployment settings

  1. We will not be segmenting our data, although it is worth noting this capability for future investigation, so select Don’t segment, as shown in Figure 9.5:

Figure 9.5: Segmentation options

  1. Under Actionable variables, indicated in Figure 9.6, select Lead Source and Industry” as these are within our gift to influence, although not fully control.

Figure 9.6: Actionable variables

  1. At this point, we will not customize our predictions. Therefore, select Don’t customize, as shown below.

Figure 9.7: Customization options

  1. Click Deploy to deploy the model, as shown in Figure 9.8:

Figure 9.8: Deploy model

  1. When you are redirected to a screen as shown Figure 9.9, you are ready to proceed to the next stage.

Figure 9.9: Model deployed

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Feb 27, 2024
Einstein prediction builder – Exploring Einstein AI and Advanced Analytics

Einstein Prediction Builder is a powerful tool within the Salesforce Einstein platform that enables users to create custom predictions for specific business use cases. It simplifies the process of predictive analytics by automating most technical aspects, such as algorithms, parameter tuning, and infrastructure management. In the context of advanced analytics, Einstein Prediction Builder’s capabilities streamline the process of building, analyzing, and deploying machine learning models, resulting in deeper insights and better decision-making.

Advanced analytics goes beyond traditional business intelligence to discover deeper insights, make predictions, or generate recommendations using techniques such as data mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, and more. Einstein Prediction Builder’s capabilities align with advanced analytics as it involves the use of machine learning to make predictions based on historical data and analyze the performance of these predictions to drive better business outcomes.

The Einstein Prediction Builder simplifies the entire prediction workflow by breaking it down into six steps: defining the use case, identifying the data that supports the use case, creating the prediction, reviewing and iterating the prediction, monitoring the prediction, and deploying and using the prediction.

The following steps can be used when implementing predictions with Einstein Prediction Builder:

  1. Define your use case.

The process begins by identifying a business outcome that needs improvement, such as increasing lead conversion rates, improving customer satisfaction scores, or reducing churn.

  1. Identify the data that supports your use case.

With a clear use case, users can frame their prediction using the “Avocado Framework”, which helps segment the data, identify examples of outcomes, and distinguish between historical data for training and new data for making predictions.

  1. Create your prediction.

Einstein Prediction Builder’s wizard enables users to set up predictions quickly by selecting the data object, specifying the question to be answered (binary or numeric), configuring positive and negative examples, and selecting the fields to be included or excluded from the analysis.

  1. Review, iterate and enable your prediction.

Once the prediction is created, a scorecard is generated that provides information on the quality of the prediction. Users can review and revise their prediction based on the scorecard metrics and insights, remove any potential biases, and enable the prediction.

  1. Monitor your prediction.

After enabling the prediction, it is recommended to let it run for a period of time behind the scenes so that its performance can be assessed on new data. Users can verify the model’s accuracy using Salesforce reports or by utilizing the Einstein Prediction Builder Model Accuracy Template.

  1. Deploy and use your prediction.

Finally, the prediction can be integrated into the business processes using list views, lightning components, Process Builder, or Einstein Next Best Action strategies. Monitoring KPIs, performing a phased rollout, and periodically reviewing the model’s assumptions and performance are essential for success.
In summary, Einstein Prediction Builder simplifies the complex process of creating machine learning models and applying advanced analytics concepts to deliver actionable insights. By streamlining the prediction workflow, users can quickly generate predictions, analyze their performance, and efficiently integrate them into business processes. This tool makes advanced analytics more accessible and easier to use for driving better business decisions.

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Oct 7, 2023
Advanced analytics – Exploring Einstein AI and Advanced Analytics

Advanced Analytics involves the independent or partially independent analysis of data or content using complex methods and tools. These often surpass the capabilities of standard Business Intelligence (BI), allowing for the uncovering of more profound insights, the making of predictions, or the provision of suggestions. Techniques employed in advanced analytics encompass data or text mining, machine learning, pattern identification, forecasting, visual representation, semantic and sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, and neural networks.

Advanced analytics applications in businesses are broad and varied, covering areas such as customer acquisition and retention, marketing efforts enhancement, risk management, and product development and innovation.

