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1 Post authored by: Joel Rothman-Oracle

Immediately after Eloqua Experience 2011, we were able to get out a Chart of the Week that showed a trending timeline of the volume of tweets over time, where we highlighted significant events (such as keynotes, and yes, 2 earthquakes). Also included was a word cloud. Because we have had a lot of interest in how we did accomplished this, we decided to write up a blurb on how we used Eloqua tools to extract this data.


To capture this data, we used 2 cloud connectors. One pulled in the specific tweets, and information about the tweeters. The next took that information and captured the Klout information for those records. I am going to highlight how we set up both of these to capture the data, and what the programs looked like. I’ll start with a quick overview of the process, and then dive into the specifics.


Using the “Tweet Feed” Cloud Connector, you define what search term you want to capture. For Eloqua Experience, we captured the hashtag #ee11sf. This cloud connector takes data about both the tweet and the tweeter. The tweet information gets stored in a custom object (one entry for each tweet). The information about the Twitter user (such as a description and the number of followers etc…) gets stored on the contact in Eloqua.


This contact is then fed into a program, which uses the Klout Cloud Component. This component grabs the twitter handle and does a lookup on the associated Klout score, which gets written back to the contact.


Now, to set this up, the first thing you need to do is create a custom object ( Name it something descriptive. For example, we named ours EE11SF Tweets. Add the following fields:

  • Tweet ID Field
  • Message --> This should be set up as an extended character field
  • Message Time --> This should be set up as a date


Define the “Tweet ID” field as the unique key in the table


The next step is to create the appropriate fields on the contact. The only required field is the Twitter ID field. Everything else is optional. The fields are:

  • Twitter Id field
  • Twitter Name Field
  • Twitter Location Field
  • Twitter Description Field
  • Twitter Followers Count Field --> this should be set up as a numeric field
  • Twitter Following Count Field --> This should be set up as a numeric field
  • Tweet Count Field
  • Twitter URL Field
  • Klout Score


You can create a contact group to hold the records and a contact view to contain the fields.


Creating the tweet feeder:


To create the Tweet feeder, navigate to this URL:
If you do not have a login, please sign up.
Under the credentials set up, you will need to input the Eloqua install, a user and a password. This user will need to have API access to Eloqua enabled. Support can help set that up. You will also need to set the query frequency, which is how often the data is pulled. You can leave the step disabled for now, but you will need to remember to enable it later.
Under the configurations tab, you will need to authorize a Twitter account. This is only used to authorize the API to capture data from twitter. Nothing will be posted to this account. You will also input the search query and the contact group. Under “Store Tweet Data in” select “Custom Object” and map it to the object that was created before. Click save.


Under field mappings, map the as they are created earlier. Click save, and you are ready to go.


Creating the Klout feeder:


The Klout score is stamped in a program using a Cloud Connector. The first step is to create the connector in Eloqua. To do that, visit this site ( and select “Get Ap”. After that is done, you need to log in to Eloqua and create a simple 2 step program (or add it into your standard program flow). My sample program has 3 steps, but the top one is not necessary. See the annotated image below that includes all the configuration steps:



For the chart the data manipulation was done in Excel (we love us our pivot tables). The word cloud was done using a free tool called Wordle (


Remember, you can sign up to have the Chart of the week delivered to you by signing up at
How are you going to use this data? Let us know!

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