# US 2014/0195339 A1 · Media Mix Modeling Tool

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- Number: US 2014/0195339 A1
- Document ID: US20140195339A1
- Status: Published application
- Kind: A1
- Application number: 13/736,710
- Filed: 2013-01-08
- Published: 2014-07-10
- Earliest claimed: 2013-01-08
- Assignees: Adobe Systems Incorporated
- Applicants: Adobe Systems Incorporated
- Inventors: Trevor H. Paulsen; Jessica L. Langford; Jared A. Lees
- Web page: https://patentgrep.com/patent/US20140195339A1

## CPC

- G06Q 30/0247: {Calculate past, present or future revenues}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A media mix modeling tool is configured to enable a marketing budget to be analyzed for purposes of allocation across different marketing channels and campaigns. The media mix modeling tool utilizes and builds upon web analytics data. For example, for particular channels that are to be the subject of a marketing investment, web analytics data is gathered for each channel. A statistical attribution method is then utilized to analyze the web analytics data to determine how much revenue should be attributed to each channel based on various touch points for each campaign. Cost data is then utilized to create a plot of campaigns within a particular channel. From this plot, a model is fitted that describes the performance of the particular channel. Once a model has been fit to each individual channel, a solver is applied to find a desirable or optimal way to distribute the marketing budget.

## Claims

### Claim 1

A method implemented by a computing device, the method comprising:
receiving web analytics data associated with an entity that utilizes online or Internet-based marketing channels;
attributing revenue to the individual marketing channels;
receiving cost data associated with the individual marketing channels;
creating scatter plots of one or more campaigns within each marketing channel;
fitting a curve to data in each scatterplot for each channel; and
using fitted curves to compute a distribution for marketing budget allocation.

### Claim 2 (depends on 1)

A method as described in claim 1, wherein said attributing is performed by statistically attributing revenue to the individual marketing channels.

### Claim 3 (depends on 1)

A method as described in claim 1, wherein said attributing is performed by using a Bayesian estimator to attribute revenue.

### Claim 4 (depends on 1)

A method as described in claim 1, wherein said receiving cost data is performed by receiving the cost data via a user interface.

### Claim 5 (depends on 1)

A method as described in claim 1, wherein said receiving cost data is performed by receiving the cost data via a user interface that includes an expected return portion that is configured to provide, for each marketing channel, an expected return for a particular amount of money spent in a respective channel.

### Claim 6 (depends on 5)

A method as described in claim 5, wherein the expected return portion is configured to show the expected return for input received via the user interface and the expected return for an optimally-positioned marketing budget allocation.

### Claim 7 (depends on 5)

A method as described in claim 5, wherein the expected return portion is configured to show the expected return for input received via the user interface and the expected return for an optimally-positioned marketing budget allocation, and wherein the user interface further includes a graphical breakdown portion configured to illustrate a graphical breakdown of the expected return for input received via the user interface and the expected return for the optimally-positioned marketing budget allocation.

### Claim 8 (depends on 1)

A method as described in claim 1, wherein said receiving cost data is performed by receiving the cost data via a user interface that includes an optimized spend portion configured to illustrate a statistically optimized spend per marketing channel.

### Claim 9 (depends on 1)

A method as described in claim 1, wherein said receiving cost data is performed by receiving the cost data via a user interface that includes a graphical portion that illustrates per dollar return versus total expected return.

### Claim 10 (depends on 1)

A method as described in claim 1, wherein said receiving cost data is performed by receiving the cost data via a user interface that includes a graphical portion that illustrates per dollar return versus total expected return and which includes a curve that is a combination of the curves generated for all of the marketing channels.

### Claim 11 (depends on 1)

A method as described in claim 1, wherein said fitting a curve comprises utilizing a curve that has a decaying return.

### Claim 12 (depends on 1)

A method as described in claim 1, wherein receiving web analytics data comprises receiving web analytics data that includes information across a variety of channels and different campaigns within individual channels.

### Claim 13

One or more computer-readable storage media comprising instructions that are stored thereon that, responsive to execution by a computing device, causes the computing device to perform operations comprising:
receiving web analytics data associated with an entity that utilizes online or Internet-based marketing channels; and
using cost data associated with the marketing channels and the web analytics data to compute a statistically optimized marketing budget across the marketing channels.

### Claim 14 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein the marketing channels include one or more of: search engine optimization, pay per click campaigns, social media marketing, affiliate marketing, shopping channel management, mobile marketing, video marketing, e-mail marketing, display advertising, or online PR and article marketing.

