How Clean is Your Data?
April 27, 2023
Similar to precious metals, data has to be refined from its raw state to be anything more than a collection of numbers and email addresses. This process is known as data cleaning and is vital to your entire organization’s efficiency and bottom line. When operating with data that is unified and reliable, businesses benefit from better decision-making and better outcomes.
But bad data can do the opposite. Gartner estimates poor data quality costs businesses an average of $12.9M annually
The Downstream Impact of Bad Data
Dirty data is incomplete, outdated, and contains errors. In addition to lost revenue and diminished customer satisfaction, bad data can cripple an IT department and negatively impact your sales teams. Data scientists spend 60% of their time on cleaning and organizing data. A study from Experian Marketing found that dirty data is responsible for:
- Lost productivity from IT, lead gen, and sales departments
- Inaccurate targeting and personalization strategies
- Wasted communications and marketing spend
- Lost resources due to wrong decisions
- Lower customer satisfaction and higher unsubscribe rates
- Higher email bounce, spam, and unsubscribe rates can lower your business’s sender reputation with internet providers like Google, Yahoo, and Microsoft
According to data scientists, it takes $1 to verify records, $10 to clean it, and $100 if you do nothing. Clean data ensures clean analytics. If your data is unreliable, any conclusions from A/B testing, click-through rates, or open rates may be misleading or completely incorrect and should not be used to inform business decisions.
Where Does Dirty Data Come From?
Dirty data is often the result of human error, scraping data, or combining data from multiple databases and third-party data. Brands collecting lots of data from multiple sources are likely to have dirty data, especially if those sources contain third-party data. Third-party data can be inaccurate and alter customer profiling data.
Brands leveraging an omnichannel strategy are also susceptible to having dirty data as they are likely to unify data from multiple sources, which opens the possibilities for duplicates and profile inconsistencies.
How Can Data Be Cleaned?
Remove Duplicate and Invalid Profiles
Duplicate profiles are common and are usually a symptom of merging databases, unifying data and human error. Invalid email addresses can be typos, spam traps, or non-existent accounts. Remove these to have clearer analytics.
Identify Inactive Subscribers for Re-engagement
Instead of removing inactive subscribers, you can segment them out. This way you can create a retargeting campaign for these users to re-engage them, and segment them out of other campaigns. If the user does not re-engage, then the name should be removed from your database.
Fix Inconsistencies in Naming Conventions
Structural errors are likely to occur after unifying databases and can include typos, strange naming conventions, inconsistent capitalization, and irregular punctuation. For example, your teams should have one standardized way of denoting abbreviations like “senior” versus “snr.”
What Can Brands Do To Keep Data Clean?
Develop Consistent Data Reviews
Dirty data naturally accumulates over time. Factor in budgeting and staffing resources to make cleaning routines part of your yearly processes.
Use Double Opt-Ins to Verify Users
Double opt-ins allow you to confirm the email address is accurate while signaling sender reliability to Internet Service Providers, thus avoiding being marked as spam.
Standardize Naming Conventions Across Your Organization
Make sure each employee knows and understands your naming conventions to avoid more inconsistencies.
Build a First and Zero-Party Data Collection Strategy
The most accurate data comes directly from the consumer. First and zero-party data is self-reported, straightforward, and accurate, allowing you to enrich your user profiles without worry. Third-party data is based on observations. This type of data can muddy customer profiles because it is often inferred and consequently, inaccurate.
Quality data is the foundation for building a customer data platform and a successful automation strategy. It’s a fact of life that email addresses, cell phone numbers, and mailing addresses change, along with preferences and purchasing behaviors. Similar to devoting a weekend to spring cleaning your home, or closing a store for a day to do inventory, data cleaning is an essential part of running a business. With clean data, your organization becomes responsive and agile. There’s less waste of both time and resources. And more importantly, clean data leads to better outcomes that improve KPIs and customer relationships.
Learn more about how a zero-party data strategy can help improve your long-term growth and customer retention strategy.
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