What is Data Cleansing and Clean Data?

To set your marketing campaigns up for success, clean data is essential. From the free-form field a customer uses to submit his/her country of residence (e.g., “US,” “U.S.,” “USA,” “U.S.A,” or “United States of America) to his email address, every piece of data collected must be consistent and accurate. After all, how can you reach your target audience if your database is marked with incorrect contacts or outdated information? Every part of a customer’s contact data can substantially improve incremental marketing ROI which is why data cleansing (or data scrubbing) is imperative. Here’s how to identify dirty data and keep your database squeaky clean.

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Identifying Dirty Data

While it’s virtually impossible to have a perfectly accurate database, it’s important to recognize the following red flags:

Duplicate Data

When building out your database, duplicate data is almost inevitable and often occurs due to improper data merging, repeated submissions, or user error. Look at both lead records, determine which one has the most admissible data (i.e., email address or phone number), then merge the two. Obviously, manually de-duplicating lead records proves time consuming (especially when uploading lists) so check that your Marketing Automation Platform and/or CRM are equipped with automation rules.

Irregularly Formatted Leads

Like the country of residence example, if customer data isn’t consistent, you’re guaranteed dirty data problems. To maintain uniformity, your platform should automatically clean similar data values. That way, if you want to email all of your leads in the United States, you won’t exclude any because of a toxic database.

Business Rule Violations

When data falls outside a certain range, it constitutes a business rule violation. For instance, if a value is supposed to be in the range 0-99 but somehow ended up being 105, it means you have bad data and you need to determine the source. Likewise, if an expiry date comes before an effective date, your database has dirty data.

Inaccurate Data

It is possible that a data value can be correct, but not accurate. For example, you could have a bad data merge between two forms, one featuring city and zip code, the other featuring multiple states (e.g., Albany, Georgia and Albany, New York). Georgia is correct in the context of city and state but not when accompanied by zip code.

Junk Records

Junk contacts are anonymous leads that use fake addresses (i.e.,“blah@gmail.com”) to avoid sharing their real email addresses. These can pollute your database and increase your bounce rate, so it is important to identify records with fake sources and then delete or suspend these contacts. You can also work with a trusted Data Management Provider to help identify bad emails and replace them with validated B2B email addresses.

Incomplete Data

Data with missing values is the main type of incomplete data.

Data Cleansing & Savvy Marketing

You know what dirty data looks like. Now it’s time for a good data scrubbing! Data cleansing is the actual process of locating data that is incorrect, incomplete, improperly formatted, or duplicated and fixing or removing it. It’s a critical tool if you want to improve marketing conversion rates and campaign performance. By implementing regular data cleansing and preventing bad addresses or duplicate data from entering your database, you’ll be able to effectively target your audience and track your campaign’s success.

Article From: social123.com

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