Today, consumers use a wide variety of devices, browsers, and channels during their buying journey. Although the advent of online shopping has opened big opportunities for brands, at the same time, the disparity in customer data has drastically increased. Companies capture, track, and store customer data at various sources. This gives birth to serious data quality issues, including format variations, incorrect patterns, missing information, and record duplication.

In this article, we will look at why unifying mailing lists and linking rows is crucial to enabling customer personalization, and how you can ensure clean, standardized, and matched data.

Why Is Unifying Mailing Lists Important?
A consumer uses about 20 marketing channels when making a buying decision. Most businesses do not have any unifying technology implemented for customer data. This causes problems when they want to understand their customers better – in terms of behavior and preferences. Some common channels used by customers include emails, websites, social media platforms, digital magazines, chatbots, etc. When customer data is generated at multiple ends, there must be some way to consolidate and unify it to get a single, comprehensive view.

Businesses that use clean, matched mailing lists avoid serious complications, such as:

· Hard bounce rate or failed email deliveries, indicating that most email addresses in your mailing list are invalid or do not exist.

· Decreased customer engagement since the brand fails to understand their customer’s preferences and buying intention.

· Failed mail deliveries since customer addresses stored in the database are not standardized or verified, and do not point to a valid mailable location.

· Duplicate marketing efforts incurred due to the presence of multiple records for the same individual can cause you to waste a lot of time and resources.

· Inaccurate business intelligence, which causes business leaders to base critical decisions on inaccurate customer information.

Unifying customer lists is a systematic process that consists of the following steps:

The first step is to get all lists that need to be unified at one place. This can include connecting to different databases, local files, third-party applications, etc. You may have to review and consider the structural differences between databases, as well as the variation in column titles. To fix such issues, you need to import selected columns, rename columns, as well as map columns from one list to another to define which ones contain the same information.

2. Profile lists

When the lists are imported, you need to run data profiling algorithms that will statistically analyze each list and highlight possible data cleansing opportunities. This includes finding out:

· Blank or empty values,

· Incomplete records,

· Incorrect formats and data types,

· Invalid pattern,

· Irrelevant or garbage values, etc.

3. Standardize data values

Now it's time to fix issues that were identified during the data profiling stage, including specifying missing information, transforming patterns, formats, and data types, and more. This step also includes column parsing or merging – you need to divide one column into multiples or merge multiple columns into one. This is done to get more accurate results when you are matching lists to identify records belonging to the same customer. For example, parsing Full Name column into First Name, Middle Name, and Last Name columns. Similarly, you can merge City and Country columns into one.

4. Match rows in lists

In this step, you will match and link records that belong to the same customer. This can be done in two ways depending on whether your lists have uniquely-identifying attributes.

• With unique attributes: Presence of unique attributes such as Social Security Number or an IP Address can help you to perform exact matches on lists and find out which belong to the same customer.
• Without unique attributes: In absence of unique identifiers, you need to perform advanced matching techniques such as fuzzy matching. This process includes selecting a combination of attributes, comparing them across records, and computing the likelihood of them belonging to the same customer.

5. Deduplicate and merge matching results

Once you have identified records that possibly belong to the same individual, now it’s time to deduplicate and merge them to get the golden record. To avoid data loss, you need to merge or overwrite information from duplicate records onto the main one. Once done, you can delete the duplicates and mark the main one for export.

Zara Ziad is a product marketing analyst at Data Ladder with a background in IT. She is passionate about designing a creative content strategy that highlights real-world data hygiene issues faced by many organizations today. She produces content to communicate solutions, tips, and practices that can help businesses to implement and achieve inherent data quality in their business intelligence processes. Her content is targeted towards a wide array of audiences, ranging from technical personnel to end-user, as well as marketing it across various digital platforms.

Click here for an example of the record linkage software mentioned in this article.