Abstract: A system integrates activity data and includes a processor to obtain a plurality of activity data of consumer data points with data channels from different data sources. The obtained plurality of activity data includes non-uniformed data formats and with data properties based on a plurality of data property definitions. A set of data buckets is determined, and the processor further classifies each of the plurality of activity data into the determined data buckets. The processor further reorganizes each of the plurality of activity data. The processor further stitches the plurality of activity data in the determined set of data buckets. The system further includes the processor to create a unified marketing interaction table (UMIT) for analysis on the data properties of the stitched plurality of activity data.

Abstract: Customer targeted advertising may be efficiently planned based on different demographic and behavioral segments. A method may use event-level data to model conversion attribution based on different dimensions. These dimensions may include channel, content, placement and every other aspect of advertising. Multiple population segments of users may be identified and attribution modeling performed independently for each population segment. The attribution modeling may be implemented in a plurality of approaches such as independent or coupled modeling. Coupled attribution modeling may be capable of modeling the combined effect of multiple dimensions. The results of attribution modeling may then be used to plan optimum advertising placement with the highest attribution for each population segment.