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Adaptive marketing using insight driven customer interaction

  • US 7,707,059 B2
  • Filed: 11/22/2002
  • Issued: 04/27/2010
  • Est. Priority Date: 11/22/2002
  • Status: Active Grant
First Claim
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1. A computer-implemented method for performing insight-driven, adaptive marketing activities, comprising:

  • providing a system comprising at least one memory to store instructions, at least one processor having a plurality of software modules, and at least one database embodied on computer-readable medium to store customer data for a plurality of customers,wherein the plurality of software modules comprise a customer data extracting software module, a training software module, a customer selection module, a campaign tracking module, a data individualizing module, and an insight module; and

    wherein the software modules access the customer data stored in the at least one database and the instructions stored in the at least one memory; and

    executing the instructions by the at least one processor to perform;

    extracting the customer data from the at least one database by the customer data extracting software module, the customer data including data fields comprising identification fields for household information and individual information, behavior fields, demographic fields, contact history fields, analytic model score fields, and ownership fields, the analytic model score fields including a household identifier and an individual identifier to identify individuals in a household for cross-referencing the customers to households and for cross-referencing the individuals to the customer data in other data fields;

    training an analytic model with the training software module to predict customer behavior and produce predicted customer behavior data from analysis of the extracted customer data;

    the analytic model predicting behavior of each of the customers, the behavior comprising response by the customers to a plurality of marketing offers via a plurality of interaction channels used to communicate the plurality of marketing offers; and

    the analytic model producing a plurality of scores, each of the scores comprising an indicator of a likelihood of response by a selected customer to a selected marketing offer via a selected interaction channel;

    wherein training comprises;

    choosing a sample set of the customers from the customer data;

    segmenting the sample set of the customers;

    describing the segments with lift values, each of the lift values comprising an indicator of a likelihood of response by a selected customer segment to the selected marketing offer;

    scoring all customers based on segments;

    defining a targeted marketing campaign comprising a targeted marketing offer and a targeted interaction channel;

    building an analytical model; and

    scoring a prospect set with the analytical model, wherein the prospect set comprises all customers in a targeted segment;

    selecting a targeted set of the customers from the at least one database by the customer selection module, the selecting based on a determination, from the scores, of customers most likely to respond to the targeted marketing offer via the targeted interaction channel;

    executing the targeted marketing campaign with the targeted set of the customers, the targeted marketing offer, and the targeted interaction channel to deliver the targeted marketing offer to the targeted set of customers via the targeted interaction channel;

    gathering campaign results data from the targeted marketing campaign by the campaign tracking module, the gathering includingcollecting results data of a count of the customers accepting the targeted marketing offer via the targeted interaction channel, identification information for the customers accepting the targeted marketing offer via the targeted interaction channel, and time statistics related to acceptance of the targeted marketing offer via the targeted interaction channel, wherein the time statistics comprise a length of time between the targeted marketing offer and the acceptance of the targeted marketing offer;

    updating the customer data in the at least one database based on the campaign results data, the updating further comprising individualizing, by the data individualizing module, the campaign results data for the customers associated with the campaign results for associating the campaign results data with the individuals for cross-referencing the individuals to the updated customer data in the data fields;

    obtaining insight from the campaign results data with the insight module by comparing the predicted customer behavior data to actual customer behavior data produced in response to the targeted marketing campaign; and

    repeating the extracting, training, selecting, executing, gathering, updating, and obtaining the insight for at least one additional targeted marketing campaign to deliver an additional targeted marketing offer to an additional targeted set of customers via an additional targeted interaction channel in a closed-loop process to obtain additional insight,wherein insight obtained from each targeted marketing campaign is used to refine and simplify subsequent customer data extraction, analytic model training, and customer selection, andwherein the analytic model is re-trained based on how the customers respond to various types of the marketing offers and to various types of the interaction channels used in each targeted marketing campaign.

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