Evaluating coincident interaction indicators

Patent metadata

Field	Value
Publication number	US-11625733-B2
Application number	US-201715730227-A
Country	US
Kind code	B2
Filing date	Oct 11, 2017
Priority date	Jul 25, 2013
Publication date	Apr 11, 2023
Grant date	Apr 11, 2023

Abstract

Disclosed are embodiments for determining the impact of one or more latent factors on user interaction metrics based at least in part on an impact model. The embodiments identify a value for a user interaction metric, the user interaction metric measuring interaction with content and identify an impact for a latent factor on the user interaction metric, the impact determined based at least in part on a model providing a relationship between the user interaction metric and the latent factor. Additionally, embodiments may involve adjusting an attribute of the electronically provided content based at least in part on the impact of the latent factor on the user interaction metric.

First claim

The invention claimed is:

1. A method for modifying an electronic advertisement based on a latentfactor-adjusted measure of advertisement effectiveness, the method comprising performing, by a processing device, operations comprising:
providing an electronic advertisement to users via an electronic network;
identifying a first measurement value for a user interaction metric to use as a measure of advertisement effectiveness, the user interaction metric measuring tracked user interactions with the electronic advertisement on computing devices of the users;
identifying a second measurement value for the user interaction metric, the first measurement value indicating a first portion of the tracked user interactions measured during a first period of time and the second measurement value indicating a second portion of the tracked user interactions measured during a second period of time;
estimating an impact value using an applied impact model, wherein:
the impact value correlates a latent factor to the second measurement value, wherein the latent factor is an aspect of the users or economy, wherein the latent factor is one or more of an economic factor, brand equity, brand loyalty, or goodwill,
the impact value is determined based at least in part on the applied impact model providing a relationship between the user interaction metric and the latent factor, and
the impact value quantifies a percentage change in the second measurement value as compared to the first measurement value, the percentage change in the second measurement value resulting from a change in the latent factor during the second period of time as compared to the first period of time;

determining a changed measurement value based on a modification of the second measurement value, the changed measurement value indicating an improved measure of advertisement effectiveness, wherein the changed measurement value is the second measurement value as modified based on the percentage change quantified by the estimated impact value;
modifying the electronic advertisement based on the changed measurement value; and
providing the modified electronic advertisement to additional users via the electronic network.

2. The method of claim 1 , wherein the applied impact model is a dynamic impact model.

3. The method of claim 1 , wherein:
the impact value indicates that the relationship between the user interaction metric and the latent factor has a ratio, and
the percentage change in the second measurement value resulting from the change in the latent factor is based in part on the ratio.

4. The method of claim 1 , wherein a threshold value is specified for the electronic advertisement.

5. The method of claim 4 , wherein the threshold value corresponds to a minimum impact value for modifying the electronic advertisement.

6. The method of claim 1 , wherein the user interaction metric comprises one or more of a number of clicks, a number of mouse overs, a number of page views, a number of mouse gestures, an amount of time spent on a web page, an amount of time elapsed before a mouse gesture, or a frequency of accesses to the web page.

7. The method of claim 1 , wherein the latent factor impacts a significance of the second measurement value as the measure of advertisement effectiveness.

8. The method of claim 1 , wherein the latent factor affects decisions of the users to interact with the electronic advertisement and wherein the changed measurement value is determined based on the user interaction metric being coincident with the latent factor.

9. The method of claim 1 , wherein the modifying the electronic advertisement includes modifying at least one attribute associated with the electronic advertisement.

10. The method of claim 9 , wherein the at least one attribute is related to one of an amount of the electronic advertisement or a subject matter of the electronic advertisement.

11. A method for recommending modifying electronic content based on a latent-factor-adjusted measure of effectiveness, the method comprising performing, by a processing device, operations comprising:
measuring a user interaction metric associated with electronic content, the user interaction metric measuring tracked user interactions with the electronic content on user computing devices;
identifying a first measurement value for the user interaction metric to use as a measure of advertisement effectiveness;
identifying a second measurement value for the user interaction metric, the first measurement value indicating a first portion of the tracked user interactions measured during a first period of time and the second measurement value indicating a second portion of the tracked user interactions measured during a second period of time;
estimating, by an applied impact model, an impact value, wherein:
the impact value correlates a latent factor to the second measurement value, wherein the latent factor is an aspect of users or economy, wherein the latent factor is one or more of an economic factor, brand equity, brand loyalty, or goodwill,
the impact value is determined based at least in part on the applied impact model providing a relationship between the user interaction metric and the latent factor, and
the impact value quantifies a percentage change in the second measurement value as compared to the first measurement value, the percentage change in the second measurement value resulting from a change in the latent factor during the second period of time as compared to the first period of time;

determining a changed measurement value based on a modification of the second measurement value, the changed measurement value indicating an improved measure of advertisement effectiveness, wherein the changed measurement value is the second measurement value as modified based on the percentage change quantified by the estimated impact value;
generating a recommendation to modify the electronic content based on the changed measurement value;
modifying the electronic content based on the recommendation; and
providing the modified electronic content to an additional user.

12. The method of claim 11 , wherein the applied impact model is based on a Stock and Watson model.

13. The method of claim 11 , further comprising receiving a threshold value as input.

14. The method of claim 11 , wherein a threshold value is specified for and specific to the electronic content.

15. The method of claim 14 , wherein the threshold value corresponds to a minimum impact value for generating the recommendation to modify the electronic content.

16. The method of claim 11 , wherein the recommendation recommends modifying at least one attribute associated with the electronic content, the at least one attribute related to one of an amount of the electronic content or a subject matter of the electronic content.

17. The method of claim 11 , wherein the impact value represents a change in units to the user interaction metric based on the latent factor.

18. The method of claim 11 , wherein the user interaction metric comprises on or more of: a number of clicks, a number of mouse overs, a number of page views, a number of mouse gestures, an amount of time spent on a web page, an amount of time elapsed before a mouse gesture, or a frequency of accesses to the web page.

19. A system comprising:
a processor for executing instructions stored in computer-readable medium on one or more devices,
the instructions comprising one or more modules configured to perform operations comprising:
providing an electronic advertisement to users via

Assignees

• Adobe Inc

Inventors

• Farahat Ayman

Classifications

• G06Q30/02 Primary

  Marketing; Price estimation or determination; Fundraising · CPC title