Continue from initial value to deeper adoption
For the reporting product, the learning journey may continue after the first report:
Completed workflows remain part of the userβs context. Autoplay can avoid repeating what they already know and focus support on unfinished or newly relevant outcomes.
Learn from observed outcomes
The same data used to guide an individual user also helps the product team improve the overall onboarding experience:- Which workflows users complete or abandon
- Where different segments tend to stall
- Which proactive offers users accept or dismiss
- Whether guided users reach value faster than a control group
- Which advanced workflows lead to deeper adoption
Observed outcomes feed the next iteration of onboarding and continual adoption.
Where the agent self-improvement loop is heading
Today, your team reviews these signals and updates the configuration with Autoplay. The longer-term direction is a more self-improving agent that can recommend changes based on observed outcomes, while keeping product teams in control of what is deployed. That future loop can help answer questions such as:- Should this segment receive a different onboarding objective?
- Is the exploration gate offering help too early or too late?
- Which nudge performs better for stalled users?
- Which workflow should become the next adoption goal?
See the technical setup
Follow the engineering quickstart to connect the required parts.
Talk to Autoplay
Discuss which user workflows and segments you want Autoplay to support.