With the aid of advanced analytics, businesses can identify customer preferences, target their marketing efforts, foresee potential risks, and innovate in product development. Let us delve deeper into these applications:

  • Customer acquisition and retention: Advanced analytics can process customer interaction data, market research, and feedback to identify customer preference trends. Salesforce’s intelligent Customer 360 platform can be used here to provide a unified customer view, allowing businesses to meet and exceed customer expectations effectively.
  • Enhancing marketing efforts: Machine learning, business intelligence, and predictive modeling can enable companies to develop sophisticated marketing campaigns. Salesforce Marketing Cloud can assist in delivering personalized experiences to customers, tracking real-time behavior changes, and making data-driven decisions.
  • Improving risk management: Advanced analytics tools can model scenarios and forecast risks, guiding enterprises toward strategic risk management plans. Salesforce Shield provides a set of integrated services for managing business risks, from archiving sensitive data to implementing fine-grained user permissions.
  • Product development and innovation: By analyzing customer needs, advanced analytics tools can identify areas of improvement and potential new product niches. Salesforce’s Product Development Cloud can manage the entire lifecycle of product development, from ideation to launch.

Real-world applications of advanced analytics

Various global brands have harnessed the power of advanced analytics to drive their success. For instance, Amazon uses advanced analytics to provide personalized customer experiences. Starbucks optimizes its menu and product offerings through data analytics. American Express uses predictive modeling to anticipate potential customer churn and optimize marketing efforts.

Tableau, with its powerful data visualization capabilities, can be used to replicate these successes by presenting complex data in an understandable and actionable format. Whether it is predicting customer behavior like Amazon, optimizing product offerings like Starbucks, or forecasting customer churn like American Express, Tableau is a versatile tool in the hands of data scientists and analysts.

Advanced analytics is crucial for fostering innovation and improving business operations. By utilizing predictive analytics, businesses can make better-informed decisions and model outcomes beforehand. This capability allows businesses to craft strategic plans for all areas of operations, from customer service to digital marketing.

With technology’s evolution, the potential applications and capabilities of data science tools, like Salesforce and Tableau, are continually expanding. This advancement has reflected in the growing number of organizations planning to integrate advanced analytics into their decision-making models.

The future of advanced analytics will likely see more complex algorithms, more data science, and more process automation. Both Salesforce and Tableau, with their robust and continually improving features, are well-positioned to support this evolution.

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Sep 1, 2023
Objectives – Exploring Einstein AI and Advanced Analytics

Introduction

This chapter will focus on the concept of advanced analytics and its applications in Tableau and Salesforce. The chapter will begin by defining advanced analytics and its capabilities in providing deeper insights and predictions from data. The chapter will then cover the use of advanced analytics in Tableau, highlighting relevant features. Additionally, the chapter will delve into advanced analytics in Salesforce, including its Einstein platform and its capabilities for machine learning and AI-powered insights. Lastly, the chapter will guide how to create a combined analytical use case across Tableau and Salesforce, leveraging the strengths of both platforms for enhanced data analysis and decision-making.

Structure

The chapter covers the following topics:

  • Advanced analytics
  • Using advanced analytics in Tableau
  • Using advanced analytics in Salesforce
  • Combining Tableau and Salesforce for advanced Analytics

Objectives

This chapter provides learners with a comprehensive understanding of advanced analytics and its significant role in interpreting data and aiding decision-making. It explores the potential of advanced analytics to yield deeper insights and more accurate predictions. Learners will discover how to harness the capabilities of advanced analytics in Tableau, leading to robust data visualization and exploration. The chapter also imparts knowledge about Salesforce’s Einstein platform, which incorporates machine learning and artificial intelligence to facilitate advanced analytics. Additionally, it explains how Salesforce utilizes advanced analytics to enhance customer relationship management and other business functions.

A key focus of the chapter is teaching learners how to create a combined analytical use case that leverages the strengths of both Tableau and Salesforce for improved data analysis. This involves understanding the integration and synergies between these platforms, which is crucial for optimizing their use in a business context. Finally, learners are encouraged to apply the techniques and knowledge acquired to real-world problems. This application not only enhances their data analysis skills but also bolsters their strategic decision-making capabilities, equipping them with the tools to make informed, data-driven decisions in various business scenarios.

