MEDIA SPEND OPTIMIZATION USING A CROSS-CHANNEL PREDICTIVE MODEL
A method, system, and computer program product for advertising portfolio management. The method form processes steps for determining effectiveness of marketing stimulations in a plurality of marketing channels included in a marketing campaign. The method commences upon receiving data comprising a plurality of marketing stimulations and respective measured responses, then determining from the marketing stimulations and the respective measured responses, a set of cross-channel weights to apply to the respective measured responses, where the cross-channel weights are indicative of the influence that a particular stimulation applied to a first channel has on the measure responses of other channels. The cross-channel weights are used in calculating the effectiveness of a particular marketing stimulation over an entire marketing campaign. The marketing campaign can comprise stimulations quantified as a number of direct mail pieces, a number or frequency of TV spots, a number of web impressions, a number of coupons printed, etc.
1 . A computer-implemented method for determining effectiveness of marketing stimulations in a plurality of marketing channels, the computer-implemented method comprising:
receiving data comprising a plurality of marketing stimulations and respective measured responses;
determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and
calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.
2 . The method of claim 1 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed.
3 . The method of claim 1 , further comprising processing the marketing stimulations and respective measured responses to form a learning model.
4 . The method of claim 3 , further comprising using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel.
5 . The method of claim 4 wherein using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel comprises running a plurality of simulations.
6 . The method of claim 5 wherein individual ones of the plurality of simulations comprise varying the stimulus in a first channel and observing the response in the second channel.
7 . The method of claim 5 , further comprising outputting a simulated model.
8 . The method of claim 7 , further comprising using the simulated model to generate one or more reports based on a user scenario.
9 . The method of claim 1 , further comprising determining a portion of aggregate response that is not attributed to aggregate stimulus.
10 . A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process, the process comprising:
receiving data comprising a plurality of marketing stimulations and respective measured responses;
determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and
calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.
11 . The computer program product of claim 10 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed.
12 . The computer program product of claim 10 , further comprising instructions for processing the marketing stimulations and respective measured responses to form a learning model.
13 . The computer program product of claim 12 , further comprising instructions for using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel.
14 . The computer program product of claim 13 wherein using the learning model to predict a portion of a response in a second channel resulting from a stimulus in a first channel comprises running a plurality of simulations.
15 . The computer program product of claim 14 wherein individual ones of the plurality of simulations comprise varying the stimulus in a first channel and observing the response in the second channel.
16 . The computer program product of claim 15 , further comprising instructions for outputting a simulated model.
17 . The computer program product of claim 16 , further comprising instructions for using the simulated model to generate one or more reports based on a user scenario.
18 . The computer program product of claim 10 , further comprising determining a portion of aggregate response that is not attributed to aggregate stimulus.
19 . A computer system comprising:
a computer processor to execute a set of program code instructions; and
a memory to hold the program code instructions, in which the program code instructions comprises program code to perform,
receiving data comprising a plurality of marketing stimulations and respective measured responses;
determining, from the marketing stimulations and the respective measured responses, cross-channel weights to apply to the respective measured responses; and
calculating an effectiveness value of a particular one of the marketing stimulations using the cross-channel weights.
20 . The computer system of claim 19 , wherein the marketing stimulations comprise at least one of, an advertising spend, a number of direct mail pieces, a number of TV spots, a number of radio spots, a number of web impressions, and a number of coupons printed.