### Claim 15 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises statistically attributing revenue to each marketing channel.

### Claim 16 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel.

### Claim 17 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel and computing, from the curves, the statistically optimized marketing budget.

### Claim 18 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel, wherein the curve comprises a log curve.

### Claim 19 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel, wherein the curve comprises an S-shaped curve.

### Claim 20 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel, wherein the curve includes parameters that can be defined by a user.

### Claim 21 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel, wherein said curve is selectable from a plurality of curves.

### Claim 22 (depends on 13)

One or more computer-readable storage media as described in claim 13, wherein said using comprises modeling each marketing channel with a curve that provides an indication of expected return for money spent within an associated channel, wherein said curve comprises a flexibility parameter to enable expression of a flexibility value that places constraints on how much is to be spent on a particular channel or how much a particular channel's spend allocation is to change.

### Claim 23

A computing device comprising:
one or more processors;
one or more computer readable storage media embodying computer-readable instructions which, when executed under the influence of the one or more processors, implement a user interface comprising:
a data input portion configured to enable a user to input marketing budget amounts associated with individual online or Internet-based marketing channels effective to enable the marketing budget amounts to be analyzed with web analytics data to compute a statistically optimized marketing budget across the marketing channels; and
an expected return portion that is configured to provide, for each marketing channel, an expected return for a particular amount of money spent in a respective channel.

### Claim 24 (depends on 23)

The computing device of claim 23, wherein the expected return portion is configured to show the expected return for input received via the data input portion and the expected return for an optimally-positioned marketing budget allocation.

### Claim 25 (depends on 23)

The computing device of claim 23, wherein the expected return portion is configured to show the expected return for input received via the data input portion and the expected return for an optimally-positioned marketing budget allocation, and
wherein the user interface further includes a graphical breakdown portion configured to illustrate a graphical breakdown of the expected return for input received via the data input portion and the expected return for the optimally-positioned marketing budget allocation.

### Claim 26 (depends on 23)

The computing device of claim 23, wherein the user interface includes an optimized spend portion configured to illustrate a statistically optimized spend per marketing channel.

### Claim 27 (depends on 23)

The computing device of claim 23, wherein the user interface includes a graphical portion that illustrates per dollar return versus total expected return.

### Claim 28 (depends on 23)

The computing device of claim 23, wherein the user interface includes a graphical portion that illustrates per dollar return versus total expected return and which includes a curve that is a combination of curves generated for all of the marketing channels.

### Claim 29

One or more computer-readable storage media comprising instructions that are stored thereon that, responsive to execution by a computing device, causes the computing device to implement a system comprising:
a data gathering module configured to receive web analytics data associated with an entity that utilizes online or Internet-based marketing channels;
a statistical attribution module configured to attribute revenue to the individual marketing channels;
a user interface/dashboard module configured to receive cost data associated with the individual marketing channels, create scatter plots of one or more campaigns within each marketing channel, and fit a curve to data in each scatter plot for each channel; and
a solver module configured to use the fitted curves to compute a distribution for marketing budget allocation.

### Claim 30 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said statistical attribution module comprises a Bayesian estimator.

### Claim 31 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises an expected return portion that is configured to provide, for each marketing channel, an expected return for a particular amount of money spent in a respective channel.

### Claim 32 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises an expected return portion that is configured to provide, for each marketing channel, an expected return for a particular amount of money spent in a respective channel, wherein the expected return portion is configured to show the expected return for received cost data and the expected return for an optimally-positioned marketing budget allocation.

### Claim 33 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises an expected return portion that is configured to provide, for each marketing channel, an expected return for a particular amount of money spent in a respective channel, wherein the expected return portion is configured to show the expected return for received cost data and the expected return for an optimally-positioned marketing budget allocation, and wherein the user interface/dashboard module further includes a graphical breakdown portion configured to illustrate a graphical breakdown of the expected return for received cost data and the expected return for the optimally-positioned marketing budget allocation.

### Claim 34 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises an optimized spend portion configured to illustrate a statistically optimized spend per marketing channel.

### Claim 35 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises a graphical portion that illustrates per dollar return versus total expected return.

### Claim 36 (depends on 29)

The one or more computer-readable storage media of claim 29, wherein said user interface/dashboard module comprises a graphical portion that illustrates per dollar return versus total expected return, and a curve that is a combination of curves generated for all of the marketing channels.