Cross-channel online advertising attribution

Patent metadata

Field	Value
Publication number	US-10169778-B1
Application number	US-201514669945-A
Country	US
Kind code	B1
Filing date	Mar 26, 2015
Priority date	Mar 26, 2015
Publication date	Jan 1, 2019
Grant date	Jan 1, 2019

Abstract

The systems and methods described herein enable measurement of the exposure of a user to advertising campaigns across different channels (e.g., websites accessed by web browsers of laptops, desktops, or mobile computers, electronic readers or tablets, applications on mobile devices, and the like) and credit the exposure of the user to the advertisement to a specific advertisement in the campaign along with subsequent shopping events generated by the user, such as visiting product detail pages, visiting the read all reviews page for a product, adding a product to a wish list, adding a product to a shopping cart, subscribing to a product through an online subscription service, and/or purchasing a product.

First claim

What is claimed is:

1. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processor, cause the processor to perform operations comprising:
receiving, by a server comprising one or more computer processors, event data comprising a first set of data from a first user device and a second set of data from a second user device, the first user device and the second user device in communication with the server over a network;
transmitting, by the server to an identity resolution service (IRS) server, the event data;
transmitting, by the server to a consolidated event store (CES) server, the event data;
receiving, by the server from the IRS server, identity mappings generated using the event data, wherein the identity mappings comprise a mapping of (i) anonymized identifiers associated with a user to (ii) identifiers associated with user devices;
determining a canonical identifier that is associated with the identity mappings, wherein the canonical identifier comprises at least a portion of the anonymized identifiers;
linking, based at least in part on the canonical identifier and the associated identity mappings, in a database, one or more shopping events in the event data with a conversion event, the canonical identifier identifying respective users of both the first user device and the second user device;
receiving, by the server from the CES server, advertisement traffic events identified from the event data;
determining that a first counter is not equal to a first threshold length of time;
determining that a second counter is not equal to a second threshold length of time that is less than the first threshold length of time;
determining that a third counter is equal to a third threshold length of time that is less than the second threshold length of time; and
generating, by the server using one or more attribution models after the third counter equals the third threshold length of time, ad attribution data indicative of an attribution of the conversion event to an advertisement of an advertisement campaign.

2. The non-transitory computer-readable medium of claim 1 , wherein the operations further comprise:
transmitting, by the server, the ad attribution data to a datastore; and
retrieving, by the server, the ad attribution data to generate a path to purchase report.

3. The non-transitory computer-readable medium of claim 1 , wherein generating the ad attribution data further comprises:
generating, by the server, the ad attribution data on an hourly, daily, and weekly basis.

4. The non-transitory computer-readable medium of claim 1 , wherein the ad attribution data is generated using the event data from an attribution window.

5. A method comprising:
receiving, by a server comprising one or more computer processors, event data from a first user device and a second user device in communication with the server over a network;
transmitting, by the server, the event data to an identity resolution service (IRS) server;
transmitting, by the server, the event data to a consolidated event store (CES) server;
receiving, by the server from the IRS server, identity mappings generated using the event data, wherein the identity mappings comprise a mapping of anonymized identifiers associated with a user to identifiers associated with user devices;
determining a canonical identifier, wherein the canonical identifier is associated with the identity mappings, wherein the canonical identifier comprises at least a portion of the anonymized identifiers;
linking, based at least in part on the canonical identifier and the associated identity mappings, in a database, one or more shopping events in the event data with a conversion event, the canonical identifier identifying one or more users of both the first user device and the second user device;
receiving, by the server from the CES server, advertisement traffic events identified from the event data;
determining that a first counter is not equal to a first threshold length of time;
determining that a second counter is not equal to a second threshold length of time that is less than the first threshold length of time;
determining that a third counter is equal to a third threshold length of time that is less than the second threshold length of time; and
generating, by the server, ad attribution data indicative of an attribution of the conversion event to an advertisement of an advertisement campaign.

6. The method of claim 5 , further comprising:
transmitting, by the server, the ad attribution data to a datastore;
retrieving, by the server, the ad attribution data; and
modifying, by the server, the one or more attribution models using the ad attribution data.

7. The method of claim 5 , further comprising:
executing, by the server, the one or more attribution models, wherein the one or more attribution models use the identity mappings and the advertisement traffic events to generate the ad attribution data.

8. The method of claim 5 , wherein the ad attribution data is generated using the event data from an attribution window, wherein the attribution window is a period of time prior to the conversion event used to identify the advertisement that may receive credit for the conversion event.

9. The method of claim 5 , further comprising:
setting, by the server, the first threshold to be less than the second threshold; and
setting, by the server, the second threshold to be less than the third threshold.

10. The method of claim 5 , further comprising:
identifying, by the server, a first conversion event from the identity mappings and advertisement traffic events;
identifying, by the server, a first candidate advertisement from the identity mappings and the advertisement traffic events;
determining, by the server, the first candidate advertisement is a secondary advertisement;
determining, by the server, a first view discount rate of the first candidate advertisement;
generating, by the server, a first attribution probability using a generated random number and the first view discount rate;
comparing, by the server, the first attribution probability to an attribution threshold; and
generating, by the server, the ad attribution data attributing the first candidate advertisement with the first conversion event in response to determining the first attribution probability exceeds the attribution threshold.

11. The method of claim 10 , further comprising:
identifying, by the server, a second conversion event from the identity mappings and advertisement traffic events;
identifying, by the server, a second candidate advertisement from the identity mappings and the advertisement traffic events;
determining, by the server, the second candidate advertisement is a secondary advertisement;
determining, by the server, a second view discount rate of the second candidate advertisement;
generating, by the server, a second attribution probability using a second generated random number and the second view discount rate;
determining, by the server, the second attribution probability does not exceed the attribution threshold;
identifying, by the server, a third candidate advertisement from the identity mappings and the advertisement traffic events;
determining, by the server, the third candidate advertisement is the secondary advertisement;
determining, by the server, a third view discount rate of the third candidate advertisement;
generating, by the server, a third attribution probability using a third generated random number and the third view discount rate;
determining, by the server, the second attribution probability does not exceed the attribution threshold; and
ge

Assignees

• Amazon Tech Inc

Inventors

• Collin Guthrie Chipman
• Craycroft Timothy John
• Despins Paula Marie
• Edwards Iii Richard Everett
• Khoshnevisan Cyrus
• Nilles John Michael
• Perevodchikov Evgeniy V
• Ramamoorthy Jaikannan
• Ramji Shiven

Classifications

• G06Q30/0246 Primary

  Traffic · CPC title