Algorithms commonly used for next-best-action recommendations include:

• Collaborative Filtering: Suggesting actions based on similar customer behavior
• Decision Trees: Making decisions based on customer attributes and historical behavior
• Reinforcement Learning: Continuously improving recommendations based on customer feedback
• Logistic Regression: Predicting the likelihood of a specific customer action

These algorithms can be integrated with Snowplow's event data to improve accuracy and ensure that actions are personalized and relevant.