CUSTOMER EXPERIENCE MANAGEMENT FOR AN ORGANIZATION

• US 20140222538A1
• Filed: 02/06/2014
• Published: 08/07/2014
• Est. Priority Date: 02/07/2013
• Status: Abandoned Application

• Current Assignee: Korl8, Inc.
• Original Assignee: Korl8, Inc.
• Inventors: Merrifield, Eric S. Jr., Vellon, Manuel, Hadland, Mark D.
• Application Number:
  US14/174,831
• Publication Number:
  US 20140222538A1
• Time in Patent Office: Days
• US Class Current: 705/14.25
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0252 based on events or environment, e.g. weather or festivals

Abstract

The present invention extends to methods, systems, and computer program products for customer experience management for an organization. Embodiments of the invention can be used to monitor and analyze customer activity. From larger volumes of data, data can be concentrated to identify events with higher relevance to customer or guest experiences with the organization. Data can be correlated with customer or guest experiences to provide more personalized experiences in the future. Embodiments include event processing rules. Event processing rules can be used to provide more intelligent rewards to customers or guests. Event processing rules can also be used to synthesize other events. Embodiments can apply data analytics at a range of organizational levels (e.g., operator to management level) to improve customer or guest experiences. Embodiments can provide visualizations to an organization to present correlated trend data about customers or guests.

Claims

1. At a computer system, the computer system including system memory, one or more processors, and a database, a method for determining a customer reward, the method comprising:

accessing customer data from one or more customer inputs;

concentrating the customer data into one or more relevant customer events;

the processor formulating one or more synthetic events from the one or more relevant events;

the processor deriving an intelligent reward for at least one customer based on the one or more relevant events; and

storing the one or more synthetic events and the intelligent reward in the database.

2. The method of claim 1, wherein accessing customer data from one or more customer inputs comprises accessing customer data from one or more of:

location services, surveys, customer relationship management systems, and point of sale systems.

3. The method of claim 1, wherein formulating one or more synthetic events from the one or more relevant events comprises formulating a synthetic event that provides a benefit to a customer.

4. The method of claim 1, wherein formulating a synthetic event that provides a benefit to a customer comprises tailoring the synthetic event for the customer based on the customer'"'"'s inclusion in a particular segment of a customer base.

5. The method of claim 4, wherein tailoring the synthetic event for the customer based on the customer'"'"'s inclusion in a particular segment of a customer base comprises tailoring the synthetic event for the customer based on the customer'"'"'s profitability.

6. The method of claim 1, wherein deriving an intelligent reward for at least one customer based on the one or more relevant events comprises tailoring a reward for the customer based on the customer'"'"'s inclusion in a particular segment of a customer base.

7. At a computer system, the computer system including system memory, one or more processors, and a database, a method for determining customer recommendations based on customer events associated with an organization, the method comprising:

accessing customer data from the database, the customer data representing individual events for one or more customers of a customer base;

the processor formulating analysis results by analyzing the accessed data using one or more of;

a customer experience index, data mining, and ad hoc queries;

the processor generating trend data for a plurality of different segments of the customer base from the analysis results, the customer base segmented using a multi-variable algorithm based on the values for a plurality of different variables provided to the multi-variable algorithm;

providing a recommendation for at least one customer based on individual events and trend data for the at least one customer, the at least one customer selected from among the one or more customers of the customer base; and

storing the recommendation in the database.

8. The method of claim 7, further comprising generating real-time data and time lapse data for the plurality of different segments of the customer base from the analysis results.

9. The method of claim 8, wherein providing a recommendation for at least one customer comprises presenting one or more of:

the real-time, the trend data, and the time lapse data for the plurality of different segments of the customer base.

10. The method of claim 8, wherein the plurality of different variables include one or more of:

profitability, frequency, current experiences with an organization, and historical experiences with the organization.

11. The method claim 8, wherein providing a recommendation for at least one customer comprises providing a recommendation to give a customer one of:

a synthetic event or a reward.

12. The method of claim 11, wherein providing a recommendation to give a customer one of:

a synthetic event or a reward comprises providing a recommendation to a customer one of;

a tailored synthetic event or a tailored reward based on the customer being included in a particular customer segment, the particular customer segment being selection from among the plurality of different segments of the customer base, the a tailored synthetic event or a tailored reward being tailored for the particular customer segment and differing from recommendations for other customer segments.

13. The method of claim 8, further comprising the multi-variable algorithm segment the customer base based on the values for a plurality of different variables provided to the multi-variable algorithm.

14. The method of claim 13, wherein the plurality of different variables include profitability, frequency, current experiences with the organization, and historical experiences with the organization

15. A customer experience management (CEM) system for an organization, the customer experience management (CEM) comprising:

one or more processors;

system memory;

a distributed database;

a customer activity event module;

an event processing rules engine;

wherein the customer activity event module is configured to;

access customer data from one or more inputs;

concentrate the customer data into one or more relevant customer events; and

send the one or more relevant events to the event processing rules engine; and

store the one or more relevant events to the distributed database;

wherein the event processing rules engine is configured to;

receive the one or more relevant events from the customer activity event module;

formulate one or more synthetic events from the one or more relevant events;

derive an intelligent reward for at least one customer based on the one or more relevant events; and

store the one or more synthetic events and the intelligent reward in the distributed database;

16. The customer experience management (CEM) system of claim 15, further comprising analytics, wherein the analytics are configured to:

access data from the distributed database;

analyze the accessed data using one or more of a customer experience index, data mining, and ad hoc queries;

generate trend data from the accessed data;

provide a recommendation for at least one customer based on individual events or trend data for the at least one customer; and

store analysis results in the distributed database.

17. The customer experience management (CEM) system of claim 16, further comprising a visualizer, wherein the visualizer is configured to:

access data from the distributed database; and

present one or more of;

real-time, trend data, and time lapse data for a plurality of different segments of a customer base, wherein the customer based is segmented using a multi-variable algorithm based on the values for a plurality of different variables provided to the multi-variable algorithm as input.

18. The customer experience management (CEM) system of claim 17, wherein the analytics being configured to provide a recommendation for at least one customer comprises the analytics being configured to provide a recommendation for a synthetic event tailored to the at least one customer based on the customer being including in a particular segment of the customer base, particular segment of the customer base selected from among plurality of different segments of a customer base.

19. The customer experience management (CEM) system of claim 17, wherein the analytics being configured to provide a recommendation for at least one customer comprises the analytics being configured to provide a recommendation for a reward tailored to the at least one customer based on the customer being including in a particular segment of the customer base, particular segment of the customer base selected from among plurality of different segments of a customer base.

20. The customer experience management (CEM) system of claim 17, wherein the plurality of different variables includes two or more of:

profitability, frequency, current experiences with the organization, and historical experiences with the organization.