Spring Cleaning: 5 Ways to Scrub Your Data and Keep it Clean

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The days are longer, the weather is warmer, and spring is in the air! The time has come to dust our bookshelves and tidy up our inboxes, but brands should think of adding data cleansing to that list.

Despite not needing a sponge for the task, cleaning dirty data is still quite the undertaking. Some brands may be tempted to overlook the importance of clean data. However, that would be detrimental to any brand hoping to fully leverage their data for a killer personalization strategy.

Why is Clean Data Important?

Clean data ensures clean analytics. If your data is unreliable, any conclusions deduced 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.

Dirty data can also negatively impact deliverability, churn and unsubscribes rates. Personalization efforts may backfire and have the opposite results when the data fueling your campaigns is inaccurate. 

Gartner estimates poor data quality costs businesses an average of $12.9M annually. To get the most out of your data and analytics, data should be as accurate as possible.

Where Does Dirty Data Come From?

Dirty data is often the result of human error, scraping data, unifying 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 muddy 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?

1. Remove Duplicate and Invalid Profiles

Duplicate profiles are a common occurrence. They are 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. 

2. 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 and prevent inactive users from skewing data. Remove them if re-engagement fails.

3. Fix Structural Errors, Then Standardize Conventions

These errors likely occur after unifying databases and can include typos, strange naming conventions, inconsistent capitalization, and irregular punctuation, subsequently resulting in multiple segments denoting the same category, i.e. “millennials” and “Mlnls.” Implement standardized naming conventions and consolidate these inconsistencies.

4. Address Incomplete, Missing or Inaccurate Data

Input data manually only if it can accurately be deduced from accompanying information, such as age/generation if you have their complete birthdate. Remove data that is incomplete or that doesn’t make sense in users profiles. 

5. Enrich profiles with accurate data

Use self-reported zero and first party data collected directly from the consumer to enhance user profiles and create robust segmentation.

What Can Brands Do To Keep Data Clean?

1. Develop Consistent Data Hygiene

Dirty data naturally accumulates over time. Implement cleaning routines and stick to them to maintain data cleanliness.

2. Consider Implementing Double Opt-Ins

Having the final opt-in be in an email, allows you to confirm the email address is accurate while signaling sender reliability to Internet Service Providers, thus avoiding being marked as spam.

3. Standardize Naming Conventions Across Your Organization

Make sure each employee knows and understands your naming conventions to avoid more inconsistencies.

4. Avoid Third-Party Data

Third-party data is based on observations; therefore, the data is inferred from limited knowledge and is often inaccurate. This type of data can easily muddy customer profiles.

5. Collect Accurate Data

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 data and user profiles without worry.

Get Cleaning!

While cleaning your data may have seemed like a daunting task at first, this breakdown should demystify the process so you can confidently take your data hygiene into your own hands. The results are well worth the effort.

Learn about the squeakiest clean data you can get, zero-party data, in our Zero Party Data Guide or contact Marigold Engage+ here!

Download the ZPD Guide

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