First-touch attribution credits the first marketing interaction for a conversion. Learn when first-touch is the right model, its limitations, and how to combine it with other models for complete measurement.
Last-touch attribution credits the final interaction before conversion. Learn when last-touch is appropriate, why it's still the default in most tools, and how it misleads budget decisions when used alone.
Linear attribution gives equal credit to every touchpoint in the customer journey. Learn when linear is the right choice, how to implement it, and why it's the best starting point for multi-touch attribution.
Time-decay attribution gives more credit to touchpoints closer to conversion. Learn how the half-life parameter works, when time-decay is the right model, and how to implement it for your business.
Position-based (U-shaped) attribution gives 40% credit to first touch, 40% to last touch, and splits 20% among middle touchpoints. Learn when this model fits your business and how to implement it.
Data-driven attribution uses algorithms to learn touchpoint importance from your data. Learn how Markov chains, Shapley values, and machine learning models work, when to use them, and their limitations.
A practical framework for selecting the right attribution model based on your sales cycle, data volume, and business goals. Includes decision tree, model comparison, and common mistakes to avoid.