Abstract: A method for identifying causal impact of introducing digital channels and enhancements, the method comprising identifying digital channels associated with online monitoring of interactions with consumers by an advertiser, identifying online metrics for assessment associated with the digital channels, determining a Bayesian time-series model for the data based on the identified online metrics, analyzing causality impact of an intervention for one or more paid digital and organic digital channels using the Bayesian time-series model, matching the advertiser to a classification code by using a data append function via an application program interface (API) or by correlating bid keywords associated with the advertiser to the classification code, benchmarking the causality impact against peer advertisers based on the classification code, and generating a report including a causality assessment of the online metrics for the one or more paid digital and organic digital channels based on the causality impact and the

Abstract: A method for identifying causal impact of introducing digital channels and enhancements, the method comprising receiving data corresponding to online interactions with consumers based on online monitoring of an advertiser, identifying digital channels associated with the online interactions with consumers, identifying online metrics for assessment associated with the digital channels, determining a Bayesian time-series model for the data based on the identified online metrics, analyzing causality impact of an intervention for one or more paid digital and organic digital channels using the Bayesian time-series model, identifying optimal pre- and post-intervention assessment time periods based on causality impact of the one or more paid digital and organic digital channels that is statistically significant, and generating a report including a causality assessment of the online metrics for the one or more paid digital and organic digital channels by period based on the causality impact during the optimal pre- an