Article Category: Attribution, AI/ML, Marketing
Published: May 27, 2024
Reading Time: 3 minutes
Marketing attribution has been a persistent challenge for marketers despite being a concept that has existed for a very long time. As digital marketing gained prominence in the early 2000s, the attribution riddle has only become more complicated. This article explores why marketing attribution remains problematic, examines common attribution models, and explains why data-driven approaches are necessary for accurate measurement.
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.
A common issue marketers face is that when combining conversion results from different channels—such as Google, Meta, email, and app—the total revenue appears to be 5x what is actually realized. This discrepancy occurs because every platform and channel measures attribution in its own way.
This complexity makes it extremely difficult to ensure correct attribution of sales to the right channels and touchpoints.
There are six common attribution models used by marketers:
One of the most widely used models, last-touch assigns the credit to the last interaction before purchase.
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.
The linear attribution model distributes equal credit across all touchpoints that a customer encounters on their journey.
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.
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.
The 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.
Rules-based attribution models have seven fundamental problems:
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.
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.
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.
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.
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.
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.
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.
Marketing attribution is undoubtedly one of the most challenging aspects of modern marketing. However, it's time marketers mastered it.
At Kognitiv, the 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.
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Track, predict, and optimize your customers' lifecycles with Kognitiv Pulse.
Enable 1:1 personalization at scale with Kognitiv Ignite.
Launch and manage a successful loyalty program with Kognitiv Inspire.
Intelligently acquire and engage customers across paid channels with Kognitiv Amplify. Kognitiv Amplify is described as an AI autopilot for paid media, providing outcome-based AI/ML optimization for paid display, video, social and programmatic.
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