How to keep the email database clean?
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This presentation talks about how to keep your database clean and why you need to improve email data quality and scrubbing. How to build a high performing database? Steps to know that you have achieved an optimal database. Various tactics for replacing redundant data and how data cleansing helps?
How to keep the email database clean?
- 1. How to keep the email database clean? ,
- 2. , Table of contents What is data cleansing? Keeping your email list healthy (Knowing and tracking) Warning signs you need to improve email data quality and scrubbing How to build a high-performing database? Steps to execute Parsing and correcting Standardization and matching Consolidation How do you know you achieved an optimal database? Tactics for removing dirty data How data cleansing helps? Comprehensive steps for the entire process Conclusion
- 3. ,The many definitions of data cleansing are: Process of removing the errors Identifying incomplete parts of the data Deleting the obsolete data Act of finding the data that do not belong to the specific dataset Helps in the email list management process What is data cleansing
- 4. , Keeping your email list healthy, how do you know? Frequent soft bounces Contact never opens your email Hard bounced email contact Recipients that are inactive
- 5. , What does list hygiene keep track of? Finding the invalid addresses Removing the addresses with typos Deleting the emails from all the bounces- soft and hard Updating the valid addresses Dummy values Multipurpose fields Lack of unique identifiers Data in the contradictory form
- 6. , Warning signs to improve your data quality: Industry average open email rate- 21.33% Industry average conversion rate- 3% Industry average click-through rate- 2.62% Industry average ROI- 122%
- 7. , Scrubbing your email list It wont transfer the bad contacts Reputation would be intact Only paying for the active subscribers Warmup process would be quicker
- 8. , How to build high-performing database: Collecting email addresses from all the best means Validating the data while it is collected Not sending emails to addresses that have spammed you Segmenting the subscribers based on demographics and behavior Segmenting the inactive users and bringing them on the same page as you Replacing the dead email addresses
- 9. , Steps to execute: Parsing the data Correcting the data Standardization Matching Consolidation
- 10. , Parsing the data: The process scraps the data from the emails. It locates the different elements in the source files to isolate in the target files For example: All the data is entered into the individual fields, name, location, city. Correcting the data: It is the verification of the data whether the data is entered into the relevant fields For example: The city name in the city field or the firm name in the firm field.
- 11. , Standardization: The process follows transforming the data into its standard business format. For example: It follows the rule where all the fields are included in a specific order. Matching: Step followed to match records across the database to eliminate redundancy
- 12. , Consolidation: It finds the relationship between the entire merged and the compared records It is consolidated in a single presentation
- 13. , How do you know you have achieved an optimal database? Validity Consistency Accuracy Uniformity Completeness
- 14. , Tactics for removing dirty data: Developing the data quality plan Validating the data accuracy Standardizing the contact data at the entry point Identifying the duplicates Appending the data
- 15. , How does data cleansing help? It helps improve the customer segmentation It improves the email deliverability Accelerates the customer acquisition process Streamlines the business practices in the long-run Target customers in an efficient way Avoid the compliance issues with GDPR Increase the overall ROI Removing errors means happier employees
- 16. , Comprehensive steps for the entire process: Removing the irrelevant data Taking care of the outliers Standardizing the data Validating the data Checking structural errors Flagging the missing data
- 17. , Conclusion: Data cleansing is required to maintain the efficiency of the database. There are various steps that could help you cleanse the same. Understand the best methods, practices, and each of the techniques in this presentation.
- 18. , InfoClutch is a leading suppilier of most sought after segmented global mailing database. We offer fully customizable prospect data of your preferred specification. 940 Amboy Avenue, Suite 104, Edison, NJ 08837, US. /InfoClutch /InfoClutch /InfoClutchData /company/infoclutch