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Does the following scenario sound familiar?

“We have a database of over 90k contacts, some of which have been in our CRM for a decade. We need a source that can help us identify which are out of business, have moved physical addresses, as well as helping us append mailing addresses and missing phone numbers.”

This was an initial inquiry we received from one of our customers who were looking for a solution to tackle their outdated data. If this is a problem affecting your organization, we’re here to help.

What is outdated CRM Data & Why is it a Problem? 

As the years go by, your CRM collects thousands of records, and over time, these records become obsolete or outdated. As stated by this customer, the contacts are either out of business or have moved addresses, which means, the data the company holds is no longer usable. Unless this data is sorted, the company will continue to struggle with inaccurate insights and poor analytics.

Some of the most common setbacks caused by outdated data include:

❌ Reduced Sales Efficiency: Sales teams rely on accurate data for targeting and outreach. Outdated information can lead to wasted efforts, such as contacting businesses that have closed or individuals who have changed roles.

❌Poor Customer Experience: Inaccurate data can result in sending irrelevant offers or information to customers, which can be frustrating for them and harm your brand’s reputation.

❌Ineffective Marketing Campaigns: Marketing strategies depend on data-driven insights. Outdated data can lead to misguided campaigns that don’t resonate with the target audience, wasting both time and resources.

❌Compliance Risks: Keeping outdated or incorrect customer data can lead to compliance issues, especially with regulations like GDPR that require accurate data storage and handling.

❌Increased Operational Costs: Continually maintaining and cleaning outdated data requires time and resources, adding to operational costs. This is especially problematic for businesses that don’t realize their data is outdated and continue to invest in its maintenance.

Outdated CRM data can have a cascading effect on various aspects of a business, from sales and marketing to compliance and operational efficiency.

A structured five-step approach to updating crm data

Navigating nearly 100K records is a tough call for sure. But, a structured approach can turn this relatively massive task into achievable milestones.

Below, we give a five-step approach that we use to help our customers update their CRM data without affecting their efficiency, or productivity.

Step 1: Breaking Down Your Main Goal into Micro-Components

Cleaning data effectively requires segmenting your overarching objective into manageable tasks. For instance, if your aim is to eliminate contacts from companies no longer in operation, your initial action should be a manual review of your list to establish verification criteria. You might cross-reference company names on your list with public records to assess their current operational status. After confirming these details, you can appropriately flag these contacts for deletion or archival.

Likewise, for other objectives like verifying existing data or appending missing mailing addresses, the key is to distill these broad goals into specific, actionable tasks.

Step 2: Profiling and Identifying the Types of Errors in the Data

Before diving into any data cleaning tasks, it’s crucial to pinpoint the specific errors affecting your data quality. Common issues often involve typos and inconsistent letter casing, but more elusive errors can also occur. These might include numerical characters in a name field, like “John 3 Smith,” or text in a zip code field, such as “ZIP33929.”

Detecting these subtle errors manually can be challenging, which is where WinPure’s data profiling feature proves invaluable. Using a pre-built library, this tool provides statistical insights into the types of errors present in your data. For example, the tool will show you that 40% of your columns have odd or non-printable characters. Once you have this insight, you can then make a strategic decision on how you want to clean the data.

Step 3: Context-Focused Data Cleaning

As easy as it sounds, data cleaning isn’t about making sure your data is perfect. It’s more about ensuring your data is purposeful and meets the required context.

For example, a data analyst might consider the “Customer Lifetime Value (CLV)” as unnecessary when matching or cleaning their data, especially if you’re not directly involved in marketing strategies. However, for marketing teams, this column is crucial as it helps them identify high-value customers and tailor marketing campaigns accordingly. If the CLV data is inaccurate, then marketing teams cannot identify high-value customers to provide them with custom experiences.

Therefore, context matters. When data cleaning is done without addressing context, it can lead to more serious challenges causing conflict between teams and departments.

Step 4: Deduplicating Data & Creating Consolidated Records

A common concern among our clients is the prevalence of duplicate records in their CRM databases. While it may seem straightforward to simply run a matching algorithm to eliminate duplicates, the process is more nuanced for it to be truly effective. Two key prerequisites must be met:

✅ Standardized Data: Consistency in data formats is crucial. For instance, all date columns should follow a uniform format, such as DD/MM/YY, rather than a mix like DD/MM/YYYY.

✅ Clean Data: The quality of your matching process is only as good as the data itself. Erroneous elements like odd characters, punctuation, and typos can compromise the outcome.

Once these conditions are satisfied, the next step is data deduplication, where the goal is to identify and consolidate duplicate records into a single “master” record. The criteria for selecting this master record should align with your business objectives.

For instance, if the aim is to maximize revenue, the master record should be the one with the highest total spend. This ensures that you retain the most valuable customer data, thereby informing and enhancing future marketing or sales strategies.

Step 5: Validation and Quality Assurance

After deduplicating and consolidating records, the next critical step is to validate the cleaned data to ensure its accuracy and relevance. This involves:

Test Runs: Perform test queries or use the data in simulated business scenarios to verify its integrity and usefulness. This will help identify any overlooked issues or inconsistencies.

Documentation: Keep a detailed record of all the cleaning and deduplication steps undertaken, including the criteria used for selecting master records. This serves as a reference for future data management tasks and audits.

Backup: Always create a backup of the cleaned data. This ensures that you have a reliable version to revert to in case of any errors or issues in the future.

This final step closes the loop on the data cleaning process, setting a strong foundation for data-driven decision-making.

How did this customer use winPure to match & deduplicate their CRM data?

The customer was able to identify nearly 56% of their records were missing key information such as valid phone numbers and email addresses. Out of 90K records, around 13K records were also duplicated.

Watch this video to see how we solved the client’s problems using dummy data provided by them.

WinPure not only cleans your data but also profiles and deduplicates it using advanced matching algorithms. Plus, it offers automated scheduling features to keep your data clean regularly.

Want to know more? Feel free to reach out to us!

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, data deduplication, and MDM.

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