Why attribution?

A typical customer conversion path involves multiple touch points and devices, which makes understanding the real value of each channel more complex than ever before: 

  • Paid (TV, SEM, Display), Free (SEO, Direct traffic, CRM) or Acquired touchpoints
  • Offline (TV, radio, point of sale, call center) 
  • Touch Points on Multiple devices (PC, tablet, smartphone or apps)

The purpose of attribution is to determine the impact of each marketing channel in complex conversion paths, with three objectives :

  • Generate more revenue for the same budget or reduce the budget while keeping sales constant
  • Eliminate the "silo view" when analyzing each marketing channel individually, and deduplicate conversions that are typically counted multiple times (leading to over-reporting and potentially overpaying media providers).
  • Understand how the various channels interact with each other

Two families of allocation models

Predefined formulas

Predefined or "ad hoc" formulas assign conversions based on the advertiser's "intuition", such as last click, or all touchpoints in the conversion path.


Last click

First click

Linear

"U"-shaped

Increasing

Custom

The main problem with these models is that they rely on subjective and unproven assumptions in the attribution of conversions.

Algorithmic models

Algorithmic models use machine learning techniques to evaluate the influence of each touch point in the conversion path by observing the statistical impact of a touchpoint in a dataset of many different paths.

Algorithmic models operate with no prior assumption and are based solely on the data collected.

Wizaly’s algorithmic attribution model

The most cutting-edge algorithm on the market, which computes the actual contribution of each media channel.

Wizaly's algorithm compares groups of paths in order to calculate the impact of each touchpoint within the conversion path.

Data driven

No ad-hoc assumptions, it is based strictly on the data collected

Granular

Analysis at a the most granular level, including such parameters as channel, source, advertisement and many other parameters

Dynamic

Evolution over time to the specifics of each media mix, the evolution of competition and the market

Multi-dimensional

Computation of the impact of each touchpoint along 6 dimensions including source, engagement and timing of touchpoints

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