How can you choose between data-driven and rule-based attribution models?

1 Data-driven attribution models

Data-driven attribution models use advanced algorithms and machine learning to analyze your customer data and assign credit to each touchpoint based on its actual impact on conversions. They are dynamic, adaptive, and customized to your specific business context and customer behavior. Data-driven attribution models can help you uncover hidden patterns and insights, and allocate your marketing budget more efficiently and accurately. However, they also have some drawbacks, such as requiring a large amount of data, being complex and opaque, and being expensive and time-consuming to implement and maintain.

2 Rule-based attribution models

Rule-based attribution models use predefined rules and logic to assign credit to each touchpoint based on a certain assumption or criteria. They are simple, transparent, and easy to implement and understand. Rule-based attribution models can help you establish a baseline and benchmark for your marketing performance, and compare different scenarios and hypotheses. However, they also have some limitations, such as being static, arbitrary, and biased, and ignoring the nuances and interactions of the customer journey.

3 How to choose between data-driven and rule-based attribution models

Choosing between data-driven and rule-based attribution models is not a one-size-fits-all solution; the best choice depends on your business objectives, data availability, analytical capabilities, and budget constraints. When making this decision, ask yourself what your primary goal for attribution is (optimizing marketing mix, understanding customer behavior, or justifying marketing spend?), how much data you have (enough conversions, channels, and touchpoints to feed a data-driven model or simpler rules and assumptions?), how complex your customer journey is (linear, short, single-device journey or nonlinear, long, cross-device journey?), how sophisticated your analytics team is (skills, tools, and resources to implement and interpret a data-driven model or a more straightforward and accessible model?), and how flexible your budget is (funds to invest in a data-driven model or a more cost-effective and scalable model?).

4 Tips for using attribution models effectively

Attribution modeling is a powerful and essential tool for marketing analytics, but it is not a magic bullet. To use it effectively and improve your marketing analytics, consider using multiple models to compare and contrast different perspectives. Review and update your model regularly to reflect changes in your strategy, customer behavior, and market conditions. Additionally, combine your attribution model with other metrics and methods to measure performance holistically. By choosing the right attribution model for your business, you can gain a deeper understanding of the customer journey and attribution, as well as optimize marketing ROI and outcomes.

5 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?