Markov Chain — Probabilistic Data-Driven Attribution

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.

Exhibit 20.9 depicts a set of customer journeys across channels. As shown in Exhibit 20.10, these three journeys can be modelled as a Markov chain with six different states: start, C1, C2, C3, × (null) and √ (conversion).

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.