Category Archive: Data Analytics

Salesforce: List Views vs. Reports

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Salesforce is one of the most popular customer relationship management (CRM) tools used by non-profits and associations. Part of the reason Salesforce has become so popular is because it’s so easy to use. Salesforce offers a wide variety of tools and processes that help organizations run smoothly – without the use of code. In fact, Salesforce was just recognized as a Leader by Gartner Inc. in its 2023 Magic Quadrant for Enterprise Low-Code Application Platforms. Low-code and no-code platforms are ideal for organizations that need functionality, but don’t have a development team in-house.

Salesforce: List Views vs. Reports
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Two prime examples of no-code or configuration based Salesforce data tools are List Views and Report Builder.

When you’re working with Salesforce, though, really understanding the differences between List Views and Reports is crucial. Once you know the difference and define the purpose for each tool, you can make the most out of your data and improve your team’s productivity.

List Views

List Views are a powerful tool for organizing and displaying data in Salesforce. They allow you to quickly filter and sort your data based on specific criteria. When viewing data in List View, you’ll be looking at a clean, easy-to-follow table of information. In List View, you can customize your data display by selecting and organizing the fields you want to see. This way, you’re getting the information you need without having to sort through what you don’t.

Quick and efficient data access isn’t the only thing List Views are used for. List Views can also be used to manage member data effectively. For instance, you can create a List View to see all members who haven’t renewed their membership, or those who have yet to register for an upcoming event. List Views like this can make it easier for your association to execute targeted marketing campaigns and prevent members from being lost in the shuffle. You can also save your List Views as a personal or shared view, making it easy to access frequently used searches.

Salesforce List Views are an incredible asset to associations, but it’s important to emphasize that they cannot perform data analysis or generate summary reports. They are best suited for day-to-day tasks that require quick access to relevant data.

Reports

Salesforce Reports offer more advanced functionality and analysis tools than list views. You can use them to summarize and analyze data in a variety of ways, including charts, tables, and graphs. Just like with List View, you’ll be looking at data that’s been sorted to your specifications, but you’ll also see relevant data analytics that allows for in depth analysis.

With Salesforce Reports, you can create custom reports to analyze and measure your members’ engagement. For example, you can create a report to track the number of event registrations per member category, or monitor the progress of your membership campaign. Reports are a great way to take the raw data you’ve been collecting and turn it into easy-to-understand summaries about performance.

Reports are also helpful for identifying trends and patterns in your data, identifying areas for improvement, and tracking progress over time.

While Salesforce Reports are undeniably important, it is important to note that using them properly and effectively requires that advanced underlying report types already exist. Once those are in place, the reports you need to generate can be easily created. If you and your association are not familiar with report creation, it’s best to seek assistance from a Salesforce expert to get started.

When should I use a List View versus a Report?

Overview of List Views
  • Basic filtered views of records
  • Quickly segment data
  • View information from a single object
What you CAN do with List Views
  • Allows users to create views specific to them
  • Can filter and “report” on data while viewing data on an object
  • Use the List View results to mass edit records, such as following multiple records and editing data on multiple records
  • Contact List View results can be added to Campaigns
  • Loads up to 50 values at a time — simply scroll down to load the next 50 values
  • Bulk updates on List Views can be performed for up to 200 records at a time
What you CAN’T do with List Views:
Overview of Reports
  • Used to summarize, calculate, and analyze data
  • Combines data from multiple records
  • Standard and Custom Reports
  • 4 standard report types: Tabular, Summary, Matrix, or Joined
  • Export to Excel or CSV
  • Reports can display up to 2000 rows of data — additional rows can be viewed once exported
What you CAN do with Reports:
  • View data across objects, such as Contacts with a set of Interest Areas and/or Geographies
  • Ability to summarize data, such as Activity/Task data (Open for a specific user, All Closed for a time period, etc.)
  • Schedule Reports
What you CAN’T do with Reports:
  • Joined Reports cannot be exported to Excel
  • Complex Salesforce architecture and configuration may limit how many objects can be seen on a single Report

Both tools bring a lot to an organization’s table, but it’s crucial to understand their limitations. While List Views are ideal for day-to-day tasks and offer quick access to relevant data, they cannot perform data analysis or generate summary reports. Reports, on the other hand, require an understanding of the data structure to create, and they’re only available to Salesforce users with the appropriate permissions.

Salesforce offers powerful tools for associations like yours to organize and analyze data. List Views and Reports are both valuable features that can help association staff members manage their data in different ways. By understanding the differences between List Views and Reports, as well as their limitations, associations can make the most of these powerful tools while improving their productivity and efficiency.