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Jul 5, 2023
Using the Tableau online output connection – Blending Tableau with Traditional CRM Analytics

The Tableau Online Output Connector is a powerful tool that enables you to seamlessly push your prepared data from CRM Analytics into Tableau Online for further analysis. By transforming, merging, and cleaning your data in CRM Analytics, you can easily create a .hyper file that can be analyzed using Tableau Online’s advanced analytics tools. This connector is designed to work with Data Prep recipes and requires a Creator license for the Tableau Online account. In this section, we will walk you through the process of enabling the Tableau Online Output Connector, configuring connection settings, and pushing data to Tableau Online.
In this example, we will write the opportunity_history dataset that we have been using all through this chapter to our Tableau Cloud environment, using the Tableau Online Output Connector.
To do so follow the instructions below:

  1. First, go to setup and search for analytics in the Quick Find box. Click Settings.
  2. Under settings, shown in the following screenshot, enable the Tableau Online output connection and save:

Figure 8.39: Analytics settings showing Tableau connector

  1. Go to Analytics Studio and click Data Manager, then from the page that appears, click Connections. This should look like the following screenshot:

Figure 8.40: Data manager showing connections

  1. Click New Connection, select Output under Connector Type and click Tableau Online Output Connector, as shown below:

Figure 8.41: New connection dialogue

  1. Now fill in the information as in the screenshot below. You can refer to this URL for the details of the parameters if needed: https://help.salesforce.com/s/articleView?id=sf.bi_integrate_connectors_output_tableau_hyper.htm&type=5

Figure 8.42: Tableau Online connector configuration

  1. Now you have established the connection. Time to test it. To do so, go back to Data Manager and click on Recipes, as shown in the following screenshot:

Figure 8.43: Data manager recipes tab

  1. On the Canvas that appears, click Add Input Data, shown in the following screenshot:

Figure 8.44: Add input data button

  1. Select the opportunity_history dataset, as shown in the following figure:

Figure 8.45: opportunity_history dataset selected as input

  1. Now add a node by clicking on “+”. Select Output, shown below:

Figure 8.46: Output node being added

  1. Fill out the form as per the screenshot below:

Figure 8.47: Output node configuration

  1. Save and Run the Recipe by clicking on the button and giving it a name, for instance as in the following screenshot:

Figure 8.48: Save and run recipe dialogue

  1. The job will now run, so wait until it has been completed. You can monitor this in Jobs Monitor, shown in Figure 8.49:

Figure 8.49: Jobs monitor showing recipe run
When the job is complete, you can find and explore the dataset in Tableau Cloud by clicking Explore, selecting default, and then clicking on Extract, which is the extract you have just created. This will look as in the following screenshot:

Figure 8.50: Explore dataset in Tableau dialogue
We have now covered the material for this chapter. Well done! However, we have even further heights to scale as we explore the world of advanced analytics in Chapter 9 – Exploring Einstein AI and Advanced Analytics.

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Jan 7, 2023
Building a lens – Blending Tableau with Traditional CRM Analytics

A lens is a visual representation of the data within a dataset, allowing you to explore data graphically and construct queries for a dashboard. To build a lens, follow the steps below:

  1. Click on the Analytics Studio tab to return to the CRM Analytics Home page.
  2. Click Browse and then select Datasets.
  3. Choose the opportunity_history dataset, which will open a new tab, shown below, with a lens for exploring the dataset.

Figure 8.10: Opportunity_history dataset details

  1. In the New Lens tab, click on Count of Rows located beneath Bar Length, as shown in the following screenshot:

Figure 8.11: Lens builder with Count of Rows highlighted

  1. Select Sum and then choose Amount from the dropdown menu, as shown in the next screenshot:

Figure 8.12: Sum Amount selected

  1. Under Bars, click the plus sign (+) and select Industry, as shown below:

Figure 8.13: Industry field added to lens

  1. Click the plus sign (+) under Bars again, and choose Opportunity Type, as shown below:

Figure 8.14: Opportunity Type added to lens

  1. Under Bar Length, click the arrow next to Sum of Amount and select Sort Descending, shown in Figure 8.15:

Figure 8.15: Sort descending selected

  1. Click the Charts icon to access different chart options, shown in the following screenshot:

Figure 8.16: Charts icon highlighted

  1. Select the Stacked Column chart icon, shown in the screenshot below, to create a Stacked Column chart, which will display the sum of the amount according to the industry.

