On-Site and In-Store Content Personalization and Optimization

• US 20140297363A1
• Filed: 03/26/2014
• Published: 10/02/2014
• Est. Priority Date: 03/26/2013
• Status: Abandoned Application

• Current Assignee: Staples, Inc.
• Original Assignee: Staples, Inc.
• Inventors: Vemana, Pratabkumar
• Application Number:
  US14/226,618
• Publication Number:
  US 20140297363A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

In an example implementation, a method receives first-party data and third-party data and generates a customer profile for the customer of a merchant based on the first-party data and the third-party data. The customer profile has a set of attributes. The method further generates a set of rules for evaluating a disposition of the customer based on the set of attributes of the customer profile, receives real-time intelligence data associated with the customer during a visit to the merchant or an interaction with marketing content of the merchant, predicts the disposition of the customer using the real-time intelligence data and one or more of the rules, and adapts a shopping experience of the customer during the visit to the merchant or a marketing content of the merchant for the customer using the predicted disposition.

Claims

1. A computer-implemented method comprising:

receiving, using one or more computing devices, first-party data and third-party data;

generating, using the one or more computing devices, a customer profile for the customer of a merchant based on the first-party data and the third-party data, the customer profile having a set of attributes;

generating, using the one or more computing devices, a set of rules for evaluating a disposition of the customer based on the set of attributes of the customer profile;

receiving, using the one or more computing devices, real-time intelligence data associated with the customer during a visit to the merchant or an interaction with marketing content of the merchant; and

predicting, using the one or more computing devices, a disposition of the customer using the real-time intelligence data and one or more of the rules; and

adapting, using the one or more computing devices, a shopping experience of the customer during the visit to the merchant or a marketing content of the merchant for the customer using the predicted disposition.

2. The computer-implemented method of claim 1, wherein

predicting the disposition of the customer includes analyzing the real-time intelligence data using the one or more of the rules to determine one or more updates to the shopping experience or the marketing content, and

adapting the shopping experience of the customer or the marketing content includes personalizing the shopping experience or the marketing content using the one or more updates to improve a customer conversion rate associated with the customer during the visit or a future visit to the merchant.

3. The computer implemented method of claim 1, further comprising:

determining an effectiveness of predicting the disposition of the customer based on whether the customer purchased an associated product;

updating the customer profile to reflect the effectiveness of predicting the disposition of the customer; and

using the effectiveness of predicting the disposition of the customer to more accurately predict a subsequent disposition of the customer during a subsequent visit to the merchant or for future marketing content.

4. The computer-implemented method of claim 1, wherein generating the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

segmenting the first-party data to identify the set of attributes associated with the customer profile,

assessing reliabilities of the attributes of the initial set, and

determining scores for the attributes based on the reliabilities of the attributes.

5. The computer-implemented method of claim 4, wherein generating the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

analyzing the third-party data for information relevant to one or more of the attributes, and

modifying one or more of the scores associated with the one or more of the attributes based on the information relevant to the one or more of the attributes.

6. The computer implemented method of claim 1, further comprising:

assigning quality scores to third-party information sources configured to provide analytics related to customers and goods; and

aggregating sets of analytics data from the third-party information sources, respectively, the sets of analytics data comprising the third-party data, wherein generating the customer profile for the customer of the merchant based on the first-party data and the third-party data includes analyzing the sets of analytics data for information associated with one or more of the attributes,

determining the relevance of the information based on an origin of the information and the quality scores that are applicable to the information, and

selectively augmenting the attributes using the information based on the relevance.

7. The computer implemented method of claim 1, wherein the real-time intelligence data includes one or more of a current behavior of the customer, a current market condition, and a product price.

8. The computer implemented method of claim 1, wherein the first-party data further includes one or more of cross-channel transaction data, ecommerce analytics data, location analytics data, and call center analytics data and the third-party data includes one or more of network ecosystem data, online history data, household data, mobile intelligence data, site journey data, and competitive intelligence data.

9. A computer program product comprising a non-transitory computer-usable medium including instructions which, when executed by a computer, cause the computer to:

receive first-party data and third-party data;

generate a customer profile for the customer of a merchant based on the first-party data and the third-party data, the customer profile having a set of attributes;

generate a set of rules for evaluating a disposition of the customer based on the set of attributes of the customer profile;

receive real-time intelligence data associated with the customer during a visit to the merchant or an interaction with marketing content of the merchant; and

predict a disposition of the customer using the real-time intelligence data and one or more of the rules; and

adapt a shopping experience of the customer during the visit to the merchant or a marketing content of the merchant for the customer using the predicted disposition.

