What is the difference between rule-based attribution and data-driven attribution?

Keeping track of every single step of your customer's journey is not an easy task. At first it might seem incredibly difficult to have a constant stream of clean data to map the full experience of your customers from the initial contact to the point of sale. The good news, however, is that there are powerful attribution models that can help you develop a professional marketing strategy based on your multi-touch attribution data.


It is thanks to these attribution models that you can achieve a meaningful and concrete big picture view. It is necessary to consider in detail the attribution of a proper investment to the correct touch points along with the conversion path that guides to a target outcome.

Trying to assign an answer to questions like:

  • Where are meaningful interactions taking place?
  • How do users come into contact with your brand across all channels?
  • What is the optimal budget allocation among these channels?

A good sign that you are using a good attribution model is the increase in your marketing ROI. The real challenge is choosing the right tool to do this, which fits your budget and provides accurate data to improve your marketing strategy.


Let’s look at two different approaches for assigning value to your touch points: the rule-based and the data-driven attribution model.




What is the difference between rule-based attribution and data-driven attribution?

These are simple attribution models, based on a predefined formulas. On these models, the touch points are defined by pre-existing rules. The most common rules-based attribution models are the single-touch, linear and custom attribution models.


Here are a few examples:

Simple-touch: 100% of the credit is assigned to the first or last touch point.

Linear: Each touch point gets the same credit.

Custom attribution: Credit is assigned arbitrarily to each touch point.

This model can be tailored in order to have better control over how you assign credit for conversions.
Rule-based attribution models are simple and pre-defined, so they cannot be customized around your specific business. The risk here is to develop a marketing strategy based on the rudimentary insights that such models give you. In most cases, these models are far too simplistic.

What is a Data-Driven Attribution model?

A data-driven model is a sophisticated algorithmic attribution model based on a scientific approach that provides output predictions built on data and the modeling of that data. Data-driven attribution can be customized for any type of business and produces dynamic outputs. Data-driven attribution assigns credit to the different touch points based on both converted and unconverted data.


It determines which touch points are the most influential in the customer journey and provides more accurate conversion data. In short, it values all steps on the conversion path. This model relies on high-quality data and requires a high degree of human interaction for analyzing its data analysis.


An algorithmic attribution model allows you to meticulously measure the most relevant KPIs for highly integrated cross-channel campaigns, including profit, ROI, and sales.




What is the right model for you?

The rule-based attribution offers predefined simple models with basic formulas, and its attribution credit goes to the first or last click. However, “keeping it simple” does not offer the complete and high quality perspective that a professional marketing strategy needs to be successful.


Data-driven attribution has a specific quality that can engage with all types of business: it can be fully customized to meet any requirements. This is the recommended model to measure and optimize campaign performance. Using data-driven attribution can help you fully understand where to allocate resources and spend your budget.


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