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Retention

As explained in Natural Language Processing in Action, Second Edition

A retention metric measures the proportion of users who return to use a product or service after an earlier interaction.

Retention measures whether users return after an earlier interaction. For chatbot UX, it belongs to HEART, a framework that groups measurement into happiness, engagement, adoption, retention, and task success. section 12.7.3

Start with events: a chatbot can record when someone begins engaging, asks a question, or says thank you. Retention analysis then asks which tracked behavior best represents continued use and supports the journey you want to improve. section 12.7.3

Chatbot UX measurement

A chatbot team might track engagement events alongside retention to understand whether users return, rather than treating an initial signup as meaningful use. section 12.7.3

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Defines retention’s role in event-based UX measurement but does not provide a formula or implementation procedure.
Examples
Uses chatbot starts, questions, thank-you messages, signups, and continued interaction as measurable user events.
What you can do after
Evaluate continued chatbot use as part of a broader user-experience measurement plan.
How it connects
Connects retention with happiness, engagement, adoption, task success, event tracking, and chatbot UX.

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This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.