AttributionMachine LearningMarkovian ModelLead Scoring
Capitol Technology University
Since the early 2010s, many marketing channel attribution models have been discussed to allocate the marketing budget among marketing channels. While the goal of all the attribution models is to maximize marketing output, different attribution models introduced different concepts to assign conversions to marketing channels. However, prior studies did not measure the impact pending leads would have on total conversions. This research proposed an attribution model that incorporates the customer journeys of pending leads in the marketing pipeline. This quantitative study combines causal experimental, correlational, and comparative studies. This study developed a machine learning-based lead scoring model to find future expected conversions from pending leads. The future conversion combined with historically realized conversions were fed to the fourth-order Markov model to develop an attribution model. The comparative analysis of the proposed model to the existing probabilistic and rule-based attribution models showed that the proposed model results in a better return on marketing investment (ROMI). When the customer journey spans over a long period, the conversion pattern changes. The proposed model introduced a new aspect to investigate marketing attribution strategies to increase ROMI when the conversion pattern changes. In addition, this study introduced an attribution model evaluation framework that can be used to compare any channel attribution model. Marketing professionals can use the proposed attribution model to maximize their ROMI.