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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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May 27, 2024
Combining Salesforce and Tableau for Advanced Analytics– Exploring Einstein AI and Advanced Analytics-2

We will test our Einstein Discovery model on the raw CSV that we used to create the model in the first place, follow the below mentioned steps:

  1. Start by creating a new Tableau workbook and connect to the opportunity_history.csv file you downloaded. You can see how this should look in Figure 9.10:

Figure 9.10: Connect Tableau to CSV

  1. Create a worksheet, like the one shown in Figure 9.11, with the four fields Is Won, Industry, Lead Source, and Opportunity Type. You can format this worksheet as you like, as long as the fields are present.

Figure 9.11: Create worksheet

  1. Now add a dashboard to the workbook and add the worksheet you have just created to it. You can see how this should look in Figure 9.12:

Figure 9.12: Add dashboard

  1. On the dashboard, add an Extension of type Einstein Discovery, as shown in Figure 9.13:

Figure 9.13: Add Einstein Discovery extension

  1. Click add to Dashboard, as shown in Figure 9.14:

Figure 9.14: Add extension to dashboard

  1. You will now have to authenticate against your Salesforce org and allow your Tableau Desktop installation to access it. You can see this screen in Figure 9.15:

Figure 9.15: Authenticate Salesforce access

  1. The extension will now need to be configured, so click Open Settings, shown in Figure 9.16:

Figure 9.16: Open extension settings

  1. If you get an error at this point, you probably need to add the CRM Analytics Admin permission set to the user you are logged in as.
  2. Select the Predicted Amount model and the sheet you have just created in the dialog, shown in the following figure:

Figure 9.17: Select model and worksheet

  1. Map the fields to their worksheet equivalents, following the example in Figure 9.18:

Figure 9.18: Map fields

  1. Leave all fields at their default values in the final screen, as shown in Figure 9.19:

Figure 9.19: Extension settings

  1. You can now see your Einstein Discovery model directly in the worksheet and use standard worksheet controls to navigate and filter it. This should look like the Figure 9.20:

Figure 9.20: View predictions in worksheet
Congratulations, that was a hard task. Now, take a little break before going onto the final chapter in this book that deals with troubleshooting common issues and where to go from here.

Conclusion

Congratulations on reaching the conclusion of this rich journey into advanced analytics with Tableau and Salesforce! By now, you should have a solid understanding of the concept of advanced analytics and how it can elevate our data exploration to provide deeper insights and predictions.

Throughout this chapter, we have unlocked the power of Tableau’s advanced analytics, admired the intelligence of Salesforce’s Einstein platform, and learned how they can be harnessed to make data-driven decisions. We have also explored the synergy of these two platforms and their combined potential in enhancing your data analysis capabilities.

Remember, the field of advanced analytics is ever evolving, with new techniques and technologies constantly emerging. What you’ve learned here serves as a robust foundation, but there’s always more to discover and master. As we continuously strive for better data comprehension and predictive prowess, these tools will prove to be invaluable assets. Keep practicing, experimenting, and pushing the boundaries.

We will now move on to the final chapter of our book.

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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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Dec 24, 2023
Tableau: Advanced analytics capabilities – Exploring Einstein AI and Advanced Analytics

Tableau and Salesforce provide powerful analytics capabilities that cater to both business users and data scientists. This comprehensive platform offers a wide range of features, including segmentation and cohort analysis, what-if and scenario analysis, sophisticated calculations, time-series and predictive analysis, and external services integration. By combining advanced analytics features with an intuitive interface, Tableau enables users to perform complex analyses and gain valuable insights from their data quickly and efficiently. Here are the applications:

