There are a few steps you can do to ensure that data in your marketing automation tool stays clean. Clean data means that your data is consistent, correct, current, complete and de-duped. Regardless of what tool you use and the amount of data that is being stored in your tool, you can take the following steps:

  • Drop Downs on Form Submits – Ensuring that your form submits have the correct drop downs and mandatory fields complete will make sure that you get consistent data. The following are just some fields that are probably better as a drop down: industry, title level and/or function, country, state/province, employee range and revenue range. Having consistent data ensures that you can use the field for segmentation, analytics and territory planning.
  • Standardize Fields on Your List Uploads – Prior to uploading the list you want to make sure the format of each field matches your field set-up to ensure fields are standardized. For example, if the list you received has two digit state code such as “NY, CA, TX” and you store states as “New York, California, Texas”, you can see how either your list upload will fail or you will introduce new values to state field and may miss the newly uploaded records from your next regional email.
  • De-duping prior to uploading lists – The other big issue is uploading duplicate records. While most tools will not allow a record with the same email address to be added, the fields that are already in the system may or may not be overridden with the latest data. A decision needs to be made if the list that you are uploading gets higher priority than what is already in the system. Some companies decide only to append data where fields are blank. The issue than becomes outdated data, so some may choose to overwrite data that is already in the system as they get data from a fresher source. The correct solution is to judge each list differently. If you are uploading account information you want to make sure that your list gets de-duped based on website, address and other key fields prior to uploading it to the system.
  • Keep track of bounces – As people change positions every couple of years if you are running B2B Marketing you probably want to keep track of all records that have bounced. If you do it systematically every month or quarter, there will be no need for large data cleaning project every few years. You can start keeping track of bounces creating a smart-list or report to put all bounces there. You can verify the reason why they bounced, if it was a hard bounce it could be that an email is simply misspelled, the person is no longer there, or it is a bogus record. If it is bogus it should be added to your master opt-out list as typically bogus emails such as or tend to repeat themselves. By checking if the person moved to a new company using LinkedIn you can try getting their new email address if they were a customer record previous or send a message to your sales team to connect on LinkedIn, at the very least you should mark that record as no longer there.
  • Using Dynamic Forms – By identifying key fields that are important for your business, you can make sure that dynamic forms are created to ask your contacts to fill in only the information that is missing the next type they fill a form on your website. That way you can add data to your records without decreasing form conversion rate.
  • Double Checking Integration with CRM – Make sure your marketing automation tool is integrated correctly with your CRM. The same field types are selected, and data is flowing the way you designed it to flow. A lot of data quality issues arise when data from CRM is added to marketing automation tool not in the right format.
  • Data Quality Reporting – It is hard to predict where bad data will come from, it can be from a specific source such as form submits, tradeshows or CRM. Bad data can also be created by a certain sales rep or employee. Therefore, it is a good idea to keep track of what percentage of your data is incomplete, your bounce rate and what percentage of your records are aging. You can present this data by source, sales rep and date created. This will ensure that you can pin point your data quality issues before they become a greater problem.

After you have implemented these basic steps to keeping your data clean, it is advisable to conduct a data audit to make sure that you identify areas of your data that may require de-duping, data appending, data standardization or other data cleaning.