What are the disadvantages of attribution?

Here are some key disadvantages of attribution modeling:

1. False promise of 100% trackability: Attribution models are built on the premise that every touchpoint can be tracked, which is not actually possible. Many touchpoints remain untrackable for legal and technical reasons.

   
   

2. Declining trackability: With the death of third-party cookies and privacy changes like iOS 14, the number of trackable touchpoints is actually decreasing, making attribution even more challenging.

   
   

3. Overvaluing measurable channels: By focusing only on trackable touchpoints, attribution models tend to overvalue the channels and touchpoints that can be measured, potentially leading to misallocation of marketing resources.

   
   

4. Inability to account for all influences: Attribution models typically fail to account for many touchpoints along the customer journey, especially offline interactions, word-of-mouth, and brand equity.

   
   

5. Correlation-based bias: Attribution models can be subject to correlation-based biases, mistaking correlation for causation in the customer journey.

   
   

6. In-market bias: Attribution may give credit to ads shown to consumers who were already in the market to buy, even if the ad didn't influence their decision.

   
   

7. Digital signal bias: Models may not properly factor in the relationship between online activity and offline sales.

   
   

8. Overlooking brand impact: Many attribution models fail to adequately capture the relationship between brand-building initiatives and conversions.

   
   

9. Complexity: Advanced attribution models can be complex to implement and interpret, requiring significant data and expertise to use effectively.

   
   

10. Short-term focus: Some attribution models, particularly last-click, can foster a short-term marketing mindset at the expense of long-term objectives like brand loyalty and customer lifetime value.

    
    

These disadvantages highlight the limitations of attribution modeling. To solve for these limitations, we created Incrementality-based Attribution that combines incrementality testing with data-driven attribution to give marketers the most accurate measurement based on incrementality.