kyo0926/retentioneering-tools
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Retentioneering is an open-source Python library for . Find where users get stuck, compare the paths different groups take, and discover patterns across clicks, sessions and repeat visits.
Обзор
Retentioneering is an open-source Python library for . Find where users get stuck, compare the paths different groups take, and discover patterns across clicks, sessions and repeat visits. Explore the data yourself or let an AI agent work with the same tools. Run a demo notebook in Google Colab – no need to install the library locally, sample data included. A demo of the interactive features of the transition graph widget. You can highlight a route, compare path groups, focus on the step you want to investigate, and explore incoming and outgoing transition probabilities in the ego view. Use Retentioneering when you have a question about the sequence of actions like these: - A metric changed. Where did journeys change and move the metric? - Users stop before reaching a goal. What do they do instead and how do successful paths differ? - You want to understand engagement. What behavior types are represented, and how does usage evolve across visits? - You need to investigate a flow.
README
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Retentioneering
Retentioneering is an open-source Python library for understanding user behavior from event logs. Find where users get stuck, compare the paths different groups take, and discover patterns across clicks, sessions and repeat visits. Explore the data yourself or let an AI agent work with the same tools.
Run a demo notebook in Google Colab – no need to install the library locally, sample data included.
Quick start · Use cases · Use an AI agent · Docs
A demo of the interactive features of the transition graph widget. You can highlight a route, compare path groups, focus on the step you want to investigate, and explore incoming and outgoing transition probabilities in the ego view.
Is it for you?
Use Retentioneering when you have a question about the sequence of actions like these:
- A metric changed. Where did journeys change and move the metric?
- Users stop before reaching a goal. What do they do instead and how do successful paths differ?
- You want to understand engagement. What behavior types are represented, and how does usage evolve across visits?
- You need to investigate a flow. Where do checkout, onboarding, support or agent runs loop, branch or stall?
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Установка
npx skillfish add kyo0926/retentioneering-tools