  • Segmentation and cohort analysis Tableau enables quick, iterative analysis and comparison of segments to generate initial hypotheses and validate them. This section covers various capabilities in Tableau, such as:
  • Clustering: Tableau’s clustering feature uses unsupervised machine learning to segment data based on multiple variables.
  • Sets and set actions: Sets in Tableau allow defining collections of data objects either by manual selection or programmatic logic. Set Actions enable storing a selection of data points within a set, allowing for use cases like proportional brushing.
  • Groups: Groups in Tableau support creating ad-hoc categories and establishing hierarchies, which can help with basic data cleaning needs and structuring data intuitively for analysis tasks.
  • What-if and scenario analysis: Tableau enables users to experiment with inputs of their analysis and share scenarios while keeping data fresh using parameters and story points.
  • Parameters: Parameters in Tableau allow changing input values into a model or dashboard, driving calculations, altering filter thresholds, and selecting data.
  • Story points: Story points in Tableau enable constructing presentations that update with data changes and retain parameter values.
  • Sophisticated calculations: Tableau offers powerful capabilities to support complex logic using calculated fields. Two types of calculated fields that enable advanced analysis are:
    • Level of detail expressions: LOD Expressions in Tableau are an extension of the calculation language, enabling answering questions involving multiple levels of granularity in a single visualization.
    • Table calculations: Table calculations in Tableau enable relative computations applied to all values in a table and are often dependent on the table structure itself.
  • Time-series and predictive analysis: Tableau simplifies time-series analysis and offers predictive capabilities such as trending and forecasting.
  • Time-series analysis: Tableau’s flexible front end and powerful back end make time-series analysis a simple matter of asking the right questions.
  • Forecasting: Tableau’s forecasting functionality runs several different models in the background and selects the best one, automatically accounting for data issues such as seasonality.
  • External services integration: Tableau integrates with external services like Python, R, and MATLAB to expand functionality and leverage existing investments in other solutions.
  • Python, R, and MATLAB integrations: Tableau integrates directly with Python, R, and MATLAB, allowing users to call any function available in R or Python on data in Tableau and manipulate models created in these environments using Tableau.

In conclusion, Tableau’s advanced analytics capabilities make it a versatile and valuable tool for users at all levels of expertise. By offering a wide range of features, such as clustering, calculated fields, forecasting, and integration with Python, R, and MATLAB, Tableau empowers users to perform complex analyses and derive actionable insights from their data.

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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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Aug 17, 2023
Conclusion – Blending Tableau with Traditional CRM Analytics

In this chapter, we have journeyed through the crucial domain of CRM Analytics and its paramount role in understanding, managing, and improving customer relationships. You should now be able to grasp the full picture of CRM Analytics, its key features, and how it facilitates thorough analysis of customer data through datasets and lenses.

The concept of Einstein Discovery within Salesforce, and its application in the CRM Analytics platform, was another essential part of our journey. Now, you should be capable of creating and utilizing Einstein Discovery models to enhance your data interpretation and decision-making processes.

Finally, we discussed the use of the CRM Analytics Tableau Output Connector, a bridge that allows Salesforce data to flow seamlessly into Tableau. This key tool enables you to perform detailed, insightful analysis of your Salesforce data, further enriching your understanding and enabling you to derive practical benefits.

In sum, we have armed you with a comprehensive understanding of CRM Analytics within Salesforce, its integration with Tableau, and the immense value these tools bring to your organization. Now, you should feel confident to apply these skills in your own work, harnessing the power of CRM Analytics and Tableau to transform raw data into strategic actions, and thereby improve your business operations and customer relationships.

We will now move on to putting our hard work on setting up CRMA to use inside Tableau for advanced analytics use cases, while also covering the basics of what advanced analytics are really all about in the process.

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Mar 13, 2023
Making a dashboard – Blending Tableau with Traditional CRM Analytics

A dashboard is an engaging compilation of widgets that display the results of data queries. In this section, you will create a dashboard with an interactive chart and a list widget. You will incorporate the lens from the previous step and explore tools in the dashboard designer.
To incorporate a lens into a dashboard, you need to clip it. Clipping the lens allows CRM Analytics to add the query as a step in a new dashboard (or the most recently used open dashboard).

