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Ever had to take out time from your busy schedule just to do a CRM data cleansing? If you’re a marketing administrator, a CRM manager, or a marketing manager who works on customer data, you know cleaning this data is a time-consuming, and annoying task. But you can’t ignore it because you know no matter how brilliant your strategy is if your customer data is dirty, your efforts will fall flat faster than a soufflé in a thunderstorm.

Dirty data can lead to misdirected marketing, ineffective campaigns, and lost opportunities. Whether it’s outdated contact information, incorrect demographic data, or inaccurate purchase histories. Dirty data is a menace that can cripple even the most brilliant of marketing campaigns.

But not to worry! In this guide, we’ll walk you through the ins and outs of CRM data cleansing – from identifying dirty data to cleaning it up and maintaining its cleanliness. By the end of this guide, you’ll have the knowledge and tools you need to ensure that your customer data is clean and reliable for marketing success.

So grab a cup of coffee, settle in, and let’s get started on the journey to clean, accurate, and effective customer data.

TYPES OF CRM DATA: WHAT TO CLEAN

Your CRM database is likely to contain a wide range of customer data, from basic demographic information to detailed purchase histories and engagement data. It’s important to identify and clean up all relevant data types to ensure that your marketing efforts are based on accurate and up-to-date information.

A big challenge in CRM data cleaning is identifying the type of data to clean. You will likely only clean what you can see as blatant errors, such as wrong spellings, missing dates, or incomplete fields. But bad data is more complex and involves a wide range of data.

When you begin to clean CRM data, you can categorize the data type into six key areas:

Data Type Description Example
Customer Demographics Basic information about customers, such as name, age, gender, location, and occupation John Smith, 32, Male, New York, Accountant
Contact Information Contact details for customers, such as email addresses, phone numbers, and postal addresses john.smith@email.com, 555-1234, 123 Main St., New York, NY
Purchase History Information about customers’ past purchases, including product name, price, date, and quantity iPhone 12, $799, 10/05/2022, 1 unit
Interactions and Engagement Data related to customer interactions and engagement with your business, such as website visits, email opens, and social media activity Visited website on 03/01/2023, clicked on email campaign on 02/28/2023
Sales Data Information about sales, including revenue, deals won/lost, and pipeline activity $100,000 in revenue Q1 2023, lost deal with XYZ company on 02/28/2023
Customer Service Interactions Data related to customer service interactions, including support tickets, chat logs, and call recordings Submitted support ticket on 03/01/2023, spoke with agent on 02/28/2023

As you can see, most of the cleaning marketers or CRM managers do is on the basic demographics, but dirty data can also mean entering the wrong information about a sales deal in the pipeline, leading to an inaccurate revenue amount.

When cleaning CRM data, you will need to assess all the main data keypoints, instead of just fixing basic demographic data. Because CRM data fields are all interconnected, errors in one segment can affect the whole pipeline, leading to inaccurate insights, predictions, and even poor business outcomes.

TYPES OF DIRTY DATA AND HOW TO FIX IT

Dirty data can take many forms, and it’s not always immediately apparent when your CRM database suffers from accuracy issues. However, several signs may indicate the presence of dirty data, including:

Type of Dirty Data Description
Duplicates Multiple records for the same customer, with conflicting or inconsistent information
Incomplete Data Missing or incomplete information for customers or records
Outdated Information Information that is no longer accurate or relevant, such as outdated contact details or purchase history
Formatting Issues Inconsistent formatting, such as variations in spelling or capitalization, or inconsistent date formats
Invalid Data Data that does not conform to expected patterns or values, such as a phone number with letters or a birthdate in the future

Identifying these signs of dirty data is critical for maintaining the accuracy of your CRM database. To do so, you may need to leverage various data-cleaning tools. The reason why we emphasize a data cleaning tool is that these kind of errors are almost impossible to identify and fix using Excel or any other traditional methods. More importantly, if you’re a marketer or a CRM manager, you may need to depend on IT teams to clean this data. We believe this dependency needs to be reduced since business users are the true custodian of the data, so it makes sense for business users to treat and clean their data, keeping in mind the context and purpose of the data.

One such tool that you can easily connect to your HubSpot CRM is WinPure, which is specifically designed to identify and flag dirty data in a variety of formats, including CRM databases. With WinPure, you can easily clean up your data by detecting duplicates, standardizing formats, and identifying invalid data. It also provides advanced options like fuzzy matching, which can help identify records that may be related but have slight variations in their data. You don’t need to code, or ask for IT support to use WinPure because it’s designed to enable business users to get the most from their data.

By using WinPure or similar tools, you can take charge of your CRM data quality, ensure you have clean data to work with every time you kickstart a campaign and reduce your dependency on IT teams, saving you time, effort, and unnecessary conflicts!

CRM DATA CLEANSING TECHNIQUES AND BEST PRACTICES

Now that you’ve identified the types of data to clean and signs of dirty data, it’s time to dive into the best practices and techniques for cleaning up your CRM database.

  • Data Standardization

One of the most critical steps in data cleansing is standardizing your data. This involves establishing a consistent format for data across your entire CRM database, such as ensuring that phone numbers are formatted in the same way or that state names are abbreviated consistently. Standardizing data can help you eliminate duplicates and inconsistencies, making it easier to identify patterns and trends in your data.

Here’s an example of standardizing phone numbers, which are often inconsistent, especially if you do not have adequate data controls when collecting numbers.

