Did you know that on average, U.S. companies are collecting data from customers in more than three different channels? With all that data coming in from different places in different formats, it’s no surprise that 85 percent of U.S. companies believe their bottom line is affected by data issues.
Kicking your data into shape
Our databases are hiding a dirty secret—bad data. In this three-part blog series, we will explore three channels where bad data is collected and share how you can kick your data quality strategy into shape.
The first two stages of email rehab helped you verify that your legacy email lists and the emails you are collecting in real time are valid and deliverable, but your hard work isn't over yet! The final stage of email rehab is to build an ongoing email data quality strategy. One part of that overall strategy includes revamping your reputation. Monitoring your sender reputation is a critical aspect of your email rehab program that should not be overlooked.
In our first blog post in this three-part series, we discussed how you can take the first steps in your email rehab program: admitting you have a problem and cleaning up your database with email validation. The next step in email rehab is to start verifying data at the point of collection.
Marketers love email. In fact, email is the most important marketing channel today. Despite our addiction to email campaigns, many of us are hiding a dirty little secret – poor email hygiene practices.
Admitting you have a problem
In this three-part blog series, we’ll explore the three stages of email rehab that will help kick your dirty email hygiene habits for good. The stages of email rehab include cleaning up your database, gathering good data at the point of collection and developing an ongoing email data quality strategy to keep your list clean.
Predictive Analytics World in Boston has been a great show for the Experian Data Quality team. Data science is an emerging field and is just now hitting its stride. The things that companies can do with predictive modeling are incredible, and can be very valuable if done the right way.
Though I’ve been learning a lot since the event started earlier this week, there are three key things I wanted to share that are important to keep in mind when thinking about predictive analytics: