CRM Data Cleansing: Everything You Should Know

Integrating CRM software is a standard process in managing customer profiles for small and large businesses alike.

However, business processes can intersect, email campaigns can overlap, and the customer lifecycle continues to change, leading to an ongoing process of changed data, inconsistent formatting, wrong contact details, and invalid email addresses.

These hiccups within your customer database can lead to errors within your information database, requiring CRM Data cleansing.

This guide highlights the cause of these errors, the definition of data cleansing, and the best approaches and tools to handle the situation.

What Causes Errors in Your CRM Data

Your CRM Data will require cleansing over time because of the accumulation of errors. These errors could result from faulty or inaccurate information inserted through the leads on your web forms.

Other error causes include, but aren’t limited to:

  • Making multiple calls through the same leads, as the sales team will make duplicate records.
  • Inputting the correct data using an incorrect format.
  • The marketing team may add new contacts to your CRM without completing their information.
  • The contacts already saved within the CRM database may get new phone numbers, email addresses, titles…etc.
  • Companies merging, rebranding, or relocating.
  • Writing typos during manual data entry.

Importance of Data Cleansing

Chart showing Data Cleansing procedures

The cleansing process isn’t an easy one. Going through all those duplicate contacts, incorrect email addresses, mailing lists, human errors, and thousands of customers’ profiles is nothing short of a hassle.

As such, most businesses undergo the CRM cleansing process only when they’re installing a CRM for the first time, or when they’re migrating their data to a new CRM.

Unfortunately, this approach can undermine the sales process. Incorrect or outdated information can lead to:

  • Inaccurate forecasts and analysis.
  • Sluggish movement of prospects through the sales funnel.
  • Hindered visibility of previous interactions and purchase history.
  • Impaired audience segmentations and marketing campaigns.
  • Wasted time, since the sales reps work overtime navigating through incorrect or outdated information.

Customer Relationship Management software is only as effective as the data they’re fed with.

As little as 10% of incorrect/inaccurate information in your database can lead to warning signs like missed targets, sluggish customer service, and duplicate records. If you spot these, you should cleanse your CRM data.

The 4 Steps to Cleanse Your Data

The expressions “cleansing” and “formatting” are sometimes mistakenly used interchangeably, but they’re far from being the same thing.

Formatting is wiping out data, while cleansing is identifying faulty data within the sea of information and addressing them.

To do that, follow these steps:

1. Address Duplicate Entries

According to HubSpot, data duplication percentage can reach up to 30% of stored data. That’s 30% wasted potential sales within your CRM database.

Duplicate data arises from many reasons, like entering a second email address for the same user, utilizing multiple formats, and the variety of postal addresses used by the same customer.

Unfortunately, your CRM won’t recognize these discrepancies and place them under the umbrella of the same customer. To your CRM, these are different entries.

Manually going through your entire CRM database is a waste of time and resources. What you can do is use de-duplicator tools like Dedupley, or data validation tools like Experian Data Quality.

2. Address Missing Data

SaaS tools are yet to find a method to completely eliminate missing data.

That’s because not every customer interaction will yield full data, and sales reps will often enter the contact record in the system expecting to receive the missing data later but forget or lose track of the contact.

You can, however, minimize missing data instances by:

  • Sorting entries with missing values together until they are updated or deleted. This typically involves flagging the data as missing to differentiate them from the list.
  • Assigning tasks to input the missing information, either by reaching out to the customers or using other information from datasets. Sometimes a customer may have complete data spread over duplicate entries. Unifying such data is solving two errors in one go.

3. Update Outdated Information

updating outdated data

According to a study by Vainu, 30% of CRM data will be outdated every year, even if the entries themselves are correct.

Customers change their phone numbers, update their email addresses, or change their job titles at any given time, rendering their old information outdated.

It can be difficult to manually track your customers to keep their data fresh, but you can automate the process by using parsing tools.

You can utilize rule-based parsing tools, like Mailparser and Zapier Email Parser. They’re fairly affordable and extract specific information from emails. However, they may struggle with complex email formats or unexpected variations in language.

If your company is on a larger scale, or you often get complex email formats, AI parsers, like Parsio and Nanonets, can be better options.

