Are you fumbling with your marketing automation software? If you’re hearing complaints that an email campaign went out to the wrong contacts or you’re finding discrepancies in your reporting, it’s likely that your data is to blame for some of your problems.
In fact, database cleanliness is one of the most common causes of marketing automation frustration that we see with incoming clients.
The solution? Data cleansing.
Data cleansing is the process of updating or removing data that is duplicated, incorrect, incomplete, or improperly formatted.
The Benefits of Data Cleansing
The cleaner your database, the better your marketing automation results will be. Clean data will help you achieve:
- Better email segmentation – The cleaner your data, the better you can identify and segment known leads so you can provide them with target content and usher them down the sales funnel.
- Better content customization – Clean data enables you to serve dynamic content within an email campaign that is tailored to either prospects or existing clients.
- Better lead qualification – Clean data helps you standardize and improve your lead scoring system, which means you’re only sending qualified leads to your sales team. This eliminates squabbling between the two departments and helps the sales team to focus on only the best prospects.
- Better reporting – Your reporting relies on your data, which means that your reports will only be as clean as your data is. Keeping it squeaky clean will help you utilize closed-loop reporting to attribute leads and measure ROIs.
Prepping for Data Cleansing
Before you clean your data, you’ll need to do some background work. If you don’t have buy-in from your sales and IT departments or you haven’t determined the data you need to capture, all the cleanup you do will be undone soon enough.
Prepare for your data cleansing process by first doing the following:
- Talk about the importance of clean data with both your sales and IT teams. Explain how clean data helps you identify leads, nurture them until they convert to prospects, and attribute sales back to the right campaign or resource. Other departments will be more inclined to pass you clean data if they understand how crucial it is to both marketing and sales success.
- Partner with your sales team to define the ideal demographic of your prospects. Once you know who you’re targeting, you can create a more efficient process for identifying, scoring, and qualifying leads.
- Choose which information your progressive profiling fields will capture. Now that you know who your ideal prospects are, what information do you need to identify and qualify them? Job title? Industry? Company size? Work with your sales team to choose 4-5 pieces of crucial information to capture.
- Create values for your progressive profiling fields. Note that you can set the value the user sees to be different than what is recorded in your marketing automation software.
- Determine whether your fields will be pick list or free form. Opt for pick list fields as much as possible, as they convert better and provide you with much cleaner data than you’ll get if all your fields are free form. While you can’t go with pick list for name, email, or company, you can use pick list for just about everything else.
Now that you’ve created a strategy and structure for the data you’ll be importing or collecting, it’s time to clean up. If you’re using Marketo, set up a data washing machine (other marketing automation softwares have similar features). This is a series of triggers that monitors and normalizes incoming data to fit within the values you’ve set for your progressive profiling fields. You can use the data washing machine for existing data as well as new incoming data.
If you answer “yes” to one of the following questions, you should set up a data washing machine for the field in question:
- Does this field have a defined set of values within your marketing automation software but not for the prospect? (examples include Company Name, Industry, or Job Title)
- Is this field used as a translator for other fields, such as Number of Employees?
Timing Is Everything
A final note on data cleansing: don’t wait until your data is old to start cleaning it up. The longer you wait, the more of a challenge it will be. It’s best to implement data cleansing as soon as you start working with marketing automation software.