Your attribution model is wrong! Have you heard someone say that before? Marketing attribution as a concept has been around for a very long time, and we would all hope that by now marketers will have it figured out. The reality is quite different. As digital marketing began to gain prominence in the early 2000s, the attribution riddle has only become more complicated.
What is marketing attribution?
Marketing attribution is the process of identifying and assigning value to the various touchpoints or channels that contribute to a desired outcome, such as a sale or conversion. The goal of marketing attribution is to determine which marketing activities are driving customer engagement and conversions so that marketers can allocate their resources more effectively.
While the concept itself is straightforward, the execution is often complex and filled with pitfalls. What we often see happening is that if you combine your conversion results from different channels, such as Google, Meta, email, app, etc., your revenue should be 5x what you actually see. Why does this happen? One of the biggest issues with attribution is that every platform/channel measures attribution in its own way. Compare apples to apples, right? But marketing attribution has not been easy. We have dozens of marketing channels, with a brand engaging a customer across 8-10 channels on average! Only looking at social, there are 15 channels with over half a billion users. Just simply listing the most common channels, we can name 40+ channels, from Google Search to Instagram, to email, to SEO, to chatbots, and the list goes on. How do you ensure that you are correctly attributing your sales to the right channels and touchpoints?
Common attribution models
There are six common attribution models.
- Last-touch model. One of the most widely used models is last-touch, which assigns the credit to the last interaction before purchase.
- First-touch model. In contrast to last-touch, the first-touch model attributes the full credit for a conversion to the first touchpoint that a customer interacts with on their journey. It emphasizes the initial point of contact as the most influential in driving the conversion.
- Linear model. The linear attribution model distributes equal credit across all touchpoints that a customer encounters on their journey.
- U-shaped model. Also known as the position-based model, the U-shaped model assigns significant credit to both the first and last touchpoints, while distributing less credit to intermediate touchpoints.
- Time-decay model. This attribution model assigns increasing value to touchpoints that occur closer to the conversion event. It acknowledges that interactions occurring nearer to the sale are often more influential in pushing the customer toward making a purchase.
- Data-driven model. Data-driven or algorithmic attribution model, unlike all other models, relies on AI and machine learning to assign a portion of the credit to each touchpoint based on its actual impact on the conversion, rather than relying on predetermined rules or equal distribution.
Despite data-driven models providing a single detailed view of the customer journey, it’s rare to see a brand leveraging one. Most brands are still relying on rules-based attribution models, which limit their ability to drive incremental value.
What’s wrong with rules-based attribution models?
- Last-click attribution bias. One of the most common attribution models, last-click attribution, gives all the credit for a conversion to the last interaction before a purchase. This model overlooks the impact of previous touchpoints in the customer journey, leading to an inaccurate representation of which channels and campaigns are truly driving conversions.
- Single-touch models. Even models that consider multiple touchpoints, such as first-click or linear attribution, fail to capture the full complexity of customer behavior. They often oversimplify the path to conversion, ignoring the nuanced interactions that influence a customer's decision-making process.
- Inability to account for cross-device and cross-channel interactions. With consumers using multiple devices and channels throughout their journey, attribution models that can't track these cross-device and cross-channel interactions are inherently flawed. They fail to connect the dots between different touchpoints, resulting in incomplete insights.
- Lack of consideration for offline interactions. Many attribution models focus solely on digital touchpoints, disregarding the impact of offline interactions. For businesses with physical stores or those running offline campaigns, this can lead to an underestimation of the effectiveness of certain marketing efforts.
- Ignoring time decay. Customer journeys are not always linear, and the impact of touchpoints can vary depending on when they occur. Attribution models that treat all touchpoints equally fail to account for the time-sensitivity of interactions, potentially misattributing conversions to less influential touchpoints.
- Limited scope of data. Some attribution models rely on limited data sources, such as click-through rates or impression data, which may not provide a comprehensive view of customer interactions. This can result in incomplete or inaccurate attribution of conversions.
- Inconsistency across channels. Using different attribution models for each marketing channel can create inconsistencies in how conversions are attributed, making it difficult to compare the effectiveness of different channels and campaigns. By using the same attribution model, marketers can ensure that conversions are attributed consistently, allowing for more accurate comparisons and better-informed decisions about where to allocate resources.
It’s time to master it
Marketing attribution is undoubtedly one of the most challenging aspects of modern marketing. But it’s time we mastered it! At Kognitiv, our suite of products leverages a proprietary data-driven attribution model that relies on a consistent methodology across channels, offering a more precise understanding of how different touchpoints contribute to conversions. The data-driven attribution model adapts to evolving consumer behaviors and market dynamics, driving enhanced customer experiences, higher campaign performance, and, ultimately, sustainable growth.
Are you ready to measure your success? Let’s chat!
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