How Autoplay is using AI differently
Understanding the interdependency between user intention, knowledge, hesitation
Autoplay doesn't just use AI to summarize session replays, we leverage AI models to predict user intentions in software. However, determining if a user's click is intentional requires understanding what the user knows or doesn't know about your software. This is why we tackle the complex task of interpreting user knowledge about a feature or flow.
Building our own taxonomy
Autoplay is developing a unique taxonomy that connects user actions to sequences of actions, which are then mapped to specific tasks and ultimately linked to user intents or goals. This innovative approach allows us to efficiently and directly map actions to tasks and intentions, a method that has not been achieved before.
This capability becomes particularly crucial when dealing with complex software that involves multiple goals and various pathways to achieve each goal.
Our own Philosophy for AI-Based Data Extraction
We are committed to creating a robust philosophy for extracting data insights. There are many ways to extract insights, and we focus on helping our customers identify the right data points.
In all the data we extract, we adhere to the following philosophy:
User Intent: What are they trying to do?
User Actions: What is the user doing? (optional)
Points of Friction: Where are they struggling?
Why?
How frequently does this occur?
Which and how many users are affected? Or which and how many sessions does this affect?
If you believe in the way we are approaching things - we would love to chat