MEDIA SPEND OPTIMIZATION USING ENGAGEMENT METRICS IN A CROSS-CHANNEL PREDICTIVE MODEL
A series of techniques, methods, systems, and computer program products for advertising portfolio management is disclosed herein. More specifically, the herein disclosed techniques enable receiving data comprising a plurality of marketing stimulations, and receiving data comprising a plurality of engagement metrics. The received data is analyzed to determine a set of engagement weights associated with the engagement metrics. The determined engagement weights are in turn used to calculate the effectiveness of particular marketing stimulations through a set of marketing channels. Additional data in the form of measured responses (e.g., sales figures, survey results, etc.) are used to form a learning model wherein the learning model comprises one or more of, a stimulus-response predictor, a stimulus-engagement predictor, and an engagement-response predictor. The predictors can be combined into a cascade of models for determining the effectiveness of marketing stimulations on consumer engagement, and for determining effectiveness of marketing stimulations on measured responses.
1 . A system comprising:
a cross-channel correlator to receive data comprising a plurality of marketing stimulations and to receive data comprising a plurality of engagement metrics;
a weight determinator to determine from the marketing stimulations and the engagement metrics, a set of engagement weights associated with respective instances of the engagement metrics; and
a weight filter to calculate a first effectiveness value of a particular one of the marketing stimulations using the engagement weights.
2 . The system of claim 1 , wherein the cross-channel correlator is configurable to receive data comprising measured responses, and wherein the weight determinator is configurable to determine from the engagement metrics and the measured responses, a set of response weights associated with the measured responses.
3 . The system of claim 2 , wherein the weight filter is configurable to calculate a second effectiveness value of a particular one of the engagement metrics using the response weights.
4 . The system of claim 2 , further comprising a learning model formed from the marketing stimulations, the engagement metrics, and the measured responses.
5 . The system of claim 4 , wherein the learning model comprises a stimulus-response predictor, a stimulus-engagement predictor, and an engagement-response predictor.
6 . The system of claim 4 , wherein the learning model is configurable to predict a portion of a response in a second channel resulting from a stimulus in a first channel.
7 . The system of claim 4 , wherein the learning model is configurable to run a plurality of simulations to predict a portion of a response in a second channel resulting from a stimulus in a first channel.
8 . The system of claim 7 , wherein the learning model is configurable to vary the stimulus in the first channel and observe the response in the second channel for individual ones of the plurality of simulations.
9 . The system of claim 4 , further comprising a simulated model.
10 . The system of claim 9 , wherein the simulated model is configurable to generate one or more reports from a user scenario.
11 . The system of claim 1 , wherein the cross-channel correlator is configurable to determine a portion of aggregate responses that is not attributed to an aggregate stimuli.
12 . The system 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.
13 . A method comprising:
receiving, by a computer, first data records comprising a plurality of marketing stimulations;
receiving second data records comprising a plurality of engagement metrics;
determining, from the marketing stimulations and the engagement metrics, a set of engagement weights associated with the engagement metrics; and
calculating a first effectiveness value of a particular one of the marketing stimulations using the engagement weights.
14 . The method of claim 13 , further comprising:
receiving third data records comprising measured responses; and
determining, from the engagement metrics and the measured responses, a set of response weights associated with the measured responses.
15 . The method of claim 14 , further comprising calculating a second effectiveness value of a particular one of the engagement metrics using the response weights.
16 . The method of claim 14 , further comprising processing the marketing stimulations, the engagement metrics, and the measured responses to form a learning model.
17 . The method of claim 16 , wherein the learning model comprises a stimulus-response predictor, a stimulus-engagement predictor, and an engagement-response predictor.
18 . The method of claim 16 , further comprising predicting a portion of a response in a second channel resulting from a stimulus in a first channel.
19 . The method of claim 16 , wherein predicting a portion of a response in a second channel resulting from a stimulus in a first channel comprises running a plurality of simulations.
20 . The method of claim 19 , wherein individual ones of the plurality of simulations comprise varying the stimulus in the first channel and observing the response in the second channel.
21 . The method of claim 16 , further comprising outputting a simulated model.
22 . The method of claim 21 , further comprising generating one or more reports from a user scenario.
23 . The method of claim 13 , further comprising determining a portion of aggregate responses that is not attributed to an aggregate stimuli.
24 . The method of claim 13 , 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.
25 . 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;
receiving data comprising a plurality of engagement metrics;
determining, from the marketing stimulations and the engagement metrics, a set of engagement weights associated with the engagement metrics; and
calculating a first effectiveness value of a particular one of the marketing stimulations using the engagement weights.
26 . The computer program product of claim 25 , further comprising instructions for:
receiving data comprising measured responses; and
determining, from the engagement metrics and the measured responses, a set of response weights associated with the measured responses.