Predictive conversion systems and methods

• US 8,326,845 B2
• Filed: 01/24/2012
• Issued: 12/04/2012
• Est. Priority Date: 12/12/2007
• Status: Active Grant

• Current Assignee: Vast.com, Inc. (Vroom, Inc.)
• Original Assignee: Vast.com, Inc. (Vroom, Inc.)
• Inventors: Sethi, Komal Singh, Tatarevic, Milos Milinko, Bradic, Aleksandr Milutin, Laws, Kevin Allen
• Primary Examiner(s): Shmatov, Alexey
• Application Number:
  US13/357,540
• Publication Number:
  US 20120143924A1
• Time in Patent Office: 315 Days
• Field of Search: 707/751, 707/776, 707/999.001
• US Class Current: 707/751
• CPC Class Codes: G06F 16/244 Grouping and aggregation
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

In one embodiment, a system and method of predicting sale transaction conversion rate of an item operates through a search of information in response to a query over a network. The system and method can includes discovering available information of the item of interest, extracting certain of the available information of the item, analyzing the certain information by comparing the information to other item information, weighting the information for the item in comparison to other items of the category, calculating a predictive score for the item of interest, and presenting the information of the item of interest ranked according to the predictive score as compared to other items of the category.

Claims

1. A computer system for building an inventory database, the computer system comprising:

an electronic memory storage configured to store modules;

a computer processor configured to execute the modules comprising at least;

a data access module configured to access inventory data for a plurality of products from at least one inventory data source,

a metadata extractor module configured to extract stated metadata from the inventory data for each of the plurality of products, wherein stated metadata comprises attributes of a product that are obtained from a product description provided by advertisers or suppliers of the plurality of products in the inventory database,

a product evaluator module configured to determine a product category or a product identification based on the stated metadata for each of the plurality of products,

a comparator module configured to compare for each of the plurality of products the product category or the product identification to stored metadata in a metadata database, wherein the metadata database comprises additional metadata for a plurality of products,

a metadata identifier module configured to identify derived metadata for each of the plurality of products based on the comparison, wherein the derived metadata comprises attributes of a product that are identified based on characteristics of the product and that are not stated in a product description provided by advertisers or suppliers of the plurality of products in the inventory database, and wherein the derived metadata is stored in the metadata database,

a conversions module configured to generate a specific conversion rate for each of the plurality of products based on inputting the stated metadata and the derived metadata into a conversion rate formula, wherein the specific conversion rate relates to generating more click-throughs or higher revenue, and wherein the conversion rate formula is based on a linear or logistic regression analysis,

a conversion rate assignor module configured to assign the specific conversion rate corresponding to each of the plurality of products, and

a storage module configured to store in the inventory database for each of the plurality of products the stated metadata, the derived metadata, and the specific conversion rate.

2. The computer system of claim 1, further comprising an analytics module configured to receive a request for a listing of available products in a specific product category or product identification over a network.

3. The computer system of claim 2, wherein the request is received from a computing device from the group comprising of a desktop, a laptop, a personal data assistant, or a data-enabled phone.

4. The computer system of claim 1, wherein the linear or logistic regression analysis is generated based on an analysis of historical conversion rates for at least one product in the plurality of products.

5. The computer system of claim 4, wherein the analysis of historical conversion rates comprises a probability that a user clicks on a particular product based on an analysis of historical user behavior.

6. The computer system of claim 1, wherein derived metadata comprises attributes of a product that are provided by a third party source and that are not stated in a product description provided by advertisers or suppliers of the plurality or products from the at least one inventory data source.

7. The computer system of claim 1, wherein the conversion rate relates to lead generation.

8. The computer system of claim 1, wherein the conversion rate relates to generating higher advertising revenue.

9. The computer system of claim 1, wherein the received inventory data comprises an identification of the inventory data source and a model, make, color, and year of an automobile.

10. The computer system of claim 1, wherein the metadata extractor module is further configured to normalize and process accessed inventory data.