While the concept of the Contact Washing Machine has been out for a while, I wanted to provide a simplified post for those looking to get started in data cleansing. It's not a surprise that the cleaner your data, the more effective your segmentation and lead qualification strategies will be. If you can't make sense of the data that you have, you are leaving opportunities to your competitors that are able to reach those people. You can also check out this excellent post on Hexaware's Data Washing Machine by Amit Varshneya from Hexaware.


We'll start with the concept of normalizing titles. If you're looking through your database and you have hundreds to thousands of job titles that you want to group into buckets, this post will help you create a process to clean your existing data by grouping your database into job title buckets. While some recommend forcing the user on webforms to select their job role, sales people prefer knowing the specific job title and when data is added to the database, it's difficult to ensure that people are assigning the correct job title. It's time to put Eloqua to work to make this easier for you.


This post assumes that you are familiar with Eloqua. You may need to do some research in the Knowledgebase on some of the specifics of the features mentioned. In addition as it uses Program Builder, I would check out the video on Program Builder in Eloqua University or the courses that are available.


To begin this process you need to have analyzed the title field in your database and created normalized "title buckets" that will be used to group the contacts in your database. Examples may include: COO, Manager Level, Sales Professional. These are based on how you currently do your segmentation (or how you want to do your segmentation). One tip worth experimenting with is using a free tool called "Google Refine" to help examine your data and look for trends.



Step by step instructions:


  • Create a new contact field in Eloqua called:"Title Normalized" or "Segmentation Title" (or whatever you feel is appropriate). This field will be the field that you will use for creating your segments and for scoring off of.
  • Create a new Update Rule Set called: "SYSTEM - Data Normalization - Title (Contacts)". Select the entity as Contacts when you create it.
  • Now you're going to create a bunch of update rules so that all of your contacts that have different titles will be updated with a normalized title. For example, if you want all contacts that have a title of "Chief Operating Officer" to have a normalized title of "COO", you would do the following:
  • Add an Update Rule to the Update Rule Set
  • Select the new field "Title Normalized" you created as the field you want to update
  • Select "Set to Value" as the update Action
  • For Value, enter the "title bucket" that you want. In this example, it's COO
  • Check off "Make this rule Conditional"
  • Choose the Title contact field
  • Change it to "Equal to"
  • Enter in the value that the program should look for to normalize. In this example, enter in: Chief Operating Officer


Here is what it looks like:

  • Since this is a washing machine, you now have to rinse and repeat. Save this Update Rule you created and create as many rules that are needed so that you can normalize all of your records into consistent title buckets. Keep in mind that you many not get all of them at the first go and that this is an iterative process.

    An added feature with Update Rules is that you can use the wild card character: *. This makes it easier to identify records and update them. For example, if you are looking to create a normalized title bucket of COO, you can enter a value such as Chief Op*. In this case, the wild card looks for any contact with a title of "Chief Op". Therefore, Chief Operating Officer would be picked up as well just "Chief Operator". You need to be careful on how you apply the wild card character.

When you're done, you'll have a list that looks like this:


Something to consider: Order the evaluation criteria (the Update Rules) of your Update Rule Set to ensure the highest quality matches. You may need to test some data to perfect this which we'll do once we build a Program in Program Builder in the next step.


  • Now you can create a Washing Machine Program and run your contacts through the program using Program Builder. Go to Program Builder and create a new program. This is a very simple program. In the Program Details, make sure the Default Member type is Contacts. This will be a very simple program.
  • Once you've created the program, add an Action step as the first step in the program but don't add any actions to it.
  • Now, add a new Action step to the first step. Select the "Update Contact/Prospect/Company Data" action and choose the Update Rule Set you created:

  • Add a final step that will remove contacts from the program. Your Program should look like this:


  • Test your Update rules by running some sample contacts through the program and make changes if needed to ensure that the majority of titles are accounted for and are normalized. You may want to disable the final step in the program and review the data for the first few weeks. Once you are confident that all is OK, let the contacts flow freely through the program.
  • You can now add in feeders to the program and add it to your overall data flow:
  • You can also pass the data to your CRM integration program using the "Move to Another Program" program action. Change the last step of the program to this action as per below:



Good luck on cleansing your data!