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There is a framework to make decisions using Markov chains and studying the removal affect.  We built this last year in our effort to make sense of what Ben is talking about.

 

Every customer journey (sequence of channels/touchpoints) is represented as a chain in a Markov graph where each node is a state (channel/touchpoint) and each arrowed link between nodes represents the probability of transition between states.

By computing the model and estimating transition probabilities we can attribute every channel/touchpoint. The probability of conversion of the complete model is 33.3% (0.667 * 0.5 * 1 * 0.5 + 0.333 * 1 * 0.5)

The last step is to estimate the impact of each channel/touchpoint on our final conversion. Here, we apply the principle of Removal Effect for analysis. The core of Removal Effect is to measure how many conversions (or percentage of conversions) would be lost if we remove one of those channels. In our simple example, suppose we take out C1.

Probability of conversion after removing C1 is 16.7% (0.333 * 1 * 0.5) or a 50% reduction