TARGETED CUSTOMER OFFERS BASED ON PREDICTIVE ANALYTICS

• US 20100057548A1
• Filed: 08/24/2009
• Published: 03/04/2010
• Est. Priority Date: 08/27/2008
• Status: Abandoned Application

• Current Assignee: Amplero Inc.
• Original Assignee: Globy's Incorporated
• Inventors: Edwards, Duane S.
• Application Number:
  US12/546,449
• Publication Number:
  US 20100057548A1
• Time in Patent Office: Days
• US Class Current: 705/14.130
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0211 Determining the effectiveness of discounts or incentives

Abstract

Embodiments are directed towards enabling product and/or service providers to maximize sales of products, services, and content to their existing customers. In one embodiment, a process, apparatus, and system are directed towards optimizing a selection of offers for any customer touch-point to ensure the provider delivers the best offer to the right customer at the most appropriate time. Offers are optimized not only according to a customer'"'"'s interests and preferences but also according to revenue and profitability potential using predictive analytics.

Claims

1. A network device, comprising:

a transceiver to send and receive data over a network; and

a processor that is operative to perform actions, comprising;

receiving a request for an offer for a telecommunications product or service to be presented to a customer of a carrier service;

receiving information about a plurality of available offers, including at least one channel constraint on at least one of the available offers, or a predicted revenue for each available offer;

eliminating at least one available offer in the plurality of offers based on information about the customer;

determining a probability of acceptance of each remaining offer using predictive analytics to perform comparisons based at least in part on a customer attribute or a context of an offer;

determining scores for each of the remaining offers by employing a revenue and profitability maximization mechanism based in part on the probability of acceptance, customer context information, and the received information about each remaining offer; and

in response to the request, providing to the carrier service an offer having a highest score as being an optimal offer for the customer for a given channel in which the optimal offer is to be presented to the customer.

2. The network device of claim 1, wherein the predictive analytics is selected from one of a statistical regression model, decision tree, neural network, Bayesian classifier, graphical model, or survival model, pattern recognition.

3. The network device of claim 1, wherein determining scores further comprises employing at least one penalty for a given channel for each of the remaining offers in determining the scores for each of the remaining offers.

4. The network device of claim 1, wherein determining a probability of acceptance further comprises based in part on at least one customer attribute associated with a purchase history of the customer or a channel in which the best offer is to be presented to the customer.

5. The network device of claim 1, wherein the customer is assigned to a predictive model based on at least one of a random selection among different predictive models, or based on a characteristic of the customer.

6. The network device of claim 1, wherein a channel penalty is employed in determining scores for each of the remaining offers, wherein the channel penalty is configured as a channel specific time-based penalty that reflects at least a timing or frequency for which the provided optimal offer is to be presented to the customer.

7. A processor readable storage medium that includes data and instructions, wherein the execution of the instructions on a computing device by enabling actions, comprising:

receiving a request for an offer for a product or service to be presented to a customer of a merchant;

receiving information about a plurality of available offers, including at least one channel constraint, and a predicted revenue for each available offer;

determining a probability of acceptance of each offer using an analytical model to perform comparisons based at least in part on a customer attribute or a context of an offering;

employing the analytical model to determine scores for each of the offers by employing a revenue or profitability maximization mechanism based in part on the probability of acceptance, customer context information, and the received information about each remaining offer; and

in response to the request, providing an optimal offer to the merchant, wherein the optimal offer is that offer having a highest score, wherein the optimal offer is presented by the merchant to the customer using at least one channel that includes a display on a computer device or a physical paper presentation.

8. The processor readable storage medium of claim 7, wherein the analytical model is selected from one of a statistical regression model, decision tree, neural network, Bayesian classifier, graphical model, or survival model, pattern recognition.

9. The processor readable storage medium of claim 7, wherein determining scores further comprises employing at least one penalty for a given channel for each of the remaining offers in determining the scores for each of the remaining offers.

10. The processor readable storage medium of claim 7, wherein a channel penalty is employed in determining scores for each of the offers, wherein the channel penalty is configured as a channel specific time-based penalty that reflects at least a timing or frequency for which the provided optimal offer is to be presented to the customer.

11. The processor readable storage medium of claim 7, wherein determining scores for each of the offers further comprises eliminating at least one offer for which it is determined that the customer is ineligible.

12. The processor readable storage medium of claim 7, wherein the scores are further determined based on maximizing, for the merchant, a purchase likelihood by the customer for the product or service and further maximizing, for the merchant, a financial impact or benefit.

13. The processor readable storage medium of claim 7, wherein the analytical model further comprises selecting the analytical model based on a classification of the customer, wherein the customer is classification based on one of a random classification, or based on a characteristic of the customer.

14. A system for managing offers over a network, comprising:

a network device employed by a carrier service and configured to provide at least one product or service offer to a customer through at least one or a plurality of different channels, and to further perform actions, including

determining information about the customer, including a context for the customer, and an identifier of the customer;

sending a request for an optimal offer to be presented to the customer based on the customer identifier, context for the customer, and information about at least a subset of the plurality of different channels; and

another network device employed as a customer intelligence platform server that is configured to perform actions, including;

receiving the request for the optimal offer;

receiving information about a plurality of offers, including at least one channel constraint, and a predicted revenue for each offer;

determining a probability of acceptance of each offer using a model selected from at least one of a predictive or non-predictive model to perform comparisons based at least in part on a customer attribute or a context of an offering;

employing the selected model to determine scores for each of the offers by employing a revenue and profitability maximization mechanism based in part on the probability of acceptance, customer context information, and the received information about each remaining offer; and

providing the optimal offer to the network device, wherein the optimal offer is that offer having a highest score.

15. The system of claim 14, wherein the context for the customer comprises at least one of a location of the customer, a time of day, or a channel used by the customer to receive the offer.

16. The system of claim 14, wherein a channel specific time based penalty is employed to determine a frequency in which the optimal offer is to be presented to the customer for a given channel, wherein at least one channel is selected from one of a telephone conversation with the customer, a physical paper presentation to the customer, or a display on a screen of a client computer device.

17. The system of claim 14, wherein the customer is assigned to a model based on at least one of a random selection among different models, or based on a characteristic of the customer, the assigned model being the model selected to perform the comparisons.

18. The system of claim 14, wherein the optimal score is further determined based on maximizing the probability of acceptance while maximizing a financial impact or benefit to the carrier service.

19. The system of claim 14, wherein determining scores further comprises employing at least one penalty for a given channel for each of the offers in determining the scores for each of the offers.

20. The system of claim 14, wherein the probability of acceptance is determined based on a customer attribute that includes at least a purchasing history of the customer, and a channel history of the customer.