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Database Cleansing Can Improve Your Business Model

written by Editor, 20 August 2015

Data cleansing

Having an efficient way to update and manage your clients in your business database is directly aligned with your overall strategy. Your current customers and prospects are the basis for your organization’s success and whom you’ll be engaging with via telephone, email, social media, and even in person.

The individuals you correspond with on a daily basis, whether they are customers or prospects, are just as important as your mission. Being able to communicate to them consistently can help increase your sales results in a matter of months. Database cleansing can help this in many ways.

When working on a campaign a contact list is shared amongst the communication teams and this list is used for lead qualification and appointment setting, however cleansing the list is a crucial aspect. Ensuring you find the right contact information for the decision makers and influencers to insert in your database is critical. This must be up to date and in line with the current criteria within your existing records.

Both new and old data need regular cleansing but where do you start??

What are the main aims for cleansing data?
To remove duplicate records
To update blank record fields
To ensure the data is correct and up to date
To make certain that when contacting your prospects you are doing so in an effective manner

 

Data cleansing

Why do we need to cleanse the data?
Clean and accurate data is essential for effective sales, marketing and customer management strategies. It’s an economical method of making your data useable again and delivers a range of benefits, all of which improve your profitability.

It makes it easier to segment email lists, ensuring that the right contact gets the right information. By using more targeted messages it is more likely to generate higher engagement and support for your organization. You don’t want to be sending the same message to everyone you need to make it easy to create lists of different personas.

If communications are sent out at random with no tracking of the activity history from the recipient this can be seen as a bad move from the organisation. This encores wasted effort and costs of marketing to contacts with incorrect details.

In addition, cleansing your data will help you to analyze your records more accurately. For instance, you will know the real number of contacts and perhaps how they are geographically distributed, rather than the distorted figures that can be derived from analyzing a corrupted database.

How can you effectively cleanse the data?
Rebuilding missing data is extremely important. Wherever possible, missing information needs to be recreated. Regular analysis of data is the most effective method to do so. Using sites such as Duedil and Company Check is useful to confirm an address or director details or Wikipedia’s Post Code List to confirm the correct county if this cannot be located. This process can be done quickly and is hassle free.

Ensure that your contact’s first name, last name, email address, mobile phone number etc. are all in their respective columns. This is done to create more standardised data.

Discovering duplicates
Finding duplicates can be a fairly easy task for someone who knows about the SQL database language. It is more difficult to find similar records that really are the same person, but are not listed in exactly the same way in your database. For instance the following two records may actually be the same person:

ID First name Surname Address1  Address2 Postcode COUNTY

3442  John   Citizen   PO Box 33  Frankston  B44 5DF AVON

682  Jonathon  Citien   14 Beach Road FRANKSTON B44 5DF AVON

With finding records such as the above the database administrator must take action by doing a “Fuzzy” Matching process. Software is available to find such records and much more experienced SQL programmers could write software to find such possible duplicates.

Because you can’t confidently use logic to determine whether or not two records are the same in the case given above, fuzzy matching would leave the data as is, but produce an exception report, highlighting likely duplicate records, you can then use the previously mentioned sites to cross reference the information.

Top Tips!
Document everything for future reference. Create a central location for your database manual and information log, including every change or update made, a list of definitions for each field and anything else from which you think future database users would benefit.

Cleaning up your data needs to be a recurring activity to ensure that you’re actively engaging and nurturing the right people as well as eliminating those who are no longer going to support your business. You can also generate reports on different metrics of your database to monitor its growth.


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