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10 Important Features of Tableau

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Businesses deal with a lot of data, and analyzing it in its raw form is frequently difficult. The quality and accuracy of the datasets you’re working with increase when you present them in attractive graphs, charts, shapes, and plots. Tableau is the most extensively used data visualization application.

Tableau is a data visualization program that allows you to generate basic oriented graph-like data representations by querying cloud databases, spreadsheets, machine learning algorithms, social databases, and other database improvements. So, in this blog, we’ll highlight the 10 most important features of Tableau that help businesses in visualizing better.

1. Informative Dashboards

Tableau Dashboards combine images, visual objects, text, and other components to present a comprehensive view of your data. Dashboards are extremely useful because they may provide data in the form of stories, allow for the inclusion of various views and objects, offer a range of layouts and styles, and allow users to apply appropriate filters. You may even effortlessly duplicate a dashboard or its individual features from one worksheet to another.

2. Supports numerous data sources

You may connect to and fetch data from a variety of data sources using Tableau. Tableau supports a wide range of data sources, including local files, spreadsheets, relational and non-relational databases, data warehouses, big data, and on-cloud data. Any of Tableau’s data sources may be readily connected and combined with data from other sources to generate a combinatorial perspective of data in the form of visuals. Tableau also supports a variety of data connections, including Presto, MemSQL, Google Analytics, Google Sheets, Cloudera, Hadoop, Amazon Athena, Salesforce, SQL Server, Dropbox, and a number of others.

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3. Connectivity with Live and In-Memory Data

Tableau offers in-memory data connection to both live and external data sources. This allows the user to freely combine data from several types of data sources. By creating live data connections, you may consume data straight from the data source or maintain data in memory by extracting data from a data source as needed. Tableau offers additional data connections capabilities including automated extract refreshes, notification of a live connection failure, and so forth.

4. Provides Great Security

Tableau takes extra precautions to protect data and users. For data connections and user access, it features a fail-safe security system based on authentication and authorization mechanisms. Tableau also allows you to connect to other security protocols like Active Directory and Kerberos. Tableau employs row-level filtering, which aids in the security of the data.

5. Easy Collaboration & Sharing

Tableau provides easy ways for users to communicate with one another and exchange data in real-time in the form of visualizations, sheets, dashboards, and so on. It enables you to securely communicate data from a variety of data sources, including on-premise, cloud, hybrid, and so on. Instant and simple cooperation and data sharing aid in obtaining immediate assessments or input on data, resulting in a more comprehensive study.

6. Provides a Mobile Version

Tableau recognizes the importance of mobile phones in today’s society and offers a mobile version of the app. Dashboards and reports may be created in a mobile-friendly format. Tableau allows you to adjust mobile layouts for your dashboard based on your mobile device. Customization allows you to add new phone layouts, interactive offline previews, and more. As a result, the mobile view provides Tableau users with a great deal of flexibility and convenience while working with their data on the move.

7. Advanced Visualization Capabilities

Tableau’s wide range of visualizations is one of the primary elements that has contributed to its success. Tableau allows you to create visualizations as simple as a bar chart or a pie chart, as well as more complex ones like a histogram, Gantt chart, Bullet chart, Motion chart, Treemap, Boxplot, and many others. By selecting the visualization type from the Show Me menu, you can simply pick and create any form of visualization.

8. Availability of Maps

The map is yet another key aspect of Tableau. Tableau comes with a lot of pre-installed map data, including cities, postal codes, administrative borders, and so on. As a result, Tableau’s maps are extremely comprehensive and insightful. You may customize the geological layers on the map to meet your needs, and use Tableau to generate meaningful maps with your data. Heat maps, Flow maps, Choropleth maps, Point distribution maps, and more types of maps are accessible in Tableau.

9. The Ask Data Tool

Tableau’s Ask data tool has increased its popularity among users all around the world. This tool simplifies data manipulation by allowing us to conduct basic Google searches. Tableau will give you the most relevant replies if you just enter a question about your data in natural language. The responses are presented not just as text but also as graphics. For example, if what you’re looking for is already in a bar graph, the Ask data option will search for it and open it for you right now. Users may simply go deep into data and uncover new insights and patterns thanks to capabilities like these.

