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The Flatiron Media Modeling Database uses selected enhancement data and Flatiron Media house variables as predictors to create the model. By identifying your best customers with their unique characteristics, and matching them to Flatiron Media customers, you can significantly increase response.
The Flatiron Media Masterfile consists of EveryDay Winner, My Daily Moment, and ILoveSamples. Sweeps-driven, these subscribers have answered up to 149 questions in order to receive newsletters in an array of categories such as lifestyle, entertainment, health, self-assessment and more. Subscribers also opt-in to receive recipes, and most enter sweeps to win cash!
Avg Age: 60
HHI: 50,000
Married: 50%
TYPES OF MODELS:
GOOD CUSTOMER MATCH -
Mailer provides a 100,000 name sampling of their best customers to Flatiron's service bureau. The Good Customer Match combines everything known about each individual on a mailer's customer file to create a model that identifies more of those best customers on the Flatiron file. The Model is built using the mailer's best customers and utilizes the data available on the Flatiron Media masterfile to create a custom regression model.
REGRESSION MODEL -
A full regression model requires mailing a quantity of Flatiron names that is significant enough to produce at least 1,000 net paid responses. The data elements of the names selected are saved at the time the order was selected. When the mailing is complete, the responses and net mailed names are returned to us to match against the gross order file. A custom regression model is created using the matched responses and non-responses.
When the model build is completed, a report with the Gains Table, Model variables, and Data Profile will be issued and sent to the broker/mailer. All models are created by Data Axle Analytical Services.
An order for the model build must be placed with Data Axle before the modeling can begin. A second order is needed to select the names that will be mailed.
Models are usually completed 4-5 weeks from receipt of data. |
