Step 1: Establish a Standardized Data Set

Collectively establish and agree on a list of standard data fields. What fields do you consistently need to capture? Once you establish your master set of fields, make a Data Dictionary out of it. Convert to pick lists wherever possible – avoid open text fields, since there is no way to consistently score, report or segment on them. Spread the word that this is the Standard Data Set.

 

Step 2: Enforce the Standardized Data Set at every Acquisition Source

Review every data acquisition source and convert the process to the Standardized Data Dictionary. forms, list uploads, you name it.

 

Step 3: Append Incomplete Data fields

It’s hard to target a marketing campaign to specific Job Functions if the field is only 10% complete. Append those fields that are important for segmentation and/or scoring first. Then focus on filling in the remaining blanks.

 

Step 4: Build a “Data Washing Machine” to clean existing bad data

Normalize as many fields as possible. Start with all the open text fields you always wanted to leverage for scoring and/or segmentation but couldn’t.

 

Step 5: Merge and Purge

Clean up your duplicate records and build a process that requires a search for existing records prior to creating new ones.

 

Step 6: Keep the New Data, Discard the Old Data

Aging plays an important role in overall DB health. According to Eloqua, Lists older than 1 year have a decrease of 25% in open rates and 12% in CTR. Create active and inactive contact groups and treat appropriately.