Industry
MarTech, B2B
Client
Adobe
Timeframe
Summer 2024
Role
ai assistant, discoverability
Creating a more personalized prompting experience for users in the Adobe Experience Cloud
Goals
Utilize the insights from the Product & Customer Analytics team to better understand and categorize users.
Surface relevant prompts and suggestions throughout the user journey based on their experience level, role, and app usage.
What did I work on?
I created a more personalized and proactive AI assistant that anticipates users' needs and provides tailored guidance throughout their Adobe Experience Cloud journey.
Why is this needed?
Relevant content
Users see prompts and suggestions specifically tailored to their needs and interests, rather than generic ones.
Improved onboarding
New users receive guidance and prompts that align with their experience level, facilitating a smoother learning curve.
Increased engagement
By offering helpful and timely suggestions, users are more likely to actively explore and utilize the AI assistant's capabilities.
"For you" section in discoverability panel
This is a prime location to surface personalized content based on segmentation data.
suggested prompts displayed prompts based on
Experience level
Beginners might see basic prompts like "Show me my recent projects" while advanced users get more complex suggestions like "Analyze campaign performance across channels."
Role
Marketers might see prompts related to campaign optimization, while designers might get prompts focused on creative workflows.
App usage
If a user frequently uses Adobe Analytics, suggest prompts related to data analysis and reporting.
Other integration points
FTUX
Tailor the onboarding process based on the user's segment. Beginners might get step-by-step tutorials, while experienced users could skip directly to advanced features.
Revisiting the application
Display personalized welcome messages or reminders based on recent activity or user segment.
Who'd I work with?
Senior UX Designers, UX Researchers, UX Writers, Product Managers, AI/ML Engineers, DPMs, Product Equity teams
Machine learning to UX considerations
hmw surface prompts that would be the most relevant to the user?
Combine segmentation data with usage patterns
Look at which features users in each segment interact with most frequently.
Consider the user's current context
Are they in a specific project or workflow? Tailor the prompts accordingly.
Experiment and iterate
Use A/B testing to compare different prompt variations and identify the most effective ones for each segment.
Gather feedback
Ask users directly what kind of prompts or suggestions they find helpful.