10. Trend Lines & Predictive Analysis

The use of time series and forecasting by Tableau is another really useful feature. Creating trend lines and forecasts is straightforward with Tableau’s powerful backend and dynamic front end. To acquire data predictions such as a forecast or a trend line, just pick particular parameters and drag-and-drop operations employing your concerned fields.

Conclusion

Tableau is an excellent program to simplify all your data visualization activities and provide better and more accurate analysis if you’ve ever tried data visualization and found it tough to grasp or too complex.

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What is Tableau CRM?

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What is Tableau CRM and why you should look at Tableau CRM if you use Salesforce?

Salesforce launched an integrated business intelligence solution with Einstein analytics in 2016.

Subsequently Salesforce purchased the BI market leader when they acquired Tableau in 2019. That has lead to Salesforce renaming Einstein Analytics to Tableau CRM. Its expected that more of Tableau’s rich feature set and visualization libraries will make its way into Tableau CRM

According to Salesforce: 

“Tableau CRM (formerly Einstein Analytics) is a revolutionary customer and business analytics platform that’s optimized for mobile use and brings flexible customer analytics to your CRM. It works with any data, from any data source, and it will change the way your company answers critical questions.”

Tableau CRM has some unique features that are interesting to Salesforce customers:

  • Integrated solution: Tableau CRM is embedded into Salesforce CRM and BI dashboards can be embedded on various Salesforce pages.  
  • Predictive analytics: Tableau CRM can do regressive and predictive analytics, lead scoring and recommendations. 
  • Ease of use: Unlike other enterprise BI solutions, Einstein is very easy to setup and use, offering a wizard-based approach and automated data discovery to setup new dashboards.

Tableau vs. Tableau CRM?

According to the Salesforce FAQ, selecting Tableau vs. Tableau CRM will depend on your use case:

  • If you are looking for an enterprise-wide business intelligence solution, Tableau is the right product for you. Tableau offers an end-to-end analytics platform serving a broad range of enterprise use cases.
  • Tableau CRM is ideal for your team members who need their analytics deeply integrated throughout their Salesforce CRM, providing actionability and AI-driven insights natively in the platform. If you are looking to augment Salesforce Clouds with insights to drive productivity in the CRM workflow, Tableau CRM is the best product for you.

How does Tableau CRM compare to native Salesforce reports?

  • Reports and Dashboards provide an instant snapshot of the metrics that matter to your organization. It looks and behaves like Tableau CRM, but provides very different functionality.
  • Tableau CRM extends beyond Reports and Dashboards to give you new views into your data, end-to-end customer insight, and historical analytics. It can also help you find trends and make discoveries you weren’t specifically pursuing, making it a much stronger choice for data-driven decision-making.

Leverage Einstein Prediction Builder For Your Association’s Next Membership Campaign

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Suppose you are reading this as the marketing manager for your association or organization. Every summer, a number of contacts in Salesforce’s Fonteva platform subscribe to premium memberships. As the marketing manager, you will want to try to understand these members by building dashboards, conducting studies and leveraging statistics. In fact, you might have already done this by using tools such as Einstein Discovery and Einstein Analytics. However, there is still a missing piece, as you need to identify opportunities based on the research you’ve completed thus far.

In comes Einstein Prediction Builder, a data science tool that utilizes Salesforce Artificial Intelligence and Machine Learning (AI/ML) to help you build models and make predictions. With this builder, you can quickly find patterns within your membership data, and even predict which contacts are most likely to sign up for premium memberships in the future.

In the conventional data science world, creating a product that can help you make predictions takes a high level of effort. The steps the data science team needs to take to build a viable data science product must include: setting up data storage, building and testing data models, building data pipelines and creating API endpoints. The process is software and code heavy, and the success of a project can sometimes be very low if the data does not have any underlying patterns.

Prediction Builder is custom AI for admins based within your CRM data available in Enterprise, Performance, Unlimited, and Developer editions of Salesforce. Einstein Prediction Builder can be implemented either as a standalone product within your Salesforce CRM, or as part of the Einstein Analytics Plus package. While there is a free version of Einstein Prediction Builder (called “Try Einstein”), that version only lets you build up to 10 predictions at one time.

There are three main benefits of using Einstein Prediction Builder:

  • It is a point and click solution
  • There is no code required
  • The entire infrastructure along with workflow is laid out for you

For associations, this serves as a great benefit, as a lot of the business questions associations ask are related to membership, certifications and events. Being able to quickly deploy models and address business questions can greatly improve your associations’ ability to attract and retain members.

