Last Click Attribution

Last-click attribution is a widely used but limited method in digital marketing analytics where the entire credit for a conversion or sale is assigned to the final user interaction (touchpoint) before the purchase. In Alviss AI, understanding last-click attribution helps highlight its shortcomings compared to more advanced techniques like Marketing Mix Modeling (MMM), which provide a holistic view of channel contributions.

Key Challenges

• Oversimplification of the Customer Journey: It ignores earlier interactions that build awareness or influence decisions, leading to biased insights.
• Overemphasis on Lower-Funnel Tactics: Channels like paid search ads or direct website visits often receive full credit, while upper-funnel efforts (e.g., TV ads, social media, or display campaigns) are undervalued.
• Skewed Resource Allocation: This can result in underinvestment in broad-reach channels that drive initial interest, potentially harming long-term growth.

How It Works

In last-click models, credit is given solely to the last touchpoint, such as:

• Clicking a paid ad
• Visiting a website
• Opening a marketing email

This simplicity makes it easy to implement with basic tracking tools, but it fails to capture multi-touch dynamics.

Example

Imagine a customer sees a TV ad for a product, sparking interest. They later search online and click a paid search ad to complete the purchase. Under last-click attribution, the search ad gets 100% credit, disregarding the TV ad's role. In Alviss AI's MMM approach, models can account for these synergies, revealing how upper-funnel activities enhance lower-funnel efficiency.