Methods and apparatus to improve reach calculation efficiency

Patent metadata

Field	Value
Publication number	US-2017061470-A1
Application number	US-201514984310-A
Country	US
Kind code	A1
Filing date	Dec 30, 2015
Priority date	Aug 31, 2015
Publication date	Mar 2, 2017
Grant date	—

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to improve reach calculation efficiency. An example method includes estimating, with a processor, a sample distribution of marketing data to generate a maximum entropy distribution, generating, with the processor, a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data, and improving calculation efficiency of the public reach of the sample distribution of marketing data by generating, with the processor, conserved quantity expressions of the geometric distribution.

First claim

What is claimed is:

1 . A computer-implemented method to improve calculation efficiency of a published reach, comprising:
estimating, with a processor, a sample distribution of marketing data to generate a maximum entropy distribution; generating, with the processor, a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data; and improving calculation efficiency of the public reach of the sample distribution of marketing data by generating, with the processor, conserved quantity expressions of the geometric distribution.

2 . The computer-implemented method as defined in claim 1 , further including calculating a first gross rating point (GRP) value and a first reach value based on the first GRP value.

3 . The computer-implemented method as defined in claim 2 , further including constraining the maximum entropy distribution with the first GRP value and the first reach value during estimation of the sample distribution of marketing data.

4 . The computer-implemented method as defined in claim 3 , wherein the first GRP value and the first reach value are constrained for a probability of zero advertising impressions associated with the sample distribution.

5 . The computer-implemented method as defined in claim 1 , further including constraining the minimum cross entropy with a candidate gross-rating-point (GRP) of the sample distribution.

6 . The computer-implemented method as defined in claim 5 , wherein the candidate GRP is based on an advertising campaign increase of the marketing plan.

7 . The computer-implemented method as defined in claim 1 , wherein estimating the minimum cross entropy includes applying a Kullback-Leibler divergence probability.

8 . The computer-implemented method as defined in claim 1 , wherein the conserved quantity expressions associate at least one of (a) gross-rating-point (GRP), and reach, (b) GRP and frequency, or (c) reach and frequency.

9 . An apparatus to improve calculation efficiency of a published reach, comprising:
a maximum entropy engine to generate a maximum entropy distribution; a maximum entropy constraint manager to generate a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data; and a conserved quantity engine to improve calculation efficiency of the public reach of the sample distribution of marketing data by generating conserved quantity expressions of the geometric distribution.

10 . The apparatus as defined in claim 9 , further including:
a gross-rating-point (GRP) engine to calculate a first GRP value; and a reach engine to calculate a first reach value based on the first GRP value.

11 . The apparatus as defined in claim 10 , wherein the maximum entropy engine is to constrain the maximum entropy distribution with the first GRP value and the first reach value during estimation of the sample distribution of marketing data.

12 . The apparatus as defined in claim 11 , wherein the maximum entropy engine is to constrain the first GRP value and the first reach value for a probability of zero advertising impressions associated with the sample distribution.

13 . The apparatus as defined in claim 9 , further including a minimum cross-entropy constraint manager to constrain the minimum cross entropy with a candidate gross-rating-point (GRP) of the sample distribution.

14 . The apparatus as defined in claim 13 , wherein the candidate GRP is based on an advertising campaign increase of the marketing plan.

15 . The apparatus as defined in claim 9 , further including a minimum cross-entropy engine to estimate the minimum cross entropy with a Kullback-Leibler divergence probability.

16 . The apparatus as defined in claim 9 , wherein the conserved quantity expressions associate at least one of (a) gross-rating-point (GRP), and reach, (b) GRP and frequency, or (c) reach and frequency.

17 . A tangible computer readable storage medium comprising instructions that, when executed, causes a processor to, at least:
estimate a sample distribution of marketing data to generate a maximum entropy distribution; generate a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data; and improve calculation efficiency of the public reach of the sample distribution of marketing data by generating conserved quantity expressions of the geometric distribution.

18 . The machine-readable instructions of claim 17 , wherein the instructions, when executed, further cause the processor to calculate a first gross-rating-point (GRP) value and a first reach value based on the first GRP value.

19 . The machine-readable instructions of claim 18 , wherein the instructions, when executed, further cause the processor to constrain the maximum entropy distribution with the first GRP value and the first reach value during estimation of the sample distribution of marketing data.

20 . The machine-readable instructions of claim 19 , wherein the instructions, when executed, further cause the processor to constrain the first GRP value and the first reach value for a probability of zero advertising impressions associated with the sample distribution.

Assignees

• Nielsen Co Us Llc

Inventors

• Sheppard Michael
• Sullivan Jonathan
• Lipa Peter
• Doe Peter Campbell
• Donato Paul

Classifications

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title

• G06Q30/0244 Primary

  Optimization · CPC title

• G06Q30/0242

  Determining effectiveness of advertisements · CPC title