Contact data is the information that you have about your customers, prospects, leads, and subscribers, such as their names, email addresses, phone numbers, company names, job titles, etc.

Contact data is the foundation of your marketing and sales efforts, as it enables you to communicate, engage, and build relationships with your audience. However, contact data is also prone to decay, errors, and inconsistencies, due to various factors, such as human mistakes, system glitches, data entry variations, or data source changes.

According to a study by MarketingSherpa, contact data decays at an average rate of 2.1% per month, which means that 22.5% of your contact data becomes obsolete or inaccurate every year.

Poor contact data quality can have serious consequences, such as:

• Lower email deliverability and open rates
• Higher email bounce and unsubscribe rates
• Reduced customer satisfaction and retention
• Increased customer acquisition and maintenance costs
• Missed sales and revenue opportunities
• Damaged brand reputation and trust
• Compliance and legal issues, especially with regulations like GDPR and CCPA

That’s why data cleansing, also known as data cleaning or data scrubbing, is an essential process that you need to perform regularly to ensure that your contact data is of high quality and fit for your purposes.

Data cleansing is the process of identifying and fixing errors, duplicates, and irrelevant data from your contact datasets, and making them consistent, accurate, and up-to-date.

Data cleaning can help you:

• Improve the deliverability and performance of your email marketing campaigns
• Enhance the personalization and relevance of your messages and offers
• Increase the conversion and retention rates of your customers and leads
• Reduce the waste and inefficiency of your marketing and sales resources
• Boost the accuracy and reliability of your contact data analysis and insights
• Comply with the data protection and privacy laws and regulations
• Reduce costs for sending marketing campaigns

In this blog post, we will explain how to perform data cleansing for your contact data, and what are the best practices and tools that you can use to make it easier and faster.

How to Perform Data Cleansing for Contact Data

Data cleansing for contact data is not a one-time activity, but a continuous and iterative process that requires planning, execution, and evaluation.

Here are the main steps that you need to follow to perform data cleansing for your contact data:

1. Define your contact data quality criteria and goals:Before you start cleaning your contact data, you need to establish what are the standards and expectations that you want your contact data to meet, and what are the objectives and benefits that you want to achieve from data cleansing. For example, you may want to ensure that your contact data is complete, accurate, consistent, valid, and decide on which data fields are critical to you.
2. Assess your contact data quality:The next step is to assess the current state and quality of your contact data, and identify the sources and types of errors and irrelevant data that exist in your contact datasets.You can use various methods and tools to perform contact data quality assessment, such as data profiling, data auditing, data validation, and data visualisation. You can also use metrics and indicators to measure and quantify the quality of your contact data, such as email deliverability, bounce rate, open rate, click rate, unsubscribe rate, etc.

By the end of this step you should have a list of issues that need to be addressed, for example, wrong job titles, extra symbols in first names, inconsistent phone number formats and so on.

3. Clean your contact data:

The core step of data cleansing is to clean your contact data, which involves finding and fixing errors, duplicates, and irrelevant data from your contact datasets, and making them consistent, accurate, and up-to-date.

It can be a manual or automated process, depending on the size of your database.

You can use various techniques and tools to perform contact data cleaning, such as data editing, data transformation, data standardization, data deduplication, data enrichment, and data verification.

Each issue is likely to use a different method of cleansing. You can also use automation and machine learning to speed up and simplify the contact data cleaning process, and reduce human errors and biases.

4. Evaluate your contact data quality:

The final step of data cleansing is to evaluate the quality of your cleaned contact data, and compare it with your contact data quality criteria and goals. You can use the same methods and tools that you used for contact data quality assessment.

You can also use feedback and testing to verify the quality and usability of your cleaned contact data, and identify any remaining or new issues that need to be addressed.

5. Repeat and maintain your contact data quality:

Data cleansing is not a one-time activity, but a continuous process.

You need to repeat the data cleansing steps periodically, and whenever you acquire new contact data or update existing contact data, to ensure that your contact data quality remains high and consistent.

Best Practices and Tools for Data Cleansing for Contact Data

Data cleansing for contact data can be a challenging and time-consuming task, especially if you have large and complex contact datasets. That’s when we would recommend approaching professionals, like us, for data cleansing, as we have the right tools, access to fresh data and the expertise to do it efficiently.

However, if you prefer to do it yourself, you can make it easier and faster by following some best practices and using some tools that can help you automate and streamline the data cleansing process.

Here are some of the best practices and tools that you can use for data cleansing for contact data:

• Define your contact data quality criteria and goals clearly and realistically, and align them with your marketing and sales needs and objectives.
• Document your contact data sources, structures, formats, and definitions, and keep them updated and consistent.
• Use contact data quality rules and standards to validate and verify your contact data, and enforce contact data quality controls and checks throughout your contact data lifecycle.
• Prioritize and focus on the most critical and impactful contact data quality issues, and use a systematic and structured approach to address them.
• Involve and collaborate with your contact data stakeholders, such as contact data owners, contact data users, contact data analysts, and contact data experts, and get their feedback and input on your contact data quality issues and solutions.
• Review and evaluate your contact data quality results and outcomes, and measure and report your contact data quality performance and improvement.
• Learn and improve from your contact data quality issues and solutions, and implement contact data quality best practices and lessons learned in your future contact data projects.

Tools:

– HubSpot: HubSpot is a CRM and marketing platform that allows you to perform data cleansing for your contact data, as well as data integration, data preparation, data analysis, and data visualization, using a user-friendly and cloud-based interface and a powerful and flexible engine. HubSpot can help you automate and simplify the data cleansing process, and handle various contact data quality issues, such as data errors, duplicates, inconsistencies, and missing values. HubSpot can also help you enrich and enhance your contact data with external data sources, such as social media, web analytics, and email marketing.

– OpenRefine: OpenRefine is a data cleaning tool that allows you to perform data cleansing for your contact data, as well as data exploration, data transformation, and data reconciliation, using a web-based and open-source application and a scripting language. OpenRefine can help you clean and refine your contact data, and deal with various contact data quality issues, such as data errors, duplicates, inconsistencies, and formatting problems. OpenRefine can also help you link and enrich your contact data with external data sources, such as web APIs, databases, and ontologies.

Data cleansing for contact data is an important process that you need to perform regularly to ensure that your contact data is of high quality and fit for your purposes.

We hope you found this blog post useful and informative, and that you learned something new and valuable. If you have any questions or comments, please feel free to reach out. As an online broker, we can help you with your contact data needs and challenges.