Intelligent sales and marketing recommendation system

• US 20060229932A1
• Filed: 03/07/2006
• Published: 10/12/2006
• Est. Priority Date: 04/06/2005
• Status: Abandoned Application

• Current Assignee: Johnson & Johnson Services Incorporated (Johnson & Johnson)
• Original Assignee: Johnson & Johnson Services Incorporated (Johnson & Johnson)
• Inventors: Stavrakas, Spyros, Shaya, Steven A., Zollo, Stephen A.
• Application Number:
  US11/370,526
• Publication Number:
  US 20060229932A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Systems and methods for generating intelligent promotional recommendations and reports are disclosed. The systems and methods utilize longitudinal patient or physician level data and longitudinal data regarding sales and marketing techniques and approaches employed to train intelligent processing elements such as collaborative filters, neural networks, and combinations thereof. Intelligent recommendations are made and data is compiled regarding the recommendations implemented and physician and patient activities after the recommendations are implemented. Ongoing data is used as feedback to re-train the processing elements and refine the sales and marketing techniques and approaches employed.

Claims

1. A system for generating intelligent promotional recommendations for a product, comprising:

a) a database containing longitudinal data related to non-promotional activity with respect to a product and longitudinal data related to one of sales activities for the product and marketing activities for the product;

b) a recommendation engine, operatively connected to the database, comprising means for generating, in response to a request relating to a target, one of an intelligent sales recommendation and an intelligent marketing recommendation; and

c) a user interface, operatively connected to the recommendation engine, for generating the request.

2. The system of claim 1 wherein the means for generating intelligent recommendations comprises one of a collaborative filter, a neural network, and a content-based filter.

3. The system of claim 1 wherein the product comprises a pharmaceutical product, and the longitudinal data related to non-promotional activity with respect to the product comprises one of longitudinal patient data and electronic medical record data.

4. The system of claim 3 wherein the target comprises one of a physician, a group of physicians, a managed care provider, and a benefits provider.

5. The system of claim 4 wherein the recommendation generated by the means for generating increases one of a likelihood that a prescription will be written for the product by the target, a likelihood that a prescription written for the product by the target will be filled by a patient, and a likelihood that a prescription written for the product by the target will be refilled by a patient.

6. The system of claim 1 further comprising means for re-training the recommendation engine with longitudinal feedback regarding ongoing non-promotional activities with respect to the product and ongoing sales and marketing activities for the product.

7. The system of claim 3 wherein the user interface comprises a personal digital assistant.

8. The system of claim 1 wherein the database includes subjective longitudinal data.

9. The system of claim 8 wherein the subjective longitudinal data comprises one of impressions of the product, impressions of the sales and marketing activities for the product, and impressions of a manufacturer of the product.

10. A method for generating an intelligent promotional recommendation for a product, comprising:

a) receiving a request to generate a promotional recommendation for a target in view of a product;

b) determining attributes of the target in view of the product based on data about the product, data about the target, and longitudinal data related to activity with respect to the product by a population of persons related to the target;

c) classifying the target relative to the population of persons based on the attributes;

d) determining, based on the classification of the target and the longitudinal data related to activity with respect to the product by the population of persons related to the target, a likelihood that each of a plurality of promotional techniques will result in the product being purchased when each of the techniques is used with the target; and

e) selecting the promotional technique having a defined likelihood of resulting in the product being purchased, the selected technique comprising the intelligent promotional recommendation for the product.

11. The method of claim 10 wherein the classifying step comprises one of placing the target in a neighborhood of similar targets within the population of persons and selecting a neural network equation incorporating connection weights modeling a relationship between the target and the product.

12. The method of claim 11 wherein the product comprises a pharmaceutical product, and the target comprises one of a physician, a group of physicians, a managed care provider, and a benefits provider.

13. The method of claim 10 wherein the product comprises a pharmaceutical product, and the target comprises one of a physician, a group of physicians, a managed care provider, and a benefits provider.

14. The method of claim 13 wherein the longitudinal data comprises one of longitudinal patient data and electronic medical record data.

15. The method of claim 10 wherein the longitudinal data related to activity with respect to the product includes longitudinal data related to activity by persons who purchase the product.

16. The method of claim 10 wherein step e) comprises selecting each of the promotional techniques having defined likelihood of resulting in the product being purchased above a defined number, the selected promotional techniques comprising the intelligent promotional recommendation for the product.

17. The method of claim 10 wherein the longitudinal data related to the population of persons comprises subjective longitudinal data.

18. The method of claim 17 wherein the subjective longitudinal data comprises one of impressions of the product, impressions of sales activities for the product, impressions of marketing activities for the product, and impressions of a manufacturer of the product.

19. The method of claim 10 further comprising generating reports based on results from one of steps b), c), d), and e).

20. A method for updating an intelligent promotional recommendation system having a recommendation processing element, comprising:

a) generating a first set of promotional recommendations for a target in view of a pharmaceutical product based on longitudinal data related to activity by the target with respect to the pharmaceutical product;

b) compiling additional longitudinal data related to activity by the target with respect to the pharmaceutical product that is created after the first set of recommendations are implemented; and

c) re-training the processing element to incorporate the additional longitudinal data.