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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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Mar 16, 2024
Other Einstein features– Exploring Einstein AI and Advanced Analytics

There are many other features that are part of the Salesforce Einstein offering that could fall under the rubric of advanced analytics. Here we will consider the following:

  • Einstein lead scoring: This advanced analytics product utilizes machine learning techniques to assign a lead score to each lead based on their likelihood of converting. The score ranges from 1-99 and helps prioritize the leads for sales teams. It also provides insights into the factors that contribute to a lead’s score. By analyzing historical data and identifying patterns, Einstein Lead Scoring makes data-driven predictions, enabling sales teams to focus on the most promising leads.
  • Einstein opportunity scoring: Like Einstein lead scoring, this advanced analytics product uses machine learning techniques to score opportunities based on their likelihood of becoming Closed Won. The scoring system ranges from 1-99, and the scores are available as a field on the opportunity record, in list views, reports, and on the forecast page. By identifying conversion patterns, Einstein Opportunity Scoring helps sales teams concentrate on high-priority opportunities while making data-backed predictions.
  • Einstein opportunity insights: This advanced analytics product draws on machine learning and data analysis techniques to provide predictions about which deals are likely to be won, reminders to follow up, and key moments in a deal process. By examining historical data and patterns, Einstein Opportunity Insights helps sales teams make informed decisions and prioritize their efforts.
  • Pipeline inspection (Einstein deal insights + Tiered Einstein opportunity scores): Pipeline inspection assists sales managers in monitoring their team’s forecast for a given time period by providing a consolidated view of pipeline metrics, opportunities, week-to-week changes, AI-driven insights, close date predictions, and activity information. With Einstein Deal Insights, the product predicts the likelihood of winning deals within the current month and provides additional insights from Einstein Opportunity Scoring and Cases. The tiered Einstein Opportunity Scores categorize scores into High, Medium, and Low tiers, making it easier for sales managers to track the progress and prioritize efforts effectively.
  • Einstein forecasting: Leveraging artificial intelligence technology, Einstein Forecasting brings more certainty and visibility to sales forecasts. It helps improve forecasting accuracy, provides forecast predictions, and offers suggestions for taking a better approach if projected performance is suboptimal. By examining historical data and trends, Einstein Forecasting can support sales teams with more accurate and data-driven forecasts.
  • Einstein account insights: This product uses advanced analytics techniques, such as sentiment analysis and text mining, to provide valuable information about accounts. account insights can help sales teams stay on top of news affecting their businesses, including account expansion or cost-cutting, changes in company leadership, and involvement in merger and acquisition talks. By identifying these insights, Einstein account insights helps teams make more informed decisions and take advantage of new opportunities.
  • Einstein automated contacts: This advanced analytics product uses email and event activity to find new contacts and opportunity contact roles to add to Salesforce, making the process more efficient for sales teams and admins. The product can either suggest new contacts to be added by sales team members or automatically associate opportunity contact roles in the background. By utilizing machine learning techniques, Einstein’s automated contacts streamline the process of managing contacts and opportunities in Salesforce.
  • Recommended connections: Although the word Einstein is not used in the product name, recommended connections is an AI-driven feature that provides suggestions for connecting with leads and contacts based on existing relationships within the company. The product showcases colleagues with the strongest connections to an individual, helping facilitate conversations with prospects. This intelligent feature can help sales teams make more meaningful connections by tapping into their colleagues’ networks and leveraging existing relationships.
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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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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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May 3, 2023
Creating an Einstein Discovery Model and using it from CRM Analytics – Blending Tableau with Traditional CRM Analytics

Einstein Discovery, a cutting-edge machine learning platform developed by Salesforce, is designed to bring transparent predictions and recommendations to users within Tableau workflows and in Salesforce CRM. This powerful tool enables data scientists, analysts, and business users to create predictive models without writing any algorithms. With its intuitive, no-code environment, Einstein Discovery empowers individuals and organizations to make smarter, more informed decisions guided by ethical and transparent AI.
In this section, we will create an Einstein Discovery model based on our opportunity history data and investigate it within CRM Analytics. In Chapter 9 – Exploring Einstein AI and Advanced Analytics, we will examine how to use this model from within Tableau.
To create a model and investigate its parameters, use the following steps:

