US-20260127634-A1 - DYNAMIC FREQUENCY CAPPING FOR TARGETED CONTENT

US20260127634A1US 20260127634 A1US20260127634 A1US 20260127634A1US-20260127634-A1

Abstract

Methods and systems are provided for dynamic frequency capping for targeted content. In embodiments described herein, target audience exploration engine determines a target audience for content corresponding to a set of target devices for each household of a set of target households. A targeted content platform serves the content to a corresponding household of the set of target households until an exploration budget of a number of impressions is met for the corresponding household or an exploration phase time limit ends for the corresponding household. A budget optimization engine determines an updated target audience by adding a target device to the corresponding set of target devices of the corresponding household or removing the corresponding household to add a new household to the set of target households. The targeted content platform serves the content to the updated target audience.

Inventors

Prashant Dahiya
Ankur Gupta
Ankur Dhir
Adarshdeep SINGH

Assignees

ADOBE INC.

Dates

Publication Date: 20260507
Application Date: 20241105

Claims (20)

1 . A computing system comprising: a processor; and a non-transitory computer-readable medium having stored thereon instructions that when executed by the processor, cause the processor to perform operations including: determining, by a target audience exploration engine, a target audience for content, the target audience comprising a corresponding set of target devices for each household of a set of target households, the corresponding set of target devices for each household corresponding to a subset of devices associated with the household; causing a targeted content platform to serve the content to a corresponding household of the set of target households until an exploration budget is met for the corresponding household or an exploration phase time limit ends for the corresponding household, the exploration budget corresponding to a number of impressions of the content for the corresponding household; responsive to the exploration budget being met for the corresponding household or the exploration phase time limit ending for the corresponding household, determining, by a budget optimization engine, an updated target audience by (1) adding an additional device from corresponding devices already associated with the corresponding household to the corresponding set of target devices of the corresponding household or (2) removing the corresponding household to add a new household to the set of target households; and causing the targeted content platform to serve the content to the updated target audience.
2 . The computing system of claim 1 , the operations further including: determining each household of the set of target households by: generating, by a household graph generation engine, household graphs mapping groups of devices to particular locations using historical customer data; and determining, by a household target device selection engine, the set of target households based on corresponding historical customer data for each corresponding group devices of each household graph of each household of the set of target households.
3 . The computing system of claim 1 , the operations further including: determining each household of the set of target households by: generating by a household graph generation engine household graphs mapping groups of devices to particular locations using historical customer data by: initially determining each corresponding household graph by mapping static devices to common IP addresses based on overlapping time windows; subsequently mapping moving devices to each corresponding household graph based on a maximum correlation between each moving device to the common IP addresses for corresponding overlapping time windows; and determining, by a household target device selection engine, the set of target households based on corresponding historical customer data for each corresponding group devices of each household graph of each household of the set of target households.
4 . The computing system of claim 1 , the operations further including: determining the corresponding set of target devices for each household of the set of target households by: determining, by a household influence determination engine, a corresponding influence score of each device with respect to remaining devices of each household, the corresponding influence score corresponding to a likelihood that displaying targeted content to a particular device of a group of devices mapped to a particular household will result in a conversion; and determining, by a household target device selection engine, the corresponding set of target devices for each household based on a summation of influence scores of the corresponding set of target devices for each household meeting a threshold cumulative influence score.
5 . The computing system of claim 1 , wherein the exploration budget corresponds to a percentage of a household-level frequency cap.
6 . The computing system of claim 1 , wherein the exploration budget comprises a device-level exploration frequency cap for each device of the corresponding set of target devices of the corresponding household based on an influence score of each device with respect to remaining devices of the corresponding household.
7 . The computing system of claim 1 , the operations further including: determining whether to (1) add the additional device to the corresponding set of target devices of the corresponding household or (2) remove the corresponding household to add the new household to the set of target households by: determining an exploration success metric based on (1) a first sum of influence scores of a first subset of the corresponding set of target devices of the corresponding household involved with interaction events with respect to (2) a second sum of influence scores of a second subset of the corresponding set of target devices of the corresponding household that received impressions; and determining whether to (1) add the additional device to the corresponding set of target devices of the corresponding household or (2) remove the corresponding household to add the new household to the set of target households based on whether the exploration success metric meets a threshold exploration success value.
8 . The computing system of claim 1 , the operations further including: responsive to an interaction event by a corresponding device of a different corresponding set of target devices of a different corresponding household of the updated target audience: determining by the budget optimization engine, a further updated target audience by (1) adding a corresponding additional device to the different corresponding set of target devices of the different corresponding household or (2) removing the different corresponding household to add a further new household to the updated target audience; and causing the targeted content platform to serve the content to the further updated target audience.
9 . The computing system of claim 1 , the operations further including: responsive to a different corresponding set of target devices of a different corresponding household of the updated target audience receiving less than a threshold number of impressions within a threshold household impression time limit: determining by the budget optimization engine, a further updated target audience by adding a corresponding additional device to the different corresponding set of target devices of the different corresponding household; and causing the targeted content platform to serve the content to the further updated target audience.
