how clean data impacts your marketing strategy image.jpg

As a marketer, the start of the new year means setting new
goals and creating new marketing strategies. With a CRM system in place, leads
to nurture, and new customers to acquire, nothing could possibly go wrong
-right? Well, I don’t mean to pop your marketing ninja bubble, but not quite.
Don’t worry, I’m not here to point out the bad and then wish you a happy new
year. I want to point out a flaw that can be fixed to increase your success with marketing strategy.

One of the last things marketers think about is the data
their CRM or other database contains. Our approach and tactic tends to be: come
up with a killer marketing strategy, create killer marketing strategy, and
implement killer marketing strategy. We write the most compelling copy, create
the best designs, hit send, and then pat ourselves on the back as we wait for responses
to flood the CRM gatekeepers.

But, wait. After 24 hours 
we realize the impact wasn’t that high, the response wasn’t as great, and
the amount of emails that bounced back was a bit much. Where did we go wrong?

It’s all in the data. Collecting data as an expert lead
generator is a piece of pie. However, little do we realize that collecting
accurate and clean data isn’t. As a matter of fact, when you completely leave
it up to the lead to input the data you are leaving a lot of room for human error.  And even if the lead inputs the correct data,
life happens and while their address may change, the information in your
database doesn’t. Here are examples of data errors that can be impacting your marketing strategy:

  • Missing area code’s from phone numbers
  • Duplicate records
  • Outdated addresses

Data errors are costly. So costly that the The Data
Warehousing Institute reported the costs of data quality for businesses to be
more than $600 billion every year. Not only is dirty data costing your company a
lot of money, but it is costing your strategy in the following ways:

  • Undelivered mail
  • Low customer retention
  • Poor decisions made off of bad data

To learn more about how to clean-up
your data visit our website!

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