Towards better attribution models

1. Towards better Attribution Models Thor Whalen, Data Scientist, April 2015 https://www.linkedin.com/in/thorwhalen/

2. Attribution Though more of 75% of the marketers are still using single-source attribution (mostly first or last click), the industry is maturing, and the need for better attribution models is being felt. last (taken from the Google and eConsultancy study) Indeed, if we compare the channels that contribute to sales, first touch, last touch, and all touch perspectives give us very different views. A first touch is important because it initiates a customer journey. Last touch is important because it’s the one that eventually “made the sale”. But really, all intermediate points of exposure and contact contributed to this sale. first position decay So who do we attribute the sale to? Many different attribution “models” are used on the market. Some are single channel (such as first click, last click, or post view. versus linear versus Others (“fractional attribution”) distributed the attribution to all touch points using some static rule based on an arbitrary heuristic (if even that!)

3. Algorithmic Attribution and Shapley Algorithmic attribution attempts to compute attribution based on a scientific methodology drawing it’s facts from data. Yet the term is often abused, and applied to any attribution that uses “some equation” The Shapley value is probably one of the most mathematically sound approaches to attribution on the market at the moment. In fact, it is also being used in other sectors where reward must be aligned to value. One of these sectors is team sports. When a football team scores a goal, it is the team that scores, not the player who touched the ball last. Assigning the entire value of the goal to the last-touch player would lead to inappropriate rewards, planning and strategies. The Shapley value of every unit is measured as an aggregate of its contribution to every possible combinations of other units, which mathematically can be written: • The Shapley method is not without it’s disadvantages though. Some choices must be made and problems solved, in order to carry out a Shapley analysis correctly. Namely: • Value Definition: Define the value of a “team” of channels in a way that is aligned with the business objectives • Scalability: The number of combinations grows exponentially with the number of channels • Sparsity: We can’t always gather enough data on every combination of channels • Coarseness: A Shapley output will be a distribution of attribution over various channels. But each individual customer journey may be more or less effected by the channels – A Shapley attribution will be insensitive to this. shapley

4. Engagement Based Attribution The engagement based attribution approach takes more granular view of what’s happening behind the scenes. Users are exposed to marketing messages, may react to some, and have more or less activity as a result. Consider instead an engagement based attribution we’ll coin as Propensity-delta Attribution. The driving idea is to attribute according to the influence that marketing messages and resulting activities have on the increase or decrease (saturation effects) of the probability of future desirable activity (purchase, subscription, etc.) We do this by linking all available data on the consumer (navigational patterns, geolocation, known preferences, time since last seen, etc.) to a model of resulting activity. In this manner, we can gauge the effect of a touch point on the probability of a desired outcome. effect influence decay This approach allows us not only to derive a distribution of attribution as the methods presented above do, but also to drill down to finer granularity, providing us with insights down to the individual level. time