Skip to content
A person wearing a yellow glove holding a squeegee and wiping away a section of bubbles that are in the shape of a cloud. Today, most marketers store their customer data on a cloud-based software, which is why this image was selected for this blog by Meltwater on how to clean your cloud-based data.

How to Use Data Cleansing & Data Enrichment to Improve Your CRM

TJ Kiely

Feb 24, 2022

There are a lot of discussions today around making data-driven decisions; however, what happens when you’re basing your decisions on data that are inaccurate, irrelevant, or redundant? That's something that management teams aren't discussing enough. To get the most reliable insights, data cleansing and data enrichment should be prioritized just as much as data collection.

Organizations have no shortage of information about their customers. This has become both a blessing and a curse, as companies need to trust the data they have and know how to use it. Both are easier said than done.

Collecting data isn’t the endgame for agencies or businesses. Rather, the next step is to focus on what that data can do to improve your company’s performance. Here’s why data cleansing and enrichment matter and how you can deploy both to improve your CRM.

Table of Contents:

What Is Data Cleansing?

Dusting a laptop.

Before we dive into the specifics, let’s start with some basic definitions. First, what is data cleansing?

Data cleansing is like the Marie Kondo method for your B2B data. Instead of letting bad data accumulate, you proactively clean up missing or inaccurate records, eliminate redundancies, and get rid of data that doesn’t matter. When you’re done, you’re left with organized customer records and can more easily find important information.

What Is Data Enrichment?

Data enrichment (also called data appending or data augmentation) is the vehicle used for data cleansing. These are often third-party data tools, software, or processes that refine and enhance your first-party data. When applied to CRM use cases, data enrichment tools may look for missing records and look to external sources to fill in the gaps. For example, if the address and phone number data fields of a company are blank, data enrichment tools can import them using external signals.

Both cleansing and enrichment seek to enhance your data so that you are able to better leverage it.

How Are Data Cleansing and Enrichment Different?

Data cleansing and data enrichment both fall under the data augmentation umbrella. The main difference is their end goals.

With data cleansing, you’re focusing on cleaning up the data you already have. For instance, if you have two entries for the same customer, you could combine those entries for a single customer file. Cleansing will remove duplicate or inaccurate records and leave accurate data alone.

With enrichment, your goal is to make your customer records as complete as possible. Knowing as much about your target audience as possible is key in making business-related decisions. The more you know about a customer, the better you can tailor your approach to cross-sell to them and create customer loyalty.

So, if you’re missing key information about customers in your CRM, such as contact information, enriched data helps to fill the gaps. From there, you can improve your marketing to current customers and prospects through hyper-targeted offers.

Why Are Data Cleansing and Enrichment Important?

Data-driven marketing, lead generation, and the total customer experience all hinge on clean data. When raw data is incorrect, your marketing campaigns can’t do their best work.

The benefits of accurate customer data are seemingly infinite:

  • Improve profiling to send relevant offers to the right prospects
  • Verify you’re sending direct mail, etc. to the right person and address
  • Reduce wastage created by marketing to nonexistent emails, addresses, etc.
  • Enrich existing data with additional information to learn more about your customers
  • Gain more control over the entire B2B customer journey
  • Make Big Data a more valuable asset

With today’s data augmentation deep learning technology, you can cleanse and enrich your data with very little involvement on your part. That’s a huge plus as your CRM grows, as you don’t have to worry about manually updating records when customer data changes.

Because there's not much to do on your end, why wouldn't you make data management a top priority?

Tip: Think about your customer data management (CDM) to gain more actionable insights from your data.

How to Do Data Cleansing

Deleting files on a computer.

If data enhancement is on your radar, we suggest starting with data cleansing. There’s no need to enrich data that are irrelevant or inaccurate.

Let’s break down the basics of data cleaning:

Step 1: Remove Duplicate and Irrelevant Entries

It’s not the size of your database that matters, but rather the quality of data inside. That’s why it’s useless to keep duplicate or irrelevant data that can muddy your insights.

Duplicate entries usually happen because of data collection processes. When you import or scrub data from third parties or combine datasets from multiple departments, you risk creating overlaps. The deduplication process consolidates records to minimize distractions and keep only important information.

Also during this step, you can discard any data that is irrelevant to your database. For instance, if you want to create a database to target just your New York-area customers, you wouldn’t want customers in Boston or Miami showing up.

When you’re done with Step 1, you should have a neat and tidy database showing only relevant entries.

Step 2: Fix Any Structural Errors

When combining data sets, fields don’t always perfectly transfer. Different data sets may have unique naming conventions or styles that look different than the data they’re being combined with. As a result, you might end up with some fields in all caps or have items with misspellings or misplacements.

Fixing any structural errors makes your dirty data look cleaner. All information becomes consistent and follows a standard format. This reduces the chance that some pieces of data may fall under the wrong category or otherwise not be properly used.

Step 3: Standardize Your Data Cleaning Process

Cleaning your data should be an ongoing process, not a one-and-done activity. Errors can occur at any time, so routine data cleaning can ensure the most accurate and up-to-date data set.

However, you can also reduce the numbers of errors you find by standardizing your data processes. Set rules about how users should input data, as well as the type of data that needs to be collected. It helps increase accuracy and your confidence in your data.

How to Do Data Enrichment

Stacking blocks.

Cleaning your company data gives you a strong foundation. Now it’s time to build it out with enrichment. Here’s how to turn incomplete records into quality data:

Step 1: Evaluate Your Data

Data enhancement has three parts: what you know, what you don’t know, and what you need to know. After cleansing, you should have a better idea of what data you have. From there, you can decide what else you really need to complete an ideal customer profile.

The key here is to be selective. That can be tricky because selective isn’t always complete. You might have tons of fields that you could fill in. But it’s best to focus on just the most relevant information. Too much data causes distractions and makes it hard to focus on what matters.

Segmentation can be a helpful exercise when evaluating your data. Depending on the purpose of your dataset, you can decide what else you need to know that you don't already have.

Step 2: Deploy a Data Enhancement Tool

So, you found that you’re missing some phone numbers for some of your customers. Do you email each one to ask for their digits? Of course not!

A data enhancement tool can do a lot of the heavy lifting for you. Tools can import data from trusted third parties in real-time. They fill in the missing fields you care about without you having to type it in manually.

Step 3: Rinse and Repeat

Data doesn’t necessarily have an expiration date, but it does age quickly. That’s why data enrichment (and cleansing) should be treated as ongoing processes.

People move. They change jobs. They get new email addresses. They may even switch to a completely different line of work! Staying on top of these changes as they happen allows you to stay connected and continue creating opportunities with those connections.

Even with standardization, to master data management should be an active priority, not a one-and-done activity. Errors can occur at any time, so routine validation can ensure the most accurate and up-to-date data set. 

How Can Data Cleansing and Enrichment Enhance Your CRM?

Organizing data.

Data makes up a big part of the solution to better marketing. You need reliable and complete customer data you can trust because every touchpoint depends on it. Your mailing lists ensure your direct mail makes it to the right person and place. Your sales team can call the right phone number the first time.

Last but not least, you can more easily get inside the mind of the consumer and drive your marketing campaigns forward.

Data augmentation deep learning tools are making data cleaning and enrichment easier, faster, and more feasible for marketing experts. By using machine learning and automation, enriching and cleaning data becomes a continuous cycle you don’t have to think twice about.

Better data quality improves everything from data analytics to the customer experience. Plus, when you use machine learning tools, you can avoid outsourcing to data enhancement services and keep all of your data in-house.