Spring is in the air and it’s time for some spring cleaning. How often do your scrub your contact lists? How often do you check for duplicates and merge them? If you are like most people, not often enough.
That’s not surprising. We all get caught up in our day to day work schedules so data cleansing is not high on the priority list. But it should be. Bad data costs money – in efficiency, lost revenue, wasted marketing expenditures, and a variety of other ways.
And you are not alone if you are one of the groups that is not regularly cleansing your data. According to one study, an average organization estimates that 22% of their contact data is inaccurate in some way. Wow, that’s a lot. If you are counting on improved marketing using your contact data, that means that possibly 22% of your effort is down the drain. And these numbers are low estimates – other studies I found said the numbers were even higher.
So, what can you do? There are several ways you can clean up your data.
The email campaign idea is a very good one. The same study that talked about how much data is inaccurate also said that 67% of the companies surveyed had problems delivering email. With so many organizations using emails as a way to market their business, emails must be accurate or you are spinning your wheels and spending your marketing dollars the wrong way.
Gartner says bad data costs over $14.2 million a year. You could lose money on paper mailings to bad addresses, losing unhappy customers because product didn’t reach the proper destination or was delayed, using up valuable computing storage space with redundant data, incorrect marketing segmentation, etc.
It behooves you to spend time making sure your data is correct. Don’t spend time coming up with a marketing strategy until you are sure that you are marketing to the right name, email and address. Time spent now means money saved/made later.
Sources:
https://econsultancy.com/blog/64612-the-cost-of-bad-data-stats/
https://www.gartner.com/doc/2636315/state-data-quality-current-practices
http://effectualsystems.com/high-cost-bad-data/
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