How to use the Smart Nudging Engine

The Smart Nudging Engine can be used to do three main things:

• Measuring effectiveness: Metrics such as click rate, view time, and self-report are used to measure the effectiveness of a nudge.
• Finding optimal nudge: Algorithms like recommender systems and reinforcement learning help find the best nudge for each nudgee or group.
• Generating nudge: Models like GPT-4, Dall-E, and Stable Diffusion are used to generate nudges based on provided components.

There are three use cases for the Smart Nudging Engine:

1. When the user provides 1 nudge:

• The engine measures the nudge's effectiveness using metrics like click rate, view time, and self-report.
• The user can make informed decisions on whether to continue using the nudge or make changes to improve its effectiveness.

2. When the user provides n nudges:

• The engine measures the effectiveness of each nudge.
• The engine finds the optimal nudge for each nudgee or group.
  • Users can do this manually by assigning nudges to different groups and using metric values and actions to calculate effectiveness.
  • Alternatively, users can do this automatically using weights and algorithms like recommender systems and reinforcement learning.

3. When the user provides n nudge components:

• The engine generates different nudges.
• The engine measures the effectiveness of each nudge.
• The engine finds the optimal nudge for each nudgee or group.

To add data to the database, follow this order:

1. Metric types
2. Nudgees
3. Groups
4. Configurations

The order for pre-made or generated nudges, config, or nudges may vary. After completing these steps, add actions and assign nudges to nudgees.