List Rental: Modeling Choices (Non Co-Op)

Finding incremental list rental universe that performs at your desired contribution threshold is critical when optimizing new customer acquisition. Marketers renting vertical lists who want to go beyond hotline prospects or turn a mediocre list into a winner have found great success with modeled list selections. Traditional selects focus on the targeted lists’ RFM.  Predictive modeling takes this one step further by taking subjectivity out of the process and focusing on the elements that are associated with increased new buyer rates. 

 There are two main modeling methods that can be applied depending upon the data available.  Clone Modeling and Response Modeling. 

  • Clone modeling seeks to identify characteristics of your customer profile that are different from those who are not yet your buyers on a given list.  This model is great for cases where you are testing a new list or have limited mail history. 
  • Response modeling creates a ranking algorithm based upon the differences between responders and non responders mailed from a targeted list.  This model is stronger because it hones in on those who were actually targeted and either became your customer or did not. 

Here’s how to get started with Clone Modeling:

  • Select a sample of your best customers for data enhancement.  A file size of 20M minimum is recommended.
  • Next, the modeler will compare your best customer profile to an equal size sample from the targeted list who are not your current buyers.
  • The result is a set of customer attributes that are scientifically weighted to compute the probability that a prospect will become your customer based upon the degree of synergy between his/her profile and yours.
  • This formula is used to score the target list which is ranked, top down, based upon how closely the profile resembles your best customer.  The optimal prospects for your campaign are then selected using this model score.
Elisa Berger, Ph.D.

About Elisa Berger, Ph.D.

Elisa Berger, Ph.D., is Principal and President at Cross Country Computer (CCC). Elisa has been successfully helping database marketers achieve their ROI goals for nearly three decades. She earned her Ph.D. in Applied Research at Hofstra University.
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