Attribution Modeling: Understanding the Customer Journey

Collecting data is great, but in order to be truly effective at marketing, you need to make sense of it all. Attribution modeling is a process of assigning credit for conversions (purchases, leads, downloads etc.) to the various marketing activities that have been used to influence a customer’s purchase decision. It enables marketing teams to understand which channels, campaigns and assets are driving growth and performance.

Media attribution models allow you to assign value to every touchpoint that your customer makes on their buyer journey. By measuring what contributes to a customer’s purchase decision and the impact of each component within the customer journey, marketers are able to identify which elements are working best and allocate their budget accordingly.

In #AttributionModeling, a touchpoint is any interaction between a potential customer and your brand, such as a click, like, or view. Click To Tweet

Different attribution models give different values to touchpoints based on proximity to a specific conversion and other criteria. If you don’t have the right attribution model for your goals, your campaigns will eventually start to plateau.

Let’s take a deeper look at each of these attribution models.

Types of Attribution Models

There are various types of attribution models:

  • Last interaction attribution
  • First touch attribution
  • Single touch attribution
  • Linear attribution
  • Time decay attribution
  • Dynamic attribution
  • U-shaped attribution
  • W-shaped attribution


The most popular approaches to marketing attribution are multi-touch models such as Last Interaction or First Touch.

Last Interaction Attribution Models

This attributes 100% of the credit to the last marketing touchpoint that a customer interacted with prior to purchase. This model is useful for understanding which channels are driving conversions in the short term, however it does not consider how earlier interactions have impacted overall performance.

First Touch Attribution Models

This gives all of the credit for the conversion to the first marketing touchpoint that a customer interacted with, ensuring earlier marketing efforts are not overlooked.

Other attribution models include Linear Model and Time Decay Model.

Single Touch Attribution Models

The simplest type of attribution model gives the full value of the conversion to a single touchpoint. Typically, this is the last touchpoint before a conversion, or the first one — this is why it’s often referred to as a last click or first click model.

  • Pros: This is the easiest model to use and understand. It rarely requires any specialized tools or software to measure and is often “right enough” for people just starting with data modeling.
  • Cons: The single touch attribution model doesn’t give you any details about the customer journey. The average customer requires 16+ touchpoints before buying something, and this model doesn’t give any value to 5+ of them.

Linear Attribution Models

Linear Model assigns equal credit to each touchpoint along the customer journey

This attribution is the simplest of the multitouch model.

  • Pros: Captures all the touchpoints in the lookback window. Gives you values for all or most of your initiatives. Much more informative than single touch.
  • Cons: Overly simplistic. The weights are assigned equally for each step, and the model does not actually evaluate the importance of the step.

Time Decay Attribution Models

This robust attribution model assigns more credit to channels located closer in time to when the conversion took place.

Think of it as a staircase of value attribution. The model gives the most value to the last touch, slightly less to the touch before it, a little less to the one before that, etc.

  • Pros: More robust than single touch, more accurate than linear attribution. Makes sense intuitively — it fits the common-sense model that touchpoints become less important as you go back in time.
  • Cons: Still more simplistic than the reality of the customer journey. Static values for each touchpoint may not weigh touchpoints with accuracy. Proximity is not necessarily equal to importance.

Dynamic Attribution Models

A dynamic attribution model uses algorithms or AI to assign weight to each touchpoint based on a series of calculations. This model uses historical data and other signals to constantly shift value attribution.

  • Pros: The most robust and intuitive of models. Gives the difficult task of determining touchpoint value to a computer. Much more accurate than static models. Can change in response to customer behavior.
  • Cons: Can be difficult to implement. Often requires relatively expensive software. Can be inaccurate if misconfigured.
In #AttributionModeling, the Look-Back Window is the period of time before the conversion happens in which touchpoints are attributed to that conversion Click To Tweet

To get the most accurate view of attribution, it is important for marketers to consider a combination of models rather than relying fully on one.

Attribution modeling is an essential part of the modern marketer’s toolkit. Attribution models provide valuable insights into customer behavior to help marketers optimize their marketing strategies and achieve better results. By understanding attribution models and which channels, campaigns or assets are driving performance, marketers are also better equipped to compare their efforts to industry benchmarks.

For cutting-edge companies today, attribution models have become an integral part of the decision-making process when it comes to investing resources in marketing activities.

Atribution modeling is a great way to get data-driven insights that help shape marketing strategies and ultimately drive results.


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Over the last 18+ years, Wandia has designed a career that combines an ardent interest in global markets with enthusiasm for adventure, fascination with science, and passion for people. She has worked at Fortune 500 companies like Google, Johnson & Johnson, and Eli Lilly. At Samsung, Wandia led ecosystem marketing including developer outreach, awareness, and engagement. A results-driven, growth-focused, data-centric senior marketing leader with both corporate and startup experience, she is passionate about connecting with creators, makers, and visionaries. She loves to dance for fun and fitness.

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