10. The computer program product of claim 9, wherein the instructions further cause the computer to:

predict the disposition of the customer includes analyzing the real-time intelligence data using the one or more of the rules to determine one or more updates to the shopping experience or the marketing content, and

adapt the shopping experience of the customer or the marketing content includes personalizing the shopping experience or the marketing content using the one or more updates to improve a customer conversion rate associated with the customer during the visit or a future visit to the merchant.

11. The computer program product of claim 9, wherein the instructions further cause the computer to:

determine an effectiveness of predicting the disposition of the customer based on whether the customer purchased an associated product;

update the customer profile to reflect the effectiveness of predicting the disposition of the customer; and

use the effectiveness of predicting the disposition of the customer to more accurately predict a subsequent disposition of the customer during a subsequent visit to the merchant or for future marketing content.

12. The computer program product of claim 9, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

segmenting the first-party data to identify the set of attributes associated with the customer profile,

assessing reliabilities of the attributes of the initial set, and

determining scores for the attributes based on the reliabilities of the attributes.

13. The computer program product of claim 12, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

analyzing the third-party data for information relevant to one or more of the attributes, and

modifying one or more of the scores associated with the one or more of the attributes based on the information relevant to the one or more of the attributes.

14. The computer program product of claim 9, wherein the instructions further cause the computer to:

assign quality scores to third-party information sources configured to provide analytics related to customers and goods; and

aggregate sets of analytics data from the third-party information sources, respectively, the sets of analytics data comprising the third-party data, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

analyzing the sets of analytics data for information associated with one or more of the attributes,

determining the relevance of the information based on an origin of the information and the quality scores that are applicable to the information, and

selectively augmenting the attributes using the information based on the relevance.

15. The computer program product of claim 9, wherein the real-time intelligence data includes one or more of a current behavior of the customer, a current market condition, and a product price.

16. The computer program product of claim 9, wherein the first-party data further includes one or more of cross-channel transaction data, ecommerce analytics data, location analytics data, and call center analytics data and the third-party data includes one or more of network ecosystem data, online history data, household data, mobile intelligence data, site journey data, and competitive intelligence data.

17. A system comprising:

one or more processors, the processors being configured to;

receive first-party data and third-party data;

generate a customer profile for the customer of a merchant based on the first-party data and the third-party data, the customer profile having a set of attributes;

generate a set of rules for evaluating a disposition of the customer based on the set of attributes of the customer profile;

receive real-time intelligence data associated with the customer during a visit to the merchant or an interaction with marketing content of the merchant; and

predict a disposition of the customer using the real-time intelligence data and one or more of the rules; and

adapt a shopping experience of the customer during the visit to the merchant or a marketing content of the merchant for the customer using the predicted disposition.

18. The system of claim 17, wherein the one or more processors are further configured to:

predict the disposition of the customer includes analyzing the real-time intelligence data using the one or more of the rules to determine one or more updates to the shopping experience or the marketing content, and

adapt the shopping experience of the customer or the marketing content includes personalizing the shopping experience or the marketing content using the one or more updates to improve a customer conversion rate associated with the customer during the visit or a future visit to the merchant.

19. The system of claim 17, wherein the one or more processors are further configured to:

determine an effectiveness of predicting the disposition of the customer based on whether the customer purchased an associated product;

update the customer profile to reflect the effectiveness of predicting the disposition of the customer; and

use the effectiveness of predicting the disposition of the customer to more accurately predict a subsequent disposition of the customer during a subsequent visit to the merchant or for future marketing content.

20. The system of claim 17, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

segmenting the first-party data to identify the set of attributes associated with the customer profile,

assessing reliabilities of the attributes of the initial set, and

determining scores for the attributes based on the reliabilities of the attributes.

21. The system of claim 19, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

analyzing the third-party data for information relevant to one or more of the attributes, and

modifying one or more of the scores associated with the one or more of the attributes based on the information relevant to the one or more of the attributes.

22. The system of claim 17, wherein the instructions further cause the computer to:

assign quality scores to third-party information sources configured to provide analytics related to customers and goods; and

aggregate sets of analytics data from the third-party information sources, respectively, the sets of analytics data comprising the third-party data, wherein to generate the customer profile for the customer of the merchant based on the first-party data and the third-party data includes

analyzing the sets of analytics data for information associated with one or more of the attributes,

determining the relevance of the information based on an origin of the information and the quality scores that are applicable to the information, and

selectively augmenting the attributes using the information based on the relevance.

23. The system of claim 17, wherein the real-time intelligence data includes one or more of a current behavior of the customer, a current market condition, and a product price.

24. The system of claim 17, wherein the first-party data further includes one or more of cross-channel transaction data, ecommerce analytics data, location analytics data, and call center analytics data and the third-party data includes one or more of network ecosystem data, online history data, household data, mobile intelligence data, site journey data, and competitive intelligence data.