Method for Passive Mining of Usage Information In A Location-Based Services System

Abstract

A method and system for providing advertising effectiveness searching capabilities, predictive modeling capabilities and usage mining in a location-based services system is disclosed. During operation of the location-based services system, usage information for advertising campaigns placed on the location-based services system is stored. Advertisers are provided with the ability to enter a search request form on a remote terminal to mine the usage information. The search request is then transmitted to an application that searches usage information to generate a response to said search request.

1 - 6 . (canceled) 
     
     
         7 . A computer-implemented method comprising:
 receiving, at a computer system and from a client computing device, a request to forecast performance of a proposed customer-facing feature for a restaurant;   generating, by the computer system, a query based, at least in part, on the proposed customer-facing feature and the restaurant;   accessing, by the computer system, customer transaction data that indicates customer engagement with previous customer-facing features for a restaurant, wherein the customer transaction data was generated, at least in part, from the computer system providing location-based product services for restaurants that were accessed by customers using mobile computing devices;   identifying, by the computer system, data elements from the customer transaction data that match at least a portion of the query generated for the proposed customer-facing feature and the restaurant;   determining, by the computer system, a likelihood of success for the proposed customer-facing feature based, at least in part, on the identified data elements, wherein the likelihood of success indicates a predicted performance of the customer-facing feature for the restaurant based, at least in part, on the customer transaction data;   providing, by the computer system, information that describes the likelihood of success for the proposed customer-facing feature to the client computing device.   
     
     
         8 . The computer-implemented method of  claim 7 , wherein determining the likelihood of success comprises:
 identifying, by the computer system, a plurality of customers who have at least a threshold likelihood of purchasing or otherwise engaging with the proposed customer-facing feature based, at least in part, on the identified data elements; and   determining, by the computer system, the likelihood of success based, at least in part, on the plurality of customers.   
     
     
         9 . The computer-implemented method of  claim 8 , wherein the likelihood of success is determined based, at least in part, on a number of customers in the plurality of customers. 
     
     
         10 . The computer-implemented method of  claim 8 , wherein the likelihood of success is determined based, at least in part, on customer types for the plurality of customers. 
     
     
         11 . The computer-implemented method of  claim 8 , wherein the identified data elements comprise data that identifies transactions in which the plurality of customers purchased or otherwise engaged with another customer-facing feature that has at least a threshold level of similarity to the proposed customer-facing feature. 
     
     
         12 . The computer-implemented method of  claim 11 , wherein the identified transactions in which the plurality of customers purchased or otherwise engaged with another customer-facing feature comprise transactions with the restaurant and transactions with other restaurants. 
     
     
         13 . The computer-implemented method of  claim 7 , wherein:
 the request includes timing information for the proposed customer-facing feature that identifies timing for the proposed customer-facing feature to be available to customers, and   the data elements are identified additionally based on the timing information.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein the timing information comprises a start date on which the proposed customer-facing feature will be made available to customers. 
     
     
         15 . The computer-implemented method of  claim 13 , wherein the timing information comprises a duration for the proposed customer-facing feature to be made available to customers. 
     
     
         16 . The computer-implemented method of  claim 7 , wherein:
 the request includes a demographic restriction that restricts the determination of the likelihood of success of the customer-facing feature to one or more demographic groups of customers, and   the query is generated to include the demographic restriction.   
     
     
         17 . The computer-implemented method of  claim 16 , wherein the one or more demographic groups are based on one or more of the following demographic features:
 gender, age, ethnicity, marital status, children, income, special interests, hobbies, education, homeowner status, and car owner status.   
     
     
         18 . The computer-implemented method of  claim 7 , wherein:
 the request includes a market restriction that restricts the determination of the likelihood of success of the customer-facing feature to customers within one or more target markets, and   the query is generated to include the market restriction.   
     
     
         19 . The computer-implemented method of  claim 18 , wherein the market restriction comprises a geographic region. 
     
     
         20 . The computer-implemented method of  claim 7 , wherein the proposed customer-facing feature comprises a proposed marketing campaign for a product or service of the restaurant. 
     
     
         21 . The computer-implemented method of  claim 7 , wherein the proposed customer-facing feature comprises a proposed advertisement for a product or service of the restaurant. 
     
     
         22 . The computer-implemented method of  claim 7 , wherein the proposed customer-facing feature comprises a proposed product or service to be offered to customers by the restaurant. 
     
     
         23 . The computer-implemented method of  claim 7 , wherein the proposed customer-facing feature comprises proposed pricing for a product or service to be offered to customers by the restaurant. 
     
     
         24 . The computer-implemented method of  claim 7 , wherein the proposed customer-facing feature comprises a proposed discount for a product or service to be to customers by the restaurant. 
     
     
         25 . The computer-implemented method of  claim 7 , wherein the restaurant comprises a restaurant chain. 
     
     
         26 . The computer-implemented method of  claim 7 , wherein the information that describes the likelihood of success includes predicted customer information including a number of predicted customers and demographic information for the predicted customers.