Figure 8.17: Stacked column chart selected

  1. Save your lens by clicking the Save button.
  2. Enter My Test Lens, indicated in the following screenshot, as the title of your new lens, and then choose App | My Test App from the dropdown menu.

Figure 8.18: Save lens dialogue

  1. Click Save to complete the process.
    Your final product should look like the following:

Figure 8.19: Completed stacked column lens visualization
With your app, dataset, and lens now created, you can proceed to create a dashboard.

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Nov 8, 2022
Creating an analytics app – Blending Tableau with Traditional CRM Analytics

A CRM Analytics app is a comprehensive collection of analyses, data exploration paths, and powerful tools designed for in-depth, real-time data examination. CRM Analytics relies on apps to organize data projects, run presentations directly from dashboards, and manage asset sharing.
To get started with creating an app in your CRM Analytics-enabled Developer Edition org, follow these steps:

  1. Open your CRMA Developer Edition org.
  2. Access the App Launcher and search for Analytics Studio. Select it to open Analytics Studio in a new tab. Keep both tabs open, as you will need to work on the original tab later in the project. This is shown in the following screenshot:

Figure 8.1: App Launcher showing Analytics Studio

  1. In Analytics Studio, click the Create button and select App from the dropdown menu, as shown below:

Figure 8.2: Create menu dropdown showing App option

  1. Choose Create Blank App to start with a clean slate, as shown in the following screenshot:

Figure 8.3: Blank App creation dialogue

  1. Click Continue to proceed to the next step. Enter My Test App as the name of your new app, as shown below:

Figure 8.4: My Test App creation dialogue

  1. Click Create to finalize the app creation process.
    Congratulations! You have successfully created an app in CRM Analytics. We will now move on to importing a dataset.
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Oct 16, 2022
Creating Datasets, Lenses, and Dashboards in CRM Analytics – Blending Tableau with Traditional CRM Analytics

In CRM Analytics, the most basic concepts are app, dataset, lens, and dashboard. These are the basic building blocks to understand to be able to work in the system, although, of course, they are just the beginning.

Before we dive into the practical steps of creating a dashboard in CRM Analytics, let’s briefly familiarize ourselves with the fundamental concepts of app, dataset, lens, and dashboard. Understanding these components is crucial as they form the backbone of CRM Analytics.

  • App: In CRM Analytics, an app serves as a container for your analytical projects. It’s a workspace where you can organize your datasets, lenses, and dashboards. Think of it as a folder on your computer where you keep related files together for easy access and organization.
  • Dataset: A dataset is a collection of data that CRM Analytics can process. This data can come from various sources, including Salesforce records, external databases, or files like CSVs. Datasets are the raw materials from which you extract insights.
  • Lens: A lens is a tool for exploring a dataset. It allows you to visualize data in different formats (such as charts or tables), filter the data to focus on specific aspects, and perform basic analyses. You can think of a lens as a magnifying glass that helps you examine the details of your dataset.
  • Dashboard: A dashboard is a visual representation of your data analyses, often comprising multiple widgets (charts, tables, etc.) that display data from one or more datasets. Dashboards are designed to provide at-a-glance insights and support decision-making.

Now that we have a basic understanding of these concepts, let’s proceed to create a simple dashboard in CRM Analytics to give you a feel for what you might be able to achieve if you were to dig deeper into the topic.

We will do this in a few steps:

  1. Create an app to hold your unique analytical assets.
  2. Import a dataset into the system.
  3. Explore this dataset through a lens.
  4. Build a simple dashboard to visualize the data.

Doing this will also teach you some of the key differences between creating dashboards in Tableau and doing so in CRM Analytics and why you might prefer one over the other in particular cases.

Let us begin!

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