  1. In your newly created lens, click the Clip to Designer icon, marked in the following screenshot, to clip the query.

Figure 8.20: Clip to designer icon

  1. Under Display Label, enter Breakdown by Industry and click Clip to Designer.
  2. CRM Analytics will open the dashboard designer and add the query in the query panel. Breakdown by Industry will be displayed under the opportunity_history dataset.

Figure 8.21: Lens added to designer with query name showing

  1. Drag the Breakdown by Industry query onto the new dashboard grid.
  2. Resize the chart by dragging its corner to make it larger. The result should look like the following screenshot:

Figure 8.22: Query chart resized on dashboard
Widgets are the basic building blocks of a dashboard, providing various functions such as displaying key performance indicators, filtering dashboard results, visualizing data using interactive charts, or showing record-level details in tables. In this step, you will add a list widget to enable dashboard users to facet all the charts in the dashboard.

  1. Drag the List widget icon, shown in the screenshot, from the left side onto an empty space on the dashboard underneath the chart.

Figure 8.23: List widget being added to dashboard

  1. Click on the List widget and select Industry.
  2. Click Create in the window shown in the next screenshot:

Figure 8.24: Industry field selected for list

  1. Select the list widget by clicking on it.
  2. In the Properties panel on the right, click the Query tab.
  3. Ensure that both Apply global filters and Broadcast selections as facets are selected.
  4. Under Selection Type, choose Multiple Selection, shown in Figure 8.25:

Figure 8.25: List widget properties

  1. Save your dashboard by clicking the Save button.
  2. Enter My Test Dashboard as the title of your new dashboard, and select App | My Test App from the dropdown menu, as shown below:

Figure 8.26: Save dashboard dialogue

  1. Click Save to complete the process. You can preview your dashboard, which should look like this:

Figure 8.27: Completed test dashboard
You have now created a basic dashboard. We hope you will explore the potential of CRM Analytics much further on your own, but for now we will move on to Einstein Discovery, the machine learning engine that powers CRM Analytics.

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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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Feb 9, 2022
Alternative methods to connect Salesforce and Tableau Dashboards – Integration, Authentication, and Tableau Viz LWC

While the Tableau Viz LWC component is the most common way to link Salesforce and Tableau, there are alternatives that you may want to consider in certain cases. First among these is the humble hyperlink or, alternately, a Salesforce Web Tab, linking to the relevant Tableau destination.

The main reason you would consider using a hyperlink instead of an embedded dashboard is that it is easy to implement and provides a level of control to the user. With a hyperlink, the user can decide when and where to access the Tableau dashboard. They can open it in a separate tab, window, or device and customize the view settings to their liking. Moreover, hyperlinks are easy to create and manage, and they do not require any special integration or maintenance efforts from either the Salesforce or Tableau side.

However, there are many drawbacks to using hyperlinks as well. First, you have to break the context and go to a different interface. It is also more difficult to pass parameters that are specific to the user context, as these will need to be set up in the visualization. Also, the security setup can become more complex by going down this route.

Using a hyperlink to link Salesforce and Tableau can be a viable option in certain cases. Still, it is important to weigh the pros and cons and consider the specific needs and preferences of your users before making a decision.

A second alternate option is to use a Tableau Connected App. Tableau-connected apps are a great way to securely and seamlessly authenticate external applications that embed Tableau content with your Tableau Cloud site. There are two types of connected apps: direct trust and OAuth 2.0 trust.

  • Direct trust allows you to restrict access to content that can be embedded and where it can be embedded. With direct trust, users can access embedded content using SSO without having to integrate with an IdP. You can also programmatically authorize access to the Tableau REST API and manage the Tableau REST API capabilities that users or applications can perform.
  • OAuth 2.0 trust also allows you to control access to embedded content and enable users to access it through SSO via your IdP. You can provide access using the standard OAuth 2.0 protocol, programmatically authorize access to the Tableau REST API, and manage Tableau REST API capabilities that users or applications can perform.

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