New Ways To Find Duplicate Entries In Customer Data

If you are in charge of customer data in your company, then it is clear that you may have encountered a duplicate data enemy. Duplicates enter our system by entering personal data, importing from external forums or for customers filling out forms. The results are the same and costly to fix.

This article discusses some of the most advanced types of data duplicates you will find on your CRM website.

Common Names Are Expressed Alternatively
The most common way for duplicate data not to appear on a website. Duplicates are created by common words expressed in a different way.

For example.
Suppose you are trying to find a duplicate and you are using a company name as a primary way to get it. The company may be represented differently in different duplicate customer records.

Image:

Alphabet Incorporated
Alphabet Inc.
As you can see the company name is expressed differently and may be born a duplicate record.

Let's take another example of Career Degrees

Senior Operations Manager
C.O.O.
COO

This is the reason why data configuration is so important. Without it, it would be almost impossible to get duplicates. If you do not have a local setting then it is almost certain that your CRM should be completed with these types of duplicates.
 

Nicknames and Short Names
As we all know most of us are known by many names. Others use short, regular versions of their first name or use original names or go by their nicknames.

For example, if the person's name is John Paul Jones, you can see his name displayed in different ways on various CRM contact records.

Shiv Jones
Siv Jones
Shiv Paul Jones
Shivs Paul John
JP Jones
J.P. Jones

There may be situations where nicknames like Junior, Bud or something like that can be plentiful. The normal recurrence recovery process may therefore fail in this case.

Typing

An interesting fact. The average personal data entry error rate is approximately 1%. This means that for every 100 key buttons there is one typo. You will find typos where people are responsible for entering data. Sadly, if you have a client or client-based form, instead of the default data collection methods, you can be sure you have duplicate data, which was not monitored due to typing.

Common data errors with companies, such as:

Microsoft
Microsift
Or in words, such as:

Jane
James

Data errors occur when uploading data to a large customer website. These errors make it difficult to retrieve duplicate data.

Topics and Suffixes

Title and supplemental contact data also creates multiple duplicate records.

Using the previous example of Shiv Paun Jones, you can have duplicate records such as:

Dr. Shiv Jones
Drs. Shiv Jones
Mr. Shiv Paul James
Shiv Paul James Jr.
Shiv James III
Shiv Paul James Esq.

The title and appendix should be carefully considered when it comes to data quality as it is one of the major sources of duplication.

Website URL

Another common way to get duplicate records is to use a website URL, within CRM.

There may be two customer records and fields may or may not include “http: //” or “www.”, Which will also result in duplicate records. Or in some cases, different customer records may have different specific domains. For example, amazon.com vs. amazon.co.uk
Another reason for duplication is lower domains. For example, a university may have different domain patterns for different departments such as - english.school.edu, math.school.edu, physics.school.edu etc.

All website URLs need to be checked to make sure your site is clear on such issues.

Similarities (Incomprehensible Matches)

Depending upon the size of the yard, only some toys will get fit. There are many variations that most fields may have in order for the exact same function to work properly.

Anonymous matching is a systematic method used to analyze data and identify customer records that have exactly the same characteristics. It works by analyzing the "closeness" of two different data points.

Proximity is measured by the number of changes required to make any two data points the same. It's also known as the Decision stage. The planning range considers the amount of input, subtraction and exchange differences, which are required for the two data points to be exactly the same.

installation: bar → repository
removal: archive → bar
replacement: repository → bark

Without an ambiguous localization method, it can be very difficult to find duplicates on a large website.

Second Check

One of the biggest problems is that duplicate customer records go into the cracks because companies today are willing to identify duplicates using standard fields, without using any second check.

For example, you can identify duplicates by name, surname, and phone number. You can capture most duplicates by matching records with a combination of these fields.

Using a second test when the first one fails can help you find and remove these free-floating free duplicates from the original.

Phone Numbers In Different Formats

Phone numbers were used to identify duplicate accounts and contacts in CRM.

Contact with two duplicate records may have the same phone number for both contacts. Also, organizations do not always change the main line number, so this can serve as a reliable platform for repeating.

But there may be some problems with using phone numbers as a primary source.

First, there are many ways to get a phone number formatted on your website.

123-456-8890
1234568890
123.456.8890
(123) -456-8890
123 456 8890
1-123-456-8890

This means that using a phone number will leave many duplicates hidden on your site.