Multi-Touch Attribution Model

The Multi-Touch Attribution (MTA) Model is a strategy in marketing that assigns credit to all digital touchpoints that contributed to a sale.

MTA Model

The Multi-Touch Attribution Model is a sophisticated approach to measure and value the effectiveness of various marketing tactics and channels. It takes into account every interaction a potential customer has with a brand on their path to purchase. The idea behind it is simple: instead of attributing the success of a conversion to a single touchpoint, it acknowledges the contribution of multiple touchpoints to the final conversion.

Formula

The Multi-Touch Attribution Model doesn't use a specific formula. It works on a distribution strategy, where the value of a sale is divided between touchpoints based on the rule or the weights defined by the business. There are different methods like linear, time decay, U-shaped, etc., each distributing the credit unevenly across all touchpoints.

Example

Consider a customer who found your eCommerce site after clicking on a Facebook ad (1st touch), then they visited your site directly a week later (2nd touch), later came through a Google search (3rd touch), and finally made a purchase after receiving an email newsletter (4th touch). In this case, each of these touches would receive credit for the sale in the MTA model.

Why is MTA Model important?

  • Insightful Customer Journey Analysis: MTA allows businesses to track and understand the complex customer journey by determining which marketing tactics are driving customer purchases.
  • Optimizes Marketing Budget: By illustrating which marketing channels are most effective, businesses can optimize their marketing budget by investing more in high-performing channels.
  • Improves Customer Engagement: MTA helps businesses identify and strengthen the touchpoints that are most engaging and effective for customers, improving the overall customer experience.

Which factors impact MTA Model?

  • Data Accuracy: Businesses should focus on improving the quality and reliability of data collected for MTA. The location, platform, device used, time of interaction - every data point can help in fine-tuning the model.
  • Understanding Customer Behavior: Close monitoring of customer behavior can lead to better distribution strategies in MTA.
  • Regular Validation: The MTA model should be reviewed and validated regularly to keep up with the changing market trends and consumer behaviors.

How can MTA Model be improved?

  • Customer Touchpoints: The number and variety of customer touchpoints have a significant effect on the MTA model.
  • Rule Selection: The distribution rule used in the model greatly impacts how the credit for a sale is attributed.
  • Data Collection: The accuracy and completeness of collected data effect the effectiveness of the MTA model.

What is MTA Model's relationship with other ecommerce metrics?

MTA is closely related to numerous eCommerce metrics like Conversion Rate, Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), etc. Understanding how MTA impacts these can provide insights into revenue growth, marketing strategies, and customer retention. For instance, optimizing your MTA model could reduce your CAC by focusing on effective marketing channels, or increase your CLV by improving customer engagement.