Patent Application

App. No. 14/551,212

Abstract

Claims (74)

1 . A method for temporal budget optimization in online advertising, the method comprising using at least one hardware processor for:

receiving a user selection of a time period in the future and of a joint budget for M online ad entities, wherein M≧2;

forecasting, based on historical data associated with the M online ad entities, a future return on investment (ROI) function of each of the M online ad entities, wherein the future ROI function provides revenue as a function of cost;

computing individual budgets for the M online ad entities for the time period, by finding M points to serve as the individual budgets, each of the M points being a certain cost in the future ROI function of a different one of the M online ad entities, such that:

(i) the M points have approximately equal derivatives, and

(ii) a sum of the costs at the M points is approximately equal to the joint budget; and

during the time period:

(a) tracking a spending of the individual budgets, to determine remaining individual budgets,

(b) periodically updating the future ROI functions and the individual budgets, based on newly-accumulated historical data associated with the M online ad entities and on the remaining individual budgets, and

(c) periodically adjusting, in an online advertising platform, a spending pace of the remaining individual budgets, wherein the adjusting is based on the updated future ROI functions and the updated individual budgets.

2 . The method according to claim 1 , wherein the forecasting of the future ROI function of each of the M online ad entities comprises:

fetching the historical data associated with the M online ad entities, wherein the historical data comprises historical cost time-series and historical revenue time-series;

correlating the historical revenue time-series to the historical cost time-series, to produce correlated historical data; and

applying a nonlinear curve fitting algorithm to the correlated historical data, to produce a nonlinear function approximately descriptive of the correlated historical data, wherein, in the nonlinear function, revenue is a function of cost, and wherein the nonlinear function is the future ROI function.

3 . The method according to claim 2 , further comprising using the at least one hardware processor for computing error bounds of the nonlinear function, based on residuals of the application of the nonlinear curve fitting algorithm to the correlated historical data.

4 . The method according to claim 1 , wherein the time period is selected from the group consisting of: up to a week, up to multiple weeks, up to a month and up to multiple months.

5 . The method according to claim 1 , further comprising using the at least one hardware processor for receiving a schedule of one or more future business events expected to occur during the time period, wherein the adjusting is further based on the schedule.

6 . The method according to claim 5 , wherein the receiving of the schedule comprises receiving a business prediction as to each of the one or more future business events.

7 . The method according to claim 1 , wherein the adjusting of the spending pace of the remaining individual budgets comprises adjusting bids associated with at least one of the M online ad entities.

8 . The method according to claim 1 , wherein the M online ad entities are each selected from the group consisting of: an individual ad, a group of ads, a campaign and a set of campaigns.

9 . A computer program product for temporal budget optimization in online advertising, the computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor for:

receiving a user selection of a time period in the future and of a joint budget for M online ad entities, wherein M≧2;

forecasting, based on historical data associated with the M online ad entities, a future return on investment (ROI) function of each of the M online ad entities, wherein the future ROI function provides revenue as a function of cost;

computing individual budgets for the M online ad entities for the time period, by finding M points to serve as the individual budgets, each of the M points being a certain cost in the future ROI function of a different one of the M online ad entities, such that:

(i) the M points have approximately equal derivatives, and

(ii) a sum of the costs at the M points is approximately equal to the joint budget; and

during the time period:

(a) tracking a spending of the individual budgets, to determine remaining individual budgets,

(b) periodically updating the future ROI functions and the individual budgets, based on newly-accumulated historical data associated with the M online ad entities and on the remaining individual budgets, and

(c) periodically adjusting, in an online advertising platform, a spending pace of the remaining individual budgets, wherein the adjusting is based on the updated future ROI functions and the updated individual budgets.

10 . The computer program product according to claim 9 , wherein the forecasting of the future ROI function of each of the M online ad entities comprises:

fetching the historical data associated with the M online ad entities, wherein the historical data comprises historical cost time-series and historical revenue time-series;

correlating the historical revenue time-series to the historical cost time-series, to produce correlated historical data; and

applying a nonlinear curve fitting algorithm to the correlated historical data, to produce a nonlinear function approximately descriptive of the correlated historical data, wherein, in the nonlinear function, revenue is a function of cost, and wherein the nonlinear function is the future ROI function.

11 . The computer program product according to claim 10 , wherein the program code is further executable by said at least one hardware processor for computing error bounds of the nonlinear function, based on residuals of the application of the nonlinear curve fitting algorithm to the correlated historical data.

12 . The computer program product according to claim 9 , wherein the time period is selected from the group consisting of: up to a week, up to multiple weeks, up to a month and up to multiple months.

