The Challenge

Working in a large organization has numerous benefits.  But when you have multiple businesses and a single Eloqua database, keeping data consistent, standardized and imported into the system in a timely fashion has its challenges.  Trade shows, for example, often leave us with dirty data. Vendor forms from content syndication in trade journals also present similar issues—inconsistent, non-standardized data.  Getting the data imported into Eloqua also takes time, sometimes too much time.  The delay in importing contacts has made follow-up emails untimely and even irrelevant.  In order to become better marketers, the timing, consistency and completeness of data for Eloqua needed to be fixed.

 

The Solution

Part 1 – Audit the database

To address these issues, we reviewed our database and looked at the most prominent data issues.  Further, we polled our internal stakeholders to determine the most valuable information used in lead scoring and marketing segmentation, so that these fields contained clean and consistent data. Based on both input from our team and the glaring issues we observed, we decided it was critical to standardize the following fields:

  • Buying role
  • Buying/project horizon
  • Customer type
  • Job role
  • Job function
  • Customer type
  • Industry
  • Lead source – most recent
  • Country
  • State or Province

 

Part 2 – Creating a contact washing machine program

In our analysis, we found that many of the values in the data were similar, with slight variations such as spelling or syntax differences.  The solution was clear, especially based on insight from a Modern Marketer Experience Conference.  We needed to develop a contact washing machine program – a robust program that can help standardize data.  You can learn more in the Targeting class as well and through Topliners.  Our program consists of update rules, look-up tables as well as additional logic in the program canvas.

 

The contact washing machine program proved to be valuable.  It immediately improved reporting, lead scoring, and segmentation. Beyond standardizing the information, we added a few steps in the program to append data to the contact record.  For example, we use our program to annex region based on the country, since region information supports contact-level security. In another use case, we added National Electrical Manufacturers Association (NEMA) trade area and the sales district based on the U.S. zip code; district names and NEMA trade areas are convenient when it comes to lead routing in the U.S.  Trade area information is also helpful to segment the database, so that we can access that information when we hold a customer event at a local sales office. Overall, the contact washing machine improved information, but it didn’t solve all of our data-related challenges. 

 

Part 3 – Clean it from the start

In a contact washing machine program, you need to map dirty values to clean values.  It can be a challenge to capture all possibilities. While we used our initial data as the source for our mapping, new data coming in from trade shows or trade publications often contained values that we didn’t have in our program.  With limited resources, we needed to come up with another solution to solve the external source challenge.  Further, we needed to address the lag time it took to upload data. 

 

To improve lead capture and data from trade shows, we analyzed a variety of options and selected Zuant, which is  an IOS-based system used for events.  With a use of a developers’ kit, available for most shows, booth staff can simply scan a visitor’s badge to collect their contact data.  That data feeds into a form in Zuant, which can also include some customized questions for each show. The Zuant form mirrors an Eloqua form on the backend and makes it easy and possible to send contacts/leads to Eloqua in real-time. With a click of the button, our team is able to send the leads directly into Eloqua as soon as they are ready.  Once the contacts pass into Eloqua, the system gets to work and sends follow-up emails and adds contacts to appropriate campaigns, which ultimately results in more timely and relevant communications.  Similarly, we implemented Integrate with other third party data sources. Integrate’s benefits are analogous to those from Zuant, but this program is used for data coming in from trade journals and other data sources.  We define the questions and the picklists that are used on the front end with media outlets. The data is sent automatically to an identical Eloqua form – eliminating the need to manually import data.

 

Results

Implementing the contact washing machine, Zuant and Integrate have helped to solve our data issues—so that our data is consistent, more accurate and communications can be sent in real time.  Specifically, the contact washing machine program helped reduce the number of field values. 

  • Buying role –  56 values to seven
  • Buying/project horizon – we had two different fields collecting this info.  The values went from 44 to 10.
  • Job role - 164 values to 10
  • Job function -   465 values to 18
  • Customer type -  78 values to 13
  • Industry - more than 1000 values to 33
  • Lead source most recent-  1029 values to 25
  • Country and state - standardization, so there is not a mix of abbreviations and proper names

This standardization improved segmentation and lead scoring. Many of our campaigns today were not active before the cleanse, therefore we are not able to provide an apples to apples comparison for impact on KPIs.  However, our industry segmentation has much smaller list sizes, and we find that the highly targeted lists yield higher opens and click-through rates. Further, the integration with Zuant and Integrate helps to funnel data immediately into campaigns.  We have the ability to follow up on trade shows that leverage the Zuant integration in a matter of day or two, whereas we have a delay in response time to events that utilize other systems, which, in some cases, can take weeks or longer. 

 

To improve results from your marketing campaigns, take action and start cleaning your data!