When most people think of clean data, they usually think of getting rid of duplicates in their CRM or Marketing Automation System. Although getting rid of duplicates in your database is an important part of data management, it is only the first step.
Data normalization is also a key part of data management that can help improve data cleansing, lead routing, segmentation, and other data quality processes.
So what does it mean to normalize your data?
Well, normalization is the process of restructuring a relational database in accordance with a series of “normal” forms to improve data integrity.
In simpler terms, normalization makes sure that all of your data looks and reads the same way across all records.
Normalization will standardize fields including company names, contact names, URLs, address information (streets, states and cities), phone numbers and job titles. Every company has different criteria when it comes to normalizing their data. What one company considers “normal” might not be “normal” for another.
Common Normalization Examples:
- 123456789 → 123–456–789: Prevent misdials and make dialing easier.
- VP Sales → Vice President of Sales: Titles will conform to match other title variations to allow marketing segmentation.
- RingLead → RingLead, Inc.: Helps reduce duplicates if matching requirements include company name.
- htttp://www.ringlead.com/home.html → www.ringlead.com: Helps reduce duplicates if matching requirements include website address. Also improves ABM requirements to link Leads to Accounts.
- 200 Broadhollow Rd → 200 Broadhollow Road: Helps reduce duplicates if matching requirements include address.
- STEVE → Steve: Improves email deliverability
- John Smith Sr. → John Smith Senior: Helps reduce duplicates if matching requirements include name.
Now that you know what normalization is, here’s 3 reasons why it’s important to normalize your data:
1. Reduce Duplicate Data:
One of the biggest impacts of normalizing your data is reducing the number of duplicates in your database. Normalizing your data before matching and merging duplicates will make it easier to find the duplicates if you don’t use a deduplication tool, like RingLead Cleanse, that does it automatically. RingLead Cleanse uses over 40+ custom matching logic rules to help find, merge, and normalize all dupes in your database in real-time.
2. Marketing Segmentation:
Anotherbenefit of normalizing your data is that it will help your marketing team segment leads, particularly with job titles. Job titles vary greatly among companies and industries, making it nearly impossible to associate a given job title with anything really actionable for segmentation or lead scoring. So standardizing this value can be very useful, and a number of approaches are possible.
For example, you can use a lookup list approach in a recent engagement. Job title is typically a combination of department/role (engineering, manufacturing, sales, finance) and level (like VP, manager, technician, analyst, associate). Depending on what is meaningful to the customer’s business process, you can implement a system that translates open-text job titles into job levels using a lookup list.
3. Performance And Metrics:
Databases that aren’t standardized and poorly maintained can cause major headaches when it comes to analyzing the data. By standardizing your data, and using a single organizational method with proper capitalization, your data will be significantly easier to sort through. Not to mention, your sales and marketing teams will save valuable time as they won’t have to spend time sorting the data. Translating crazy data into a standardized list gives you the ability to take actions that otherwise would be difficult or impossible to do properly.