Introduction

Data are key for many marketing activities such as segmentation, lead scoring, and analysis.
Having at disposition a range of tools to manage the data is, therefore, a key element to accomplish marketing duties.

 

Those tools should help to maintain accurate, current, and relevant data in the database ensuring high-quality data. High-quality data is the starting point to put in place effective distribution practices, in order to establish meaningful conversations and long-lasting relationships with prospects and customers.

The present blog post is about data management tools that can be used in Eloqua to cleanse the database to achieve the objective of having high-quality data.

 

Marketing challenges

Hitting targets based on key metrics such as email deliverability, addressable contacts, and the volume of the target audience, is a challenge that marketers face every day. All these targets rely on effective distribution: ensuring the delivery of the right message to the right audience.

 

Quality of the data sitting in the database is, therefore, a fundamental antecedent to reach the right audience.

The efforts to maintain a healthy database are even more important when multiple data sources feed into the database, which can lead to some inconsistencies in taxonomies and conventions. Also, changes in the competitive arena and in the consumers' demand can impact on the kind of data acquired in the database. It is also worth mentioning that some incorrect values or blank fields are derived from human errors and manual uploads. These factors can lead to poor data quality and, therefore, to the risk of missing revenue opportunities.

 

Documenting current state

To start the process of cleaning the database, the first step is to evaluate the current status of the database. This can be done through the Eloqua analytics section:

  • Contact field completeness expressed in %. Using this analysis we can assess how many blank fields we have in the database


  • Database health dashboards. This dashboard indicates the number of contacts in the database and the percentage of reachable contacts. We, therefore, expect to increase the number of reachable contacts once implemented the cleansing process

  • Fields & Views section. This is important to start building lookup tables for the update rules to overwrite wrong values or fill the blank fields




Goals

To assess the effectiveness of the data cleansing process, we need to compare the figures and stats shown above, before and after the changes. The most important goal is to reduce the number of blank fields and to correct the incorrect values.

 

 

Implementation steps

 

a. Data Audit

First of all, using the Field & Views section, it is important to carry on a detailed data audit procedure. In doing so, you will see how many incorrect values you have in your database for each record field. This will allow you to build lookup tables and then, put together your update rules that will be set up in the Program Canvas.

 

b. Building the Program to rinse your Data

To reduce the number of blank fields and overwrite the incorrect values, we used two tools in Eloqua

  • Program Canvas

Within the program canvas, we have created specific steps for the action that needs to be performed on each contact field.

  • The program canvas is mainly made of update rules
  • The update rules are created for each field we want to correct or fill up-in case the field is blank. For each update rule, we can select what kind of update action we want to perform: append a value to the field, apply data stamp, overwrite from the Lookup table, or overwrite from other fields

 

  • In case we overwrite values from lookup tables, we have to prepare a spreadsheet to upload in Eloqua: on one column, we list all the errors we found in the Field and Views, and on the other one, we put the corresponding value we want to substitute the existing field with.

  • Contact Washing Machine (CWM) that can be downloaded from the Oracle Marketing AppCloud

The Cloud actions that the CWM can perform to modify contact fields are:

  • Trim Actions to delete extra spaces
  • Case Actions to modify the casing of the field text
  • Substitute Actions to replace or remove some field text
  • Extraction Action to extract data from field text
  • Standardization Actions

 

c. Results
There are many ways to evaluate whether the contact washing machine is doing its job. One of those is to produce a second data audit report after having put in place the data cleansing process. If the tool works successfully, some figures and percentages should change, for example, the % of reachable contacts or the volume of a certain segment.

In doing so, the goal of achieving a more effective distribution selecting the right audience is accomplished.

 

 

Conclusion

A cleaner database leads to more effective distribution practices, allowing the business to target contacts that were previously excluded from marketing communications.

The cleansing process can be done effectively through some Eloqua tools- Program Canvas and the CWM app.

This has resulted in the following benefits:

  • more efficient targeting activities
  • more accurate lead scoring reports: the explicit score is now reflecting more contacts present in the database
  • more contacts resulted to have met the threshold required to pass through the next stage of the marketing funnel
  • a higher volume of MQLs that the Marketing team has been able to deliver to the Sales team
  • more opportunities for the Business
  • more accurate reports for the marketing funnel analysis, conversion, and validity of the lead scoring model.

 

An effective and efficient Contact Washing Machine will also help in the future to monitor the quality of incoming new data; it will help the marketing team to achieve its objectives around the effectiveness of targeting efforts and health of the database.

Overall, a cleaned database should result in better business performance, measurable in terms of revenues.

 

Helpful Marketing Cloud courses

  • B2B Targeting
      • B2B: Advanced Segmentation
      • B2B: Data Cleansing
      • B2B: Web Profiling
      • B2B: Custom Subscription Management
  • RPM: Effective Nurturing
  • RPM: Lead Quality
  • RPM: Targeting & Segmentation
  • B2B: Fundamentals of Segmentation