THE HIGH COST OF BAD DATA

Poor data quality costs U.S. businesses more than $600 billion each year according to estimates by The Data Warehousing Institute.
"The cost of bad data for an individual business can be put at 15% to 20% of revenues." (the Insurance Data Management Association)
The single most important element in a direct mail campaign is the list. It can account for 60% to 70% of the campaign’s success. (John Coe, The Fundamentals of Business-to-Business Sales and Marketing)
The United State Post Office reports:
"Individual address data has an information quality decay rate of 17% per year in the U.S."
Nearly 25% of all mail pieces have delinquent or inaccurate addresses
Actual data errors in a typical customer base may range from 25% to 30%. (Larry English, Improving Data Warehouse and Business Information Quality)
Poor data quality is the most common reason why organizations do not realize the benefits of their data platform investments including:
- Customer relationship management (CRM) systems
- Business intelligence systems
- Data warehouses and financial systems

(SOURCE: Nancy Rybeck, author of “The Bane of CRM—Data Quality” in DM Direct)

Errors in B2B marketing data cost data customers in many ways:

Real Dollars

Assume your company has 10 telesales representatives generating an average of $250,000 each in sales annually or $2,500,000 total. If bad data reduces their productivity by just 10%, then your firm’s opportunity cost is $250,000 a year.
A 20% non-deliverable rate on a 30,000 piece mailing costing $2.00 per piece will waste $12,000……….more than the cost of the list iftself.

Wasted Time

A sales person playing phone tag with a contact for days discovers that the manager’s title differs from the one on the prospect list. Their time was wasted pursuing the wrong person.

Damage to company's image

Misspelled company or contacts in a direct mail piece damages the sender’s credibility and image, whether from a bulk mailing, or worse in a personal introductory letter from a sales executive.

Motivation of their sales staff

Providing sales staff with inaccurate data reduces their motivation and productivity. This costs far more than the price of any data.

Typical Example of Data

Errors can be hiding in seemingly clean data:

An audited comparison of Platinum Data’s records compared to a prominent mega compiler’s records reveal that Platinum Data had
77% more valid sales contacts per establishment.