DataLab USA Featured in the Magazine DMNews for its Educational Article on Moving from Vertical Lists to Broad Market

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DataLab USA (http://www.datalabusa.com), one of the nation's foremost analytics and database marketing agencies is featured in the magazine ‘DMNews’ for its educational article on moving from vertical lists to broad market. The following article, written by Alex Aigner, EVP of business development, was published in the March 15, 2010 edition for the annual Essential Database Guide.

DataLab USA (http://www.datalabusa.com), one of the nation's foremost analytics and database marketing agencies is featured in the magazine ‘DMNews’ for its educational article on moving from vertical lists to broad market. The following article, written by Alex Aigner, EVP of business development, was published in the March 15, 2010 edition for the annual Essential Database Guide.

With the average cost of a vertical list around $125 per thousand, marketers can cut their data cost almost in half by switching to a national demographic file supplemented by a predictive model. Vertical lists by default are specialized groupings of individuals rather than mass millions. Utilizing one data source with the thousands of attributes it contains eliminates the time it takes to manage several lists, as well as controls the cost of your merge/purge efforts.

With a potential universe of approximately 200 million households, a successful broad market campaign must have an effective predictive model to identify the households that will meet the marketer’s response, sales, and ultimately profitability requirements. The unique challenge is the direct mail campaign experience (based on vertical lists) is very dissimilar to the population that the model will be deployed on (using a national file). Observed trends and relationships that are present in the vertically restricted training population and drive the discrimination of the model very well may not be present in the demographically diverse population at large. Without controlling the inherent biases between the training and application populations, such models will provide unpredictable results that will not meet expectations.

Segmentation of the broad market universe in regards to the level of similarity to the vertical list experience is an essential feature of a successful broad market rollout. Segments that carry a very similar feature set to that of the past vertical list experience will exhibit the highest levels of accuracy when a traditional response or sales model is applied. Such a model is developed in a similar fashion to a profile model - a representative sample of the broad market population is pulled and serves as the modeling domain, all variables in the compiled list serve as the independent variables, and all records in the sample that exist in the mail experience are flagged and serve as the dependent variable for this exercise. When the model is applied to a campaign, the marketer should also include a random population which will aid in the future expansion of the marketable universe.

About DataLab USA:
DataLab USA, LLC is an award-winning data analysis, database warehousing and database marketing agency. For over 30 years, we have provided Fortune 500 companies in the financial, retail, education, insurance, and non-profit industries our expertise in database strategy marketing. By providing our clients with customized solutions to their marketing challenges, we are able to maximize efforts and increase return on investment. Our philosophy is unusual in the industry today, as there is no attempt to “black box” our technology. We partner and compete in the database marketing space with Accudata, Acxiom, Alliance Data, InfoUSA, Epsilon, Equifax, Experian, Knowledgebase, Merkle, and other database marketing companies. DataLab USA, LLC is a privately held, company with headquarters just outside of Washington, DC in Germantown, Maryland. For more information, contact DataLab USA at 1-800-972-1430 or visit http://www.datalabusa.com.

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Alex Aigner
DataLab USA
1-800-972-1430
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