10 . A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising: determining, by a target audience exploration engine, a target audience for an advertisement, the target audience comprising a corresponding set of target devices for each household of a set of target households, the corresponding set of target devices for each household corresponding to a subset of devices associated with the household; causing an advertisement platform to serve the advertisement to a corresponding household of the set of target households until an exploration budget is met for the corresponding household or an exploration phase time limit ends for the corresponding household; and subsequent to the exploration budget being met for the corresponding household or the exploration phase time limit ending for the corresponding household, responding to an interaction event by a corresponding device of the corresponding set of target devices of the corresponding household by: determining, by a budget optimization engine, an updated target audience by (1) adding an additional device from corresponding devices already associated with the corresponding household to the corresponding set of target devices of the corresponding household or (2) removing the corresponding household to add a new household to the set of target households; and causing the advertisement platform to serve the advertisement to the updated target audience.
11 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: determining each household of the set of target households by: generating, by a household graph generation engine, household graphs mapping groups of devices to particular locations using historical customer data; and determining, by a household target device selection engine, the set of target households based on corresponding historical customer data for each corresponding group devices of each household graph of each household of the set of target households.
12 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: determining each household of the set of target households by: generating by a household graph generation engine household graphs mapping groups of devices to particular locations using historical customer data by: initially determining each corresponding household graph by mapping static devices to common IP addresses based on overlapping time windows; subsequently mapping moving devices to each corresponding household graph based on a maximum correlation between each moving device to the common IP addresses for corresponding overlapping time windows; and determining, by a household target device selection engine, the set of target households based on corresponding historical customer data for each corresponding group devices of each household graph of each household of the set of target households.
13 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: determining the corresponding set of target devices for each household of the set of target households by: determining, by a household influence determination engine, a corresponding influence score of each device with respect to remaining devices of each household, the corresponding influence score corresponding to a likelihood that displaying a targeted advertisement to a particular device of a group of devices mapped to a particular household will result in a conversion; and determining, by a household target device selection engine, the corresponding set of target devices for each household based on a summation of influence scores of the corresponding set of target devices for each household meeting a threshold cumulative influence score.
14 . The non-transitory computer-readable medium of claim 10 , wherein the exploration budget corresponds to a percentage of a household-level frequency cap.
15 . The non-transitory computer-readable medium of claim 10 , wherein the exploration budget comprises a device-level exploration frequency cap for each device of the corresponding set of target devices of the corresponding household based on an influence score of each device with respect to remaining devices of the corresponding household.
16 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: determining whether to (1) add the additional device to the corresponding set of target devices of the corresponding household or (2) remove the corresponding household to add the new household to the set of target households by: determining an interaction success metric based on (1) an influence score of the corresponding device with respect to (2) a sum of influence scores of a subset of the corresponding set of target devices of the corresponding household that received impressions; and determining whether to (1) add the additional device to the corresponding set of target devices of the corresponding household or (2) remove the corresponding household to add the new household to the set of target households based on whether the interaction success metric meets a threshold interaction success value.
17 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: responsive to a different interaction event by a different corresponding device of a different corresponding set of target devices of a different corresponding household of the updated target audience: determining by the budget optimization engine, a further updated target audience by (1) adding a corresponding additional device to the different corresponding set of target devices of the different corresponding household or (2) removing the different corresponding household to add a further new household to the updated target audience; and causing the advertisement platform to serve the advertisement to the further updated target audience.
18 . The non-transitory computer-readable medium of claim 10 , the operations further comprising: responsive to a different corresponding set of target devices of a different corresponding household of the updated target audience receiving less than a threshold number of impressions within a threshold household impression time limit: determining by the budget optimization engine, a further updated target audience by adding a corresponding additional device to the different corresponding set of target devices of the different corresponding household; and causing the advertisement platform to serve the advertisement to the further updated target audience.
19 . A computer-implemented method comprising: determining, by a target audience exploration engine, a target audience for an advertisement, the target audience comprising a corresponding set of target devices for each household of a set of target households, the corresponding set of target devices for each household corresponding to a subset of devices associated with the household; causing an advertisement platform to serve the advertisement to a corresponding household of the set of target households until an exploration budget is met for the corresponding household or an exploration phase time limit ends for the corresponding household; and subsequent to the exploration budget being met for the corresponding household or the exploration phase time limit ending for the corresponding household, responding to the corresponding set of target devices of the corresponding household receiving less than a threshold number of impressions within a threshold household impression time limit by: determining, by a budget optimization engine, an updated target audience by adding an additional device from corresponding devices already associated with the corresponding household to the corresponding set of target devices of the corresponding household; and causing the advertisement platform to serve the advertisement to the updated target audience.
20 . The computer-implemented method of claim 19 , further comprising: determining each household of the set of target households by: generating, by a household graph generation engine, household graphs mapping groups of devices to particular locations using historical customer data; and determining, by a household target device selection engine, the set of target households based on corresponding historical customer data for each corresponding group devices of each household graph of each household of the set of target households.

