Heuristic attribution models based on thumb rules and gut-feels, though easy to implement, are relatively inaccurate. For a more robust approach, marketers should consider probabilistic attribution model. These models provide a better assessment of the marketing channels that drive sales.
One approach to a probabilistic-based solution is by means of the Markov chain, a stochastic model describing a sequence of possible events. It well suited for modelling customer journeys that follow a chain of linked events. The model assumes that what happens next in the chain of events depends only on the current state of the system.
A customer journey may be viewed as a sequence of touchpoints linked in a chain where the nodes represent marketing initiative or marketing channel, and the links represent the probability of transition between the initiatives/channels.
Markov chains are increasingly used for the attribution of sales across marketing channels.
Two paths lead to conversion:$$Conversion \, probability = (0.67×0.5×1×0.5)+(0.33×1×0.5)=33%$$
In this example, channel C1 influences one of the conversions (50% of the total), whereas C2 and C3 lie in the path of all conversions (100%). Therefore, C2 and C3 have double the weight of C1, and the Markov accordingly attributes channels C1, C2 and C3 in the ratio 20%, 40%, and 40% respectively.
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