Requirements for the Dataset / DataObject

For Einstein Prediction Builder to work, you need a well structured dataset that contains multiple variables and one predictive outcome. The prediction outcomes that Einstein Prediction Builder can generate is limited to a Yes/No answer or a Numerical Value. For instance, if you want to predict how much revenue a contact is likely to generate (customer lifetime value), you can set your total sales as your numerical outcome. If you want to predict which contacts are most likely to sign up for membership, you can set whether a person signed up or not as your Yes/No outcome.

The spreadsheet image below contains mock data we used to build our membership predictions, which also serves as a good example of how Einstein Prediction Builder looks at data. In the dataset, the green column, column P, contains Yes/No entries, this is our known outcome of whether someone signed up for memberships or not. We’ll be using most of the data from columns C to O to predict the unknown cells in Column P.

The general idea is to feed Einstein Prediction Builder training data that contains some of the existing outcomes, in order to predict the remaining unknown outcomes.

Do note that our known Yes/No data points need to be at least 400 entries, approximately 50% positive and 50% negative. Einstein Prediction Builder will eventually use the example data to train itself to build a suitable model to populate the prediction (unknown) data.

Building your Prediction Model

To start building the model, we will tell Einstein Prediction Builder that we are predicting Yes/No outcome and choose “No Field” as shown below so we can split the example and prediction data easily.

We will then tell Einstein which records we’d be using for “Yes”/”No” examples, in our case, it’s column P outcome_term_subscription that we mentioned above in our prerequisites. Using the Data Checker on the right hand side, we can see how big our examples are and how many records we are trying to predict at the moment.

After that, we choose which columns to contribute to your predictions, making sure we do not include any unnecessary features such as who created the record in salesforce and the email address of the contact. Just to reiterate, we are using age, income, contact_times, education to predict whether a person will sign up for membership or not.

Finally, we can click through the remaining pages and land on the status page for our model. Depending on how much data you are using to train your model, the time it takes to generate a model could range from 30 minutes to 24 hours.

Once the model is completed, we are able to obtain a scorecard on how well the model did. In our example, we can see that our prediction quality is around 74 points, which means that our model will be 74% accurate in predicting future outcomes. The top predictor side bar graph shows that the variables that most impact the model include: whether a person signed up for previous memberships or not, whether they have a housing loan and how many times they were contacted this year.

To dig into more details, we can click on the predictors tab and see that housing_loan field has a strong impact but negative correlation, which means having a housing loan negatively impacts our model and the person is less likely to sign up for premium membership if they have a housing loan.

The other predictors such as prev_outcome – success, contact_times all have positive correlation and impacts over 0.50, which means they contribute to our outcome predictions positively and people that signed up last term and contacted multiple times are likely to sign up again.

Moving on, we want to get a list of these members so that we can reach out to them. In order to do so, we move to Salesforce’s front page and create a list based on the premium_membership object.

We proceed to filter the list by unknown outcomes and include contact emails with high impact variables. We also include the prediction results (score). This will be blank when first added, but will populate after about ten minutes or so.

After waiting for our results, we refresh our Salesforce page and filter by highest prediction_results. Prediction results show the likelihood of a successful sign up. We can see that the first user is most likely to sign up with a score of 91. We should reach out to him to see if he is interested in signing up for membership.

Monitoring your Prediction Model

So far, we have built and enabled our prediction model and created a list of users who are most likely to sign up for premium memberships. As new data comes in, our model will continue to make predictions. Based on this new data, the models performance could change. Thus, it is recommended to check periodically how well the model is doing by comparing the prediction results with the actual results.

Usually, this check is done by looking at the true/false positive predictions of the model over a certain period of time. An example of this is provided below:

Stay Tuned For More To See How fusionSpan Can Help

Overall, you’ve seen how you make a prediction with Einstein Prediction Builder and single out high value targets within your association for your next membership campaign. As mentioned before, this is a hassle free, no code required application that allows you to make predictions with Salesforce objects.

For those using the Fonteva platform, this will be particularly useful. Users will be able to quickly deploy models, comprehensively view how well the model performed and create dashboard visuals around the prediction outcomes.

Of course, that is not the only tool your association can leverage. Apart from Einstein Prediction Builder, there’s also Einstein Discovery; Discovery is another tool on the Einstein platform that can automatically analyze millions of data combinations in minutes. Einstein Discovery can help associations better understand who their customers are and why they do what they do.

If you would like to know more about Einstein Prediction Builder, stay tuned for our upcoming articles diving deeper into the tool. In the meantime, reach out to fusionSpan for help setting up your next membership campaign for success!