What is attribution modeling and how can machine learning improve it?

1 What are the challenges of attribution modeling?

Attribution modeling is often based on assumptions and rules that may not reflect the reality of customer behavior and preferences. For instance, Last-click gives all the credit to the last channel or touchpoint before the conversion, First-click gives all the credit to the first channel or touchpoint that introduced the customer to the brand, and Linear gives equal credit to all the channels and touchpoints in the customer journey. While these models are straightforward and simple to implement, they may not capture the intricacy and dynamics of customer behavior and marketing effectiveness. Moreover, they can lead to inaccurate or biased results that impact your marketing budget allocation and performance measurement.

4 What are the challenges of machine learning for attribution modeling?

Machine learning is not a one-size-fits-all solution for attribution modeling problems. It comes with its own challenges and limitations, such as data quality and availability, model complexity and interpretability, and ethical and legal implications. For example, the data used to train and validate models may be incomplete, inconsistent, inaccurate, or outdated, which affects the quality and reliability of the models. Additionally, some machine learning models may use hidden layers and nonlinear functions that are not transparent or intuitive, making it difficult to communicate and justify the results. Furthermore, machine learning may use personal or sensitive data that requires permission and protection of the data subjects. Moreover, it may generate results or actions that could affect the rights and interests of the data subjects or other parties, necessitating compliance with relevant laws and regulations.

5 How can you get started with machine learning for attribution modeling?

Machine learning for attribution modeling is not a one-size-fits-all solution and requires a clear understanding of business objectives, customer needs, and marketing context. To get started, you must define your goals and questions, collect and prepare data, choose and apply machine learning techniques, interpret and communicate results, and implement and evaluate actions. When defining your goals, ask yourself what you are trying to achieve with attribution modeling. When collecting data, consider the sources and types needed. For machine learning techniques, train, test, and validate models to compare the best for your attribution modeling. Interpretation should involve finding the main insights of your models. Communication should involve explaining and visualizing results to stakeholders. Finally, implementation should involve optimizing marketing budget allocation and performance while evaluation should involve measuring the impact of marketing actions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?