These tools leverage machine learning algorithms to understand the context and meaning of emails, enabling them to extract information more accurately and handle diverse email formats.

4. Fix Structural Errors

Structural errors are among the easiest mistakes to make and the most frustrating to find. Inconsistent abbreviations, different capitalizations and punctuations, and even extra spaces can lead to duplicates or incorrect entries.

You need to apply a consistent format company-wide to avoid the occurrence of such errors. For example, a rule should be implied that the phrase “not applicable” should only be written as N/A or vice versa.

How to Maintain Your CRM Data

It’s true that you’ll periodically need to update your CRM data. However, maintaining your existing data as long as possible is a critical step in increasing the intervals between CRM cleansing frequencies.

Maintaining your data involves minimizing the mistakes that lead to data cleansing in the first place. This saves time, money, and effort, as all your efforts in CRM cleansing will be in vain if you don’t fix the error causes.

Your primary aim should be standardizing your data entry practice. Here are some pointers to help:

  • Standardize the uppercase and lowercase values.
  • Standardize the units of measurement and numerical data use.
  • Teach the employees how to check for duplicates before creating a new contact record.
  • If your business utilizes multiple applications, you need to sync the data between them to reduce manual data entry and the chance of human error. If that’s not possible, have the same data entry employees operate on both applications.

Note: making a schedule for CRM data cleansing can be a remarkable approach. That way, you don’t have to wait for warning signs in your database after your sales have already been hurt.

5 Helpful CRM Data Cleansing Tools

Here are some tools to help you cleanse your CRM data:

1. DemandTools by Validity

DemandTools is a cloud-based data quality solution specifically designed for Salesforce CRM. It offers features like deduplication, email verification, data enrichment, and real-time processing.

Pro

  • Deep integration with Salesforce: Works seamlessly within your existing CRM environment.
  • Multifaceted cleaning: Handles various data issues like duplicates, invalid emails, and missing information.

Cons

  • Limited platform support: Primarily focused on Salesforce, not ideal for other CRM systems.

2. Melissa Clean Suite

Mellissa Clean is a comprehensive data cleansing software compatible with various CRM and ERP platforms like Salesforce, Dynamics, and Oracle. It provides address verification, email validation, phone standardization, and more.

Pros

  • Broad platform compatibility: Works with multiple CRMs and offers tailored solutions for each.
  • High accuracy: Boasts industry-leading accuracy for data verification and standardization.

Cons

  • Pricing complexity: Can be expensive for smaller businesses with tiered pricing based on features and data volume.

3. Winpure Clean & Match

Winpure is an affordable data cleansing tool offering deduplication, data standardization, and matching across various data sources, including spreadsheets, databases, and CRMs.

Pros

  • User-friendly interface: Easy to use for non-technical users with a visual interface and guided workflows.
  • Cost-effective: More affordable option compared to some enterprise-level tools.

Cons

  • Limited data enrichment features: Primarily focuses on data cleaning and matching, with less emphasis on data enhancement like other tools.

4. Trifacta Wrangler

Trifacta (currently under the umbrella of Alteryx Designer Cloud) is a powerful data-wrangling tool with visual data transformation capabilities for cleaning, shaping, and enriching CRM data.

Pros

  • Flexibility and customization: Offers advanced features for complex data manipulation and transformation.
  • Self-service platform: Enables business users to clean data without relying on IT expertise.

Cons

  • Steeper learning curve: The advanced features may require some technical knowledge or training.

5. OpenRefine (Previously Known as Google Refine)

OpenRefine or Google Refine is an open-source data cleansing tool offering basic data cleaning and transformation capabilities through a user-friendly interface.

Pros

  • Free and open-source: No licensing fees, ideal for budget-conscious users or small teams.
  • Community support: Benefits from a large and active user community for help and resource sharing.

Cons

  • Limited features compared to paid tools: Lacks some advanced functionalities found in paid data cleansing solutions.

In Closing

Accurate contact information is paramount to get the best out of your CRM database and improve your customer service interactions.

To do so, you must identify the errors and their causes, and eliminate them consistently. This ensures high-quality data and unlocks countless opportunities within your business.