Understanding which channels contribute to conversions is crucial for making informed decisions and improving business outcomes in digital marketing
Consider the following scenario: a significant portion of the budget is allocated to a marketing channel where most prospects convert into customers. However, upon closer inspection, it becomes evident that this marketing channel is just the final step in a series of interactions that led the customer to the product or service. Other channels may have played a crucial role in attracting the customer initially, but they are not receiving recognition or budget allocation.
This unbalanced marketing strategy can result in taking budget away from more effective channels and potentially negatively impacting the return on investment (ROI). Therefore, it is essential to accurately attribute conversions to the appropriate channels and ensure a balanced marketing approach that optimizes budget allocation based on the true contribution of each channel.
Understanding the attribution modelling
Attribution models are analytical tools that track the customer's journey and identify the significant touchpoints or channels that influence conversions. These models can be broadly categorized into two main types:
1. Rule-based models, such as Last Click and First Click, are straightforward and assign credit to a single interaction. The Last Click model attributes credit to the last interaction that occurs before the conversion, while the First Click model assigns credit to the first interaction in the customer journey prior to the conversion.
For example, let's consider a customer journey where a potential customer first learns about a product or service through a blog post, then encounters a social media ad, and finally decides to make a purchase due to a direct email campaign. In this scenario, the Last Click model would only credit the email campaign, while the First Click model would credit only the blog post. Both of these rule-based models overlook the contribution of other channels that played a significant role in the entire conversion process.
The LNDA attribution model is a type of rule-based model.
2. Statistical models, based on data-driven analysis, take a more granular approach by assigning credit to multiple touchpoints in the customer's journey. These models provide a deeper understanding of the individual contributions of each channel in the conversion chain, enabling a more balanced allocation of the budget across channels.
Not all statistical models are equally unbiased. Attribution models used by ad platforms are limited to the activities within their respective platforms. These models may overstate the contribution of their own platform's channels and overlook the influence of channels outside their platform. This biased approach can result in a distorted perception of the effectiveness of different channels. It is crucial to be aware of this bias and complement ad platform models with insights from attribution models that provide a more holistic view of the channel mix. Ultimately, this comprehensive approach has a significant impact on marketing performance.
Alvie regression-based attribution model
Alvie offers advanced regression-based attribution model that surpass the limitations of traditional rule-based methods. You can learn more about how the model works here.