The rapid growth of online advertising in recent years, as well as the amount of recorded user data, have pushed marketers toward developing innovative techniques of measuring the effectiveness of advertisement channels. The diversity of channels has increased the chance of users' exposure as a marketing target and raised the complexity of customer journey by many folds. Figure 2 shows that a large part of a conversion value is sourced by multiple-touchpoints journeys in modern marketing models. The users’ behavior also is another variable that is pretty much dependent on various external factors at society or individual level. All the introduced uncertainties make it almost impossible to understand complex customer journeys and evaluate channels’ effectiveness by conventional methods. The necessity of a more in-depth analysis of the customer journey has provided the ground for the emergence of the second major category of attribution models called data-driven attribution. A successful attribution model has to take account of different journey aspects, such as journey length, sequence, diversity, user behavior dynamics, and as well, the non-converting journeys. Out of the two major attribution approaches, only data-driven attribution can deal with all the aspects of a real-life large business customer journey by leveraging the capacity of big data.