PREDICTIVE MODELING OF ATTRIBUTION
Methods, systems, and media for predictive modeling of online and offline attribution are disclosed. In one example, a system for predictive modeling of online and offline attribution comprises one or more databases comprising one or more inputs and one or more processors for receiving the one or more inputs, processing the one or more inputs using a general linear model, and providing predicted online and offline campaign impact.
1 . A computer-implemented method of predictive modeling of attribution, the method comprising the steps of:
receiving one or more inputs;
processing the one or more inputs using a general linear model; and
providing predicted online and offline campaign impact.
2 . The method of claim 1 , wherein the one or more inputs are selected from a group consisting of: real-time campaign data, audience profiles, attribution data, and combinations thereof.
3 . The method of claim 2 , wherein the real-time campaign data is selected from a group consisting of: opens, clicks, landing page actions, complaints, unsubscribes, metrics rates, rate of change of metric rates, datetime, and combinations thereof.
4 . The method of claim 2 , wherein the audience profiles are selected from a group consisting of: demographics, geographic, online sales, offline sales, psychographic, purchase intent data, and combinations thereof.
5 . The method of claim 2 , wherein the attribution data is selected from a group consisting of: advertiser customer data, treated prospects records, control prospects records, incremental customers, incremental customer rate, and combinations thereof.
6 . The method of claim 1 , wherein the one or more inputs are provided in real time.
7 . The method of claim 1 , wherein the general linear model weights the one or more inputs.
8 . The method of claim 1 , wherein the general linear model processes the one or more inputs by weighting the one or more inputs, wherein the one or more inputs are independent variables, to determine effects on a dependent variable.
9 . The method of claim 1 , wherein the general linear model determines influential factors.
10 . The method of claim 1 , wherein the predicted online and offline campaign impact is determined on a periodic basis.
11 . A system for predictive modeling of online and offline attribution, the system comprising:
one or more databases comprising one or more inputs; and
one or more processors for:
receiving the one or more inputs;
processing the one or more inputs using a general linear model; and
providing predicted online and offline campaign impact.
12 . The system of claim 11 , wherein the one or more inputs are selected from a group consisting of: real-time campaign data, audience profiles, attribution data, and combinations thereof.
13 . The system of claim 12 , wherein the real-time campaign data is selected from a group consisting of: opens, clicks, landing page actions, complaints, unsubscribes, metrics rates, rate of change of metric rates, datetime, and combinations thereof.
14 . The system of claim 12 , wherein the audience profiles are selected from a group consisting of: demographics, geographic, online sales, offline sales, psychographic, purchase intent data, and combinations thereof.
15 . The system of claim 12 , wherein the attribution data is selected from a group consisting of: advertiser customer data, treated prospects records, control prospects records, incremental customers, incremental customer rate, and combinations thereof.
16 . The system of claim 11 , wherein the one or more inputs are provided in real time.
17 . The system of claim 11 , wherein the general linear model weights the one or more inputs.
18 . The system of claim 11 , wherein the general linear model processes the one or more inputs by weighting the one or more inputs, wherein the one or more inputs are independent variables, to determine effects on a dependent variable.
19 . The system of claim 11 , wherein the general linear model determines influential factors.
20 . A non-transitory machine-readable medium, comprising instructions that, when read by a machine, cause the machine to perform operations comprising, at least:
receiving one or more inputs;
processing the one or more inputs using a general linear model; and
providing predicted online and offline campaign impact.