Before implementing data cleaning, it’s important to look at the big picture, otherwise you may drown in a mess of old, inaccurate contact data. What are your goals and expectations? How do you plan to execute it successfully? As you implement data cleaning, keep the following tips in mind. Continue reading
A crowd of people gathered around a cornerstone of a building in Brooklyn. They could barely contain their excitement that October day in 2014.
A time capsule from 1950 was about to be opened, revealing mysterious contents. No one knew what to expect. Historical treasures? Letters from the past? Maybe even gold bars or valuable collectibles? The crowd strained to hear what officials would announce to the crowd as they opened the container. Continue reading
If I were to ask every email marketing professional his or her top headache-inducing challenge, I’m willing to bet it would have something to do with email deliverability.
In 2014, Marketing Land reported 11% of permission-based email marketing campaigns were blocked and 6% ended up in people’s spam folders. With ISPs moving toward engagement-based filtering, in which specific subscriber signals determine how and where email should be delivered, accessing and analyzing email and subscriber data are essential to help senders improve deliverability. Continue reading
There is only one drawback to data-driven marketing: It requires conclusive, comprehensive data to be effective. While this may seem obvious, some brands continue to make important decisions about their marketing with an inaccurate or incomplete email database. Unfortunately, if they don’t do anything to verify their data (particularly user email addresses), they’ll be unaware of one of the primary reasons their marketing efforts failed. Continue reading
It is necessary for organizations to have an updated database, both for ensuring efficient contact with their customers and maintaining compliance standards. Data Cleansing or data scrubbing is the process of identifying and correcting inaccurate data from a data set. With reference to customer data, data cleansing is the process of maintaining consistent and accurate (clean) customer database through identification & removal of inaccurate (dirty) data. Here, inaccurate data stands for any data that is incorrect, incomplete, out-of-date, or wrongly formatted. Continue reading