Original Phone Number Standardized Phone Number
(123) 456-7890 123-456-7890
123.456.7890 123-456-7890
(111) 222-3333 111-222-3333

By standardizing phone numbers in this way, you can ensure that all phone numbers are stored in a consistent format, making it easier to identify duplicates and patterns in your data.

  • Deduplication

Deduplication is another critical step in data cleansing. This involves identifying and removing duplicate records from your CRM database, which can result from data entry errors, customer updates, or other factors. Removing duplicates can help you avoid sending multiple communications to the same customer and can also reduce confusion when analyzing data. Duplicates usually  occur when you have more than one person handling data entry or when you have the same customer filling their information in more than one way (from the website or from an app). Either ways, removing duplicates is necessary to ensure the accuracy and reliability of your CRM data!

Here’s an example of common duplicate entry issues to look out for:

First Name Last Name Email Phone 
John Doe johndoe@gmail.com  123-456-7890
John Doe john.doe@gmail.com  456-789-1230
Jane Smith jane.smith@yahoo.com  555-555-5555
Jane Doe janedoe@gmail.com  123-456-7890
Bob Smith bsmith@gmail.com  555-555-5555

Notice how one person has multiple email addresses? Chances are one of the email address of John Doe is an error where a “dot” was accidentally added. Moreover, he has two numbers, which could be a work or a personal number. If your web form relies on phone numbers as unique identifiers, you’ve got a duplicate!

CRM data cleansing would require you to identify this duplication, consolidate the information after manually validating the information. In this case, you will have to find out which of the two email address is valid and which of the two phone numbers is in use. These scenarios require your attention as a CRM manager, which is a far more effective use of your time than trying to clean data on Excel. Hence, having a data cleansing solution do the job for you leaves you with ample time to assess the validity of data.

  • Data Enrichment

Data enrichment is the process of adding new, relevant information to your CRM database, such as customer demographics or behavioral data. By enriching your data, you can gain a deeper understanding of your customers, allowing you to create more targeted and effective marketing campaigns.

Data enrichment is often added as a part of data cleansing because it involves adding missing or incomplete data to your database, which can help to fill in gaps in your customer profiles and provide a more complete picture of your audience.

For example, let’s say you have a CRM database with basic customer information, such as names and contact details. By enriching this data with information on customer demographics, such as age, gender, and income level, you can gain valuable insights into your target audience, allowing you to tailor your marketing efforts more effectively.

  • Regular Data Maintenance

Cleaning your CRM database is not a one-time activity. Every day, your CRM is updated with dozens of leads and new information, which means you need to make CRM data cleansing a routine activity. This is also where a solution like WinPure can help you with its automation feature. You simply need to set a date for automated data cleaning!

Let’s say a customer has moved and their address information is no longer accurate. By regularly reviewing your CRM data and updating this information, you can ensure that your communications reach the correct address and that you maintain a positive relationship with the customer.

Watch how you can solve key  CRM data quality challenges with WinPure. 👇

By following these CRM data quality best practices and techniques, you can ensure that your CRM database is accurate, up-to-date, and effective for your marketing efforts.

HOW TO MAINTAIN A CLEAN CRM DATABASE?

We’ve discussed the importance of maintaining the database with regular data cleaning, but how do you go about it? Well, the first step to having a clean CRM database is to limit the influx of poor data. This essentially means you will have to revisit your data collection practice and identify the most common source of error. For example, do you have data quality controls on your lead form to ensure the customer doesn’t have to manually type in data for most of it? Examples of this could be drop-down options for country codes, postal codes, etc to limit manual typing.

Other than identifying errors at the collection point, here are some other techniques you can use to maintain a clean CRM.

Data Validation and Verification

Data validation and verification are critical steps in ensuring that your customer database remains clean. By validating and verifying your data, you can catch errors and inconsistencies in real time and prevent them from entering your database. Here are some best practices for data validation and verification:

  • Use automated data validation tools like WinPure to ensure that data is formatted correctly and is valid.
  • Require mandatory fields (as given in the examples above) so that critical information is not left blank.
  • Implement validation rules for fields like email addresses and phone numbers to ensure that they are in the correct format.
  • Verify customer data against external data sources like government databases or credit bureaus to ensure accuracy.

Regular Data Audits and Updates

Regular data audits and updates are necessary to ensure that your customer database remains clean and up-to-date. Here are some best practices for regular data audits and updates:

  • Conduct regular data audits to identify data inconsistencies, duplicates, and missing information.
  • Update customer data regularly, especially for inactive customers.
  • Use third-party data sources to enrich your customer profiles with up-to-date information.
  • Purge obsolete data that is no longer relevant.

Data Hygiene and Management Practices

Data hygiene and management practices are essential for maintaining clean data over time. Here are some best practices for data hygiene and management:

  • Set up a data management plan that includes clear procedures for data collection, storage, and maintenance.
  • Define roles and responsibilities for team members involved in data management.
  • Use data security measures to protect customer data from breaches or unauthorized access.
  • Train employees on best practices for data management.

By following these strategies for maintaining clean data, you can ensure that your customer database remains accurate and up-to-date, helping you to make more informed decisions about your business strategies and ensure optimal customer satisfaction.

TO CONCLUDE

Data cleansing is an essential process for maintaining clean and accurate data in your CRM system. However, it’s equally important to ensure that the data you collect in the first place is of high quality. By implementing best practices for data collection, you can minimize the amount of dirty data that enters your system, making the data cleansing process easier and more effective.

Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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