Your marketing department runs on data. It’s like fuel for your demand generation engine. And the cleaner your fuel is, the more efficient your engine runs. Dirty data destroys your engine’s efficiency, reducing the impact of your campaigns—or in the worst cases, causes harm. Dirty data causes poor results when segmenting databases, and cripples support for dynamic content.
According to Judith Kincaid, author of Customer Relationship Management: Getting It Right!, contact data deteriorates at a rate of up to 30% per year. The longer incorrect records remain in your database, the more expensive it is to deal with them. (Consider the 1-10-100 rule: where Sirius Decisions says it takes $1 to verify a record as it is entered in the database, $10 to cleanse and de-dupe it, and $100 if nothing is done, as the ramifications of mistakes are felt over and over again by sales & marketing.)
How dirty are marketers’ databases? Well, Sirius Decisions research suggests 10 to 25 percent of B2B marketing databases contain critical errors (that’s 1 in 4!). These errors range from as simple as incorrect demographic data to vital as inaccurate information about current buying cycle status. Sending to an incorrect address is embarrassing and expensive, but having the incorrect name, marital status or (heaven forbid) accurate record of life or death could be relationship-destroying.
The road to repairing and maintaining your data is not easy, but ask a marketer currently walking that road and they will tell you it’s well worth the effort. Those who get it right are marketers who have shifted focus from one-off projects and have established policies and processes that maintain the quality of your data over time.
We recently worked with one such marketing team, and helped them create a ‘Contact Washing Machine’. For those who don’t know, this is an automated data cleanliness program that standardizes and normalizes data within the company’s marketing automation platform.
The problem:
The client realized non-standard free text fields and blanks had made it incredibly difficult to segment or prioritize their Prospect and Lead database. They needed a solution that could standardize field values to increase the clarity of reporting and ease of segmentation for all the users of their database.
The process:
1. Assess:
a. Identify all fields commonly used for segmentation and reporting.
b. Standardize a list of values that data in each field should adhere to.
2. Automate:
a. Use tools like Excel or GoogleRefine to segment non-standardized data to identify common incorrect values.
b. Create Rules-based program logic to find and replace incorrect values with standard values.
3. Adjust:
a. For non explicit matches, add exception rules to catch all non-blank values.
b. Program and test these rules on test data for QA purposes.
4. Audit:
a. Review program results and repeat steps 1-2 until all data is standardized.
b. Perform quarterly and yearly reviews. Asses if existing normalizations are still relevant or require updating.
The results:
Below is a snapshot of the impact the data cleanliness program had on the standardization of the client’s data points.
It also had the added benefit of making it much simpler for the sales team to calculate a lead score. Based on these normalized values they were then also able to more accurately show the quality of the leads they handed to sales.
With results like these, it’s easy to see the value of implementing a data quality program with its boosts in efficiency, improved campaign results and improved marketer morale.
How clean is your data? Could your organization benefit from a similar program?
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