Description

BACKGROUND Advertisers are increasingly utilizing an omnichannel digital marketing strategy where customers are exposed to targeted advertisements over different communication channels to increase the likelihood of conversions. Frequency capping allows advertisers to limit the number of times a user sees the same advertisement in order to prevent overexposure to the advertisement while improving the effectiveness and reach of the advertisement with respect to the marketing budget. Currently, digital marketers manually input a static frequency capping setting that does not change with respect to the user being targeted. As such, the digital marketer must then manually experiment and refine the static frequency capping setting in order to determine the preferred frequency capping setting for the specific marketing campaign. SUMMARY Various aspects of the technology described herein are generally directed to systems, methods, and computer storage media for, among other things, dynamic frequency capping for targeted content. For example, historical customer data is used to generate household graphs mapping groups of devices to particular locations. An influence score is determined for each device of each household corresponding to the likelihood that displaying targeted content to a particular device of a group of devices mapped to a particular household will result in a conversion. A target audience that includes a set of target devices for each household of a set of target households is determined based on the influence score for the set of target devices. During an exploration phase for each household, a targeted content platform serves content to the target audience until an exploration budget is met for the household or a time limit is reached. During an exploitation phase, the set of target devices of the set of target households and the set of target households are updated responsive to each time that: (1) the exploration budget is met for a target household; (2) the exploration phase time limit ends without the exploration budget being met for a target household; (3) an interaction event occurs for a target household after the exploration phase ends for the target household; and/or (4) after a time period ends when a target household received less than a threshold number of impressions. In this regard, the targeted content platform serves content to the updated target audience to increase the likelihood of conversions. This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 depicts a diagram of an environment in which one or more embodiments of the present disclosure can be practiced, in accordance with various embodiments of the present disclosure. FIG. 2 depicts an example configuration of an operating environment in which some implementations of the present disclosure can be employed, in accordance with various embodiments of the present disclosure. FIG. 3 provides an example diagram of an architecture for implementing dynamic frequency capping for targeted digital advertisements, in accordance with embodiments of the present disclosure. FIGS. 4 and 5 provide example diagrams of generating household graphs for implementing dynamic frequency capping for targeted digital advertisements, in accordance with embodiments of the present disclosure. FIG. 6 is a process flow showing a method for implementing dynamic frequency capping for targeted digital advertisements, in accordance with embodiments of the present disclosure. FIG. 7 is a process flow showing a method for implementing dynamic frequency capping for targeted digital advertisements, in accordance with embodiments of the present disclosure. FIG. 8 is a process flow showing a method for implementing dynamic frequency capping for targeted digital advertisements, in accordance with embodiments of the present disclosure. FIG. 9 is a block diagram of an example computing device in which embodiments of the present disclosure can be employed. DETAILED DESCRIPTION Various terms are used throughout the description of embodiments provided herein. A brief overview of such terms and phrases is provided here for ease of understanding, but more details of these terms and phrases is provided throughout. A “customer device” generally refers to an electronic device that is used by a customer where an application operating on the electronic initiates advertisement requests for targeted advertisements. For example, a customer device may refer to a personal computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a global posit