Pardot Tableau Integration

This article aims at providing you with a step-by-step guide to help you set up Pardot Tableau integration for a fruitful analysis of your marketing data.

Upon a complete walkthrough of the content, you will be able to visualize your Salesforce Pardot data using Tableau with ease!

It will help you draw crucial insights about your marketing performance and customers. You will be able to provide an enhanced personalized experience to your customers, target them better, & carry out analytics on your customers’ entire journey across numerous channels.

Understanding the Need to Set Up the Pardot Tableau Integration

• Salesforce Pardot provides users with intuitive engagement-based data, allowing them to draw crucial and valuable insights about how their prospects interact with the marketing collaterals.
• It further supports generating business reports on this data by letting users leverage the connected campaign’s functionality.
• However, such in-built reporting and analytics functionalities are quite limited and, hence you’ll need to have a robust business intelligence tool in place, that allows you to analyze this data and draw data-backed actionable insights.
• One such tool that allows users to analyze the engagement data & draw crucial insights is Tableau.
• It lets users leverage in-built robust data exploration and analytics functionalities to analyze their customer engagement data and draw actionable insights that help them optimize their customer interaction and engagement strategies.

Prerequisites

• Working knowledge of Salesforce Pardot.
• Working knowledge of Tableau.
• A general idea about APIs.
• A general idea about Python and its libraries.

Methods to Set Up Pardot Tableau Integration

Method 1: Using Salesforce Pardot APIs

• Salesforce Pardot provides users with a diverse set of APIs, allowing them to retrieve and interact with data programmatically.
• Salesforce Pardot lets users authenticate either by using the API keys or using the Salesforce OAuth authentication, ensuring that each transaction remains safe.
• Implementing this authentication through custom scripts adds an unwanted overhead. Hence it is recommended to use one of the Salesforce Pardot client libraries to simplify this.
• Salesforce Pardot client libraries are available in PHP, Python, and Java. Here, you will be leveraging the Python library known as PyPardot. 

You can implement this using the following steps:

• Step 1: Installing and Configuring Salesforce Pardot’s Python Library
• Step 2: Querying Data using Salesforce Pardot API
• Step 3: Transforming Salesforce Pardot API Response into CSV Files
• Step 4: Loading Salesforce Pardot Data to Tableau as CSV Files

Step 1: Installing and Configuring Salesforce Pardot’s Python Library

To start setting up Pardot Tableau integration using Salesforce Pardot APIs, you first need to install the PyPardot Python library on your system. To do this, you can use the pip command as follows:

pip install pypardot4

Once you’ve installed the Python library, you now need to import the CSV and Salesforce Pardot API class on your system. To do this, you can use the following lines of code:

 import csv
 from pypardot.client import PardotAPI

With your Salesforce Pardot API class now installed, you now need to authenticate your Salesforce Pardot account.

To do this, you first need to get your Salesforce Pardot user key, found under the my settings option of your Salesforce Pardot application. Once you’ve fetched it, you now need to create a Salesforce Pardot client by specifying your username, password, user key, etc. as follows:

pardot_client = PardotAPI( email='your_email',password='your_password',
                                     user_key='your_user_key' )
p.authenticate()

Ensure that you reinitialize the script as the authentication will last only for an hour.

This is how you can install and configure the Python library for your Salesforce Pardot instance.

Step 2: Querying Data using Salesforce Pardot API

Once you’ve established the connection with Salesforce Pardot, you can now start querying and fetching data by leveraging the Salesforce Pardot API. To do this, you can use the following lines of code:

email_clicks = p.emailclicks.query(created_after='yesterday')
results = email_clicks[‘total_results’]

This is how you can query data from Salesforce Pardot using APIs.

Step 3: Transforming Salesforce Pardot API Response into CSV Files

With Salesforce Pardot API now fetching data, you now need to convert and write the API responses as a CSV file. For example, if you’re fetching data about email clicks, you can use the following lines of code to save it as a CSV file:

with open('email_clicks.csv', 'w', newline='') as file:
           writer = csv.writer(file)
           for email_click in results[‘emailClick’]
                   id = email_click.get(‘id’)
                   prospect_id = email_click.get(‘prospect_id’)
                   list_email_id = email_click.get(‘list_email_id’)
                   created_at = email_click.get(‘created_at’)
 			 
           writer.writerow([id,prpospect_id,list_email_id,created_at]

Salesforce Pardot provides users with numerous other objects and other attributes apart from email clicks, IDs, etc.

This is how you can transform the Salesforce Pardot API responses into CSV files to set up Pardot Tableau integration.

Step 4: Loading Salesforce Pardot Data to Tableau as CSV Files

Once you’ve fetched your Salesforce Pardot data as CSV files, you can now import it into Tableau. To do this, you can use the in-built text connector.

To do this, launch Tableau on your desktop and select the text file connector, found in the connect panel on the left. Once you’ve clicked on it, a new window will now open up on your screen, where you can select the CSV files you want to import into Tableau.

This is how you can set up Pardot Tableau integration using Salesforce Pardot APIs.

Conclusion

This article teaches you how to set up Pardot Tableau integration with ease. It also provides in-depth knowledge about the concepts behind every step to help you understand and implement them efficiently.

Carrying out an in-depth analysis of your marketing data requires you to integrate data from a diverse set of marketing data sources.

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