DATA ATTRIBUTION PIPELINE
Aspects of the present disclosure provides systems and methods that analyze data transmissions and location data to measure and optimize data distribution campaigns’ effectiveness in driving foot traffic to venues in real-time. For example, data related to impression events can be received from a distributed set of data stores and/or distributed using different data networks. Subsequently, location information can be analyzed to determine visits to venues associated with the impression event. In further embodiments, data can be collected from different transaction sources and associated with impression events and location information. The collected data can be normalized and segmented to determine the effectiveness of the impression event.
1 . A method for analyzing data from disparate data sources and networks using an attribution pipeline, the method comprising:
receiving data from one or more disparate data sources and data networks;
determine visit information, wherein the visit information is determined based upon data received one or more mobile devices associated with a user base;
aggregate transaction data from a plurality of different transaction sources;
generate normalized data by normalizing visit information, transaction data, and users associated with the visit information and transaction information;
generate an analysis report based upon the normalized data; and
providing the analysis report.
2 . The method of claim 1 , further comprising generating a plurality of scores for a plurality of impression events based upon the normalized data.
3 . The method of claim 2 , further comprising generating at least one projection weight, wherein the at least one projection weight is used to generate a weighted plurality of scores based upon the generated plurality of scores.
4 . The method of claim 3 , wherein generating at least one projection further comprises:
determining a first segment exposed to an impression event on only a first channel;
determining a second segment exposed to the impression event on only a second channel;
determining a third segment exposed to the impression event on both the first channel and the second channel;
calculating weights for the first, second, and third segments by applying such that applying the weights to census-weighted users in each segment causes total weighted impressions for each channel to match respective target impression counts while minimizing distortion of the normalized data.
5 . The method of claim 1 , wherein the analysis report is provided in a data file.
6 . The method of claim 1 , wherein the analysis report is provided via a portal accessible via a network.
7 . The method of claim 1 , further comprising segmenting the normalized data into at least two groups.
8 . The method of claim 1 , wherein the at least two groups include a control group and a treatment group.
9 . A system comprising:
at least one processor; and
memory encoding computer executable instruction that, when executed by the at least two processors, perform a method for analyzing data from disparate data sources and networks using an attribution pipeline, the method comprising:
receiving data from one or more disparate data sources and data networks;
determine visit information, wherein the visit information is determined based upon data received one or more mobile devices associated with a user base;
aggregate transaction data from a plurality of different transaction sources;
generate normalized data by normalizing visit information, transaction data, and users associated with the visit information and transaction information;
generate an analysis report based upon the normalized data; and
providing the analysis report.
10 . The system of claim 9 , wherein the method further comprises generating a plurality of scores for a plurality of impression events based upon the normalized data.
11 . The system of claim 10 , wherein the method further comprises generating at least one projection weight, wherein the at least one projection weight is used to generate a weighted plurality of scores based upon the generated plurality of scores.
12 . The system of claim 11 , wherein the plurality of scores are determined based upon a determined conversion window for one or more impression event of the plurality of impression events.
13 . The system of claim 9 , wherein the analysis report is provided in a data file.
14 . The system of claim 9 , wherein the analysis report is provided via a portal accessible via a network.
15 . The system of claim 9 , wherein the method further comprises segmenting the normalized data into at least two groups.
16 . The system of claim 15 , wherein the at least two groups include a control group and a treatment group.
17 . A non-transitory computer readable medium encoding computer executable instructions that, when executed by at least one processor, perform a method comprising:
receiving data from one or more disparate data sources and data networks;
determine visit information, wherein the visit information is determined based upon data received one or more mobile devices associated with a user base;
aggregate transaction data from a plurality of different transaction sources;
generate normalized data by normalizing visit information, transaction data, and users associated with the visit information and transaction information;
generate an analysis report based upon the normalized data; and
providing the analysis report.
18 . The non-transitory computer readable medium of claim 17 , wherein the method further comprises generating a plurality of scores for a plurality of impression events based upon the normalized data.
19 . The non-transitory computer readable medium of claim 18 , wherein the method further comprises generating at least one projection weight, wherein the at least one projection weight is used to generate a weighted plurality of scores based upon the generated plurality of scores.
20 . The non-transitory computer readable medium of claim 19 , wherein the plurality of scores are determined based upon a determined conversion window for one or more impression event of the plurality of impression events.