13 . The computer program product according to claim 9 , wherein the program code is further executable by said at least one hardware processor for receiving a schedule of one or more future business events expected to occur during the time period, wherein the adjusting is further based on the schedule.

14 . The computer program product according to claim 13 , wherein the receiving of the schedule comprises receiving a business prediction as to each of the one or more future business events.

15 . The computer program product according to claim 9 , wherein the adjusting of the spending pace of the remaining individual budgets comprises adjusting bids associated with at least one of the M online ad entities.

16 . The computer program product according to claim 9 , wherein the M online ad entities are each selected from the group consisting of: an individual ad, a group of ads, a campaign and a set of campaigns.

17 . A system for temporal budget optimization in online advertising, the system comprising:

at least one hardware processor; and

a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor for:

receiving a user selection of a time period in the future and of a joint budget for M online ad entities, wherein M≧2;

forecasting, based on historical data associated with the M online ad entities, a future return on investment (ROI) function of each of the M online ad entities, wherein the future ROI function provides revenue as a function of cost;

computing individual budgets for the M online ad entities for the time period, by finding M points to serve as the individual budgets, each of the M points being a certain cost in the future ROI function of a different one of the M online ad entities, such that:

(i) the M points have approximately equal derivatives, and

(ii) a sum of the costs at the M points is approximately equal to the joint budget; and

during the time period:

(a) tracking a spending of the individual budgets, to determine remaining individual budgets,

(b) periodically updating the future ROI functions and the individual budgets, based on newly-accumulated historical data associated with the M online ad entities and on the remaining individual budgets, and

(c) periodically adjusting, in an online advertising platform, a spending pace of the remaining individual budgets, wherein the adjusting is based on the updated future ROI functions and the updated individual budgets.

18 . The system according to claim 17 , wherein the forecasting of the future ROI function of each of the M online ad entities comprises:

fetching the historical data associated with the M online ad entities, wherein the historical data comprises historical cost time-series and historical revenue time-series;

correlating the historical revenue time-series to the historical cost time-series, to produce correlated historical data; and

applying a nonlinear curve fitting algorithm to the correlated historical data, to produce a nonlinear function approximately descriptive of the correlated historical data, wherein, in the nonlinear function, revenue is a function of cost, and wherein the nonlinear function is the future ROI function.

19 . The system according to claim 18 , wherein the program code is further executable by said at least one hardware processor for computing error bounds of the nonlinear function, based on residuals of the application of the nonlinear curve fitting algorithm to the correlated historical data.

20 . The system according to claim 17 , wherein the time period is selected from the group consisting of: up to a week, up to multiple weeks, up to a month and up to multiple months.

21 . The system according to claim 17 , wherein the program code is further executable by said at least one hardware processor for receiving a schedule of one or more future business events expected to occur during the time period, wherein the adjusting is further based on the schedule.

22 . The system according to claim 21 , wherein the receiving of the schedule comprises receiving a business prediction as to each of the one or more future business events.

23 . The system according to claim 17 , wherein the adjusting of the spending pace of the remaining individual budgets comprises adjusting bids associated with at least one of the M online ad entities.

24 . The system according to claim 17 , wherein the M online ad entities are each selected from the group consisting of: an individual ad, a group of ads, a campaign and a set of campaigns.

25 . A method for temporal budget optimization in online advertising, the method comprising using at least one hardware processor for:

receiving a user selection of a time period in the future;

computing an optimal budget distribution between M online ad entities, wherein said computing comprises:

(a) forecasting, based on historical data associated with the M online ad entities, future return on investment (ROI) functions of the M online ad entities for the time period, wherein each of the future ROI functions provides revenue as a function of cost,

(b) finding M points to serve as the individual budgets, each of the M points being on a certain cost in the future ROI function of a different one of the M online ad entities, such that the M points have approximately equal derivatives;

forecasting, based on the M points found, a joint future ROI function of the M online ad entities;

receiving a user selection of a certain point on a graph of the future joint ROI function, and setting a cost at the certain point as a joint budget for the M online ad entities for the time period;

determining the individual budgets based on the user selection of the point on the graph of the future joint ROI function; and

during the time period:

(c) tracking a spending of the individual budgets, to determine remaining individual budgets,

(d) periodically updating the future ROI functions and the individual budgets, based on newly-accumulated historical data associated with the M online ad entities and on the remaining individual budgets, and

(e) periodically adjusting, in an online advertising platform, a spending pace of the remaining individual budgets, wherein the adjusting is based on the updated future ROI functions and the updated individual budgets.

Assignments (8)