  1. Go to Analytics Studio and click Create | Model, as demonstrated in the following screenshot.

Figure 8.28: Model creation dialogue

  1. Choose Create from Dataset in the dialogue that appears, shown below:

Figure 8.29: Create from dataset dialogue

  1. Now select the opportunity_history dataset that we have previously created, as indicated in the following screenshot:

Figure 8.30: opportunity_history dataset selected

  1. As the goal, follow the settings in the following screenshot to maximize the amount of the opportunity:

Figure 8.31: Model goal configuration
Einstein Discovery allows for considerable freedom in manually configuring the model that you want. However, for the purposes of this exercise, we will simply allow it to make the decisions itself. Therefore, select Automated in the Configure Model Columns dialogue. Note that this does mean we will take into account whether the opportunity was won or not, which you may want to exclude in a real scenario. However, in this scenario, the influence is not huge, so keeping it in does not materially change the result. You can see this in the following screenshot:

Figure 8.32: Automated configuration selected
The system will now build your model, which usually takes 5 to 10 minutes. When it is done, you should see the following overview. You can check the alert and also note that at the highest level, shown in the screenshot below, the model’s prediction seems fairly good.

Figure 8.33: Model overview showing performance
If you now click on the Model Evaluation tab, you can see more detail about the model performance. This is shown in Figure 8.34:

Figure 8.34: Model evaluation report
It is also worth going into the Model Coefficients tab, shown below, where you can see the most important variables in the model for predicting the opportunity amount.

Figure 8.35: Model coefficients tab
Now go into the Prediction Examination tab, shown in the following screenshot, where you can see how the model classified actual data from the training dataset. This can give you a good sense of how it will work in practice.

Figure 8.36: Prediction examination tab
On the Data Insights tab, shown in Figure 8.37, accessible from the sidebar, you get an overview of what factors play the biggest role in making the amount high or low.

Figure 8.37: Data insights overview
Finally, on the Predictions tab, shown in Figure 8.38, you can actually test out different values and see what the model predicts.

Figure 8.38: Predictions tab
Have a good play with the different aspects of the model, and now, we will move on to look at the Tableau Online Output Connection.

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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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Aug 7, 2022
Conclusion-Integration, Authentication, and Tableau Viz LWC

It is important to note that Tableau-connected apps and Salesforce-connected apps are different and offer distinct functionalities. Currently, Tableau connected apps are intended for embedding Tableau views and metrics in external applications and authorizing access to the Tableau REST API.

Generally, you should not use a Connected App with Salesforce. The one exception might be if you are planning to use the same app across several target systems that need to embed information from Tableau and should be managed in a consistent way. You would just be creating more trouble for yourself.

Finally, there is the option of embedding Tableau Dashboard as a canvas app using the Sparkler framework. This used to be the preferred way of embedding Tableau Dashboards into Salesforce, but it has now been superseded by the Tableau Viz LWC Component. The setup for this option is very complex. It involves a Java-based application, Sparkler, which can be used to embed Tableau dashboards in Salesforce using Salesforce’s canvas framework.

To set up Sparkler, you must download the adapter, create a virtual machine to run it, install Java 8, install Tomcat, enable HTTPS for Tomcat, install Sparkler, configure secure communication between Sparkler and Tableau Online, and configure a connection between Salesforce and Sparkler.

Finally, you must embed and filter the dashboard on a record in Lightning Experience by creating a new Visualforce page and customizing the record page. All in all, not something you want to do, given other options. However, you should know it as you could see it in a legacy environment.

Conclusion

In this chapter, we have provided a comprehensive guide on integrating Tableau with Salesforce using the Tableau Viz LWC component. You have learned the purpose and benefits of this integration, as well as the process of installing and configuring the Tableau Viz LWC component for seamless integration in Salesforce.

Furthermore, we have delved into advanced usage techniques, including the creation of custom visualizations and modifying the component’s settings. We have also covered the implementation of Single Sign-On (SSO) between Salesforce and Tableau to streamline the authentication process and enhance security.

Additionally, we have explored alternative methods for connecting Salesforce and Tableau dashboards, such as direct connections and third-party integration tools. With the knowledge gained from this chapter, you are now well-equipped to enhance your CRM analytics in Salesforce using the Tableau Viz LWC component and make more data-driven decisions to drive business success.

In the next chapter, we will dive deeper into how you can combine Tableau with CRM Analytics to create game-changing analytical use cases.

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