web analytics

Dirty Data Increases Shipping Costs

Danger Dirty Data SignFedEx, UPS and the United States Postal Service (USPS) all have raised their rates. We have been analyzing the impact of how we have shipped in the past with how we plan on shipping now and  in the future. We crunch the numbers every-which-way and often we are left with a gut feeling ‘there is something more we can do…”

When was the last time you did any type of data analysis and clean-up?

It is possible that inaccurate addresses, old addresses, duplicate addresses and even the contact name associated with the address may be wrong. Simply dirty-data equals wasted dollars. Dirty-data equals undeliverable mail, increased operational costs, low customer retention, and faulty data analysis. What you thought of as a sure-fire marketing campaign does not give you the projected ROI!

Cleaning your database will directly result in better response rates, allowing you to focus on and nurture quality prospects for long-term customer relationships and it saves you money on direct mailing and shipping costs. It also can has a direct impact with speeding up postal processing and deliveries.

Dirty data is inaccurate, incomplete or erroneous data, especially in a computer system or database.

In reference to databases, this is data that contain errors. Unclean data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.

wikipedia.org

Dirty Data Costs Money

Consider the “1-10-100 Rule,” the costs associated with bad data and how it can rob your company.

  • $1 to verify data accuracy up-front
  • $10 to clean data (batch programming/correction solution)
  • $100 per record — when you ignore the problem:
    • Undeliverable Shipments
    • Poor Customer Service
    • Failed Direct Sales and Marketing Initiatives

Primary Contact Information

Today’s primary contact data boils down to (there may be multiple addresses, emails and phone numbers per record requiring clean-up):

  • Full Name
  • Mailing Address
  • Email Address
  • Phone Number

Here are some thoughts on what to look for when cleaning you your database.

Contact Name

Standardize how you store the contact name by maintaining first, middle and last name fields, to include title and suffix fields.

  • Is the name spelled correctly
  • Are hyphens placed correctly
  • Consider adding a gender field to assist in warmer correspondence
  • Use a list of standardized names to match against (corrected data can lead to identifying duplicate records)
  • Add data fields to help you learn more about your client

Mailing Address

A scrubbed address is also good customer service.

  • Make sure Zip Code™ is correct
  • Verify Address still belongs to the Contact Name
  • Verify address against an address verification database

Email

Domain names can quickly get blacklisted when an organization ignores email verification solutions.

  • Front-end email validation tools
  • Opt-in tools
  • Use an email change-of-address solution to identify and update email addresses that are incorrect or have bounced back.

Phone Number(s)

  • Verify the phone number
  • Make sure you have the area code

A recent Gartner Study found that over 40% of failed business initiatives was a result of bad data. Doing continuous due-diligence on your data will result in keeping your overhead down and quality business transactions.