Ai Job Ad Campaign Creation
Ai Job Ad Campaign Creation
GoCentriq is Leoforce’s intelligent job ad campaign platform, designed as a seamless extension for Arya, their flagship AI talent-sourcing solution.
GoCentriq is Leoforce’s intelligent job ad campaign platform, designed as a seamless extension for Arya, their flagship AI talent-sourcing solution.
My Role - Coming up with Idea and Suggestions
Timeline: 2 weeks
Team Size: 1 product designer, 6 developers, 1 Product Manager and 1 in-house user
My Role - Coming up with Idea and Suggestions
Timeline: 2 weeks
Team Size: 1 product designer, 6 developers, 1 Product Manager and 1 in-house user
The Challenge
The Challenge
The real challenge of this project wasn’t designing a new feature—it was earning clarity and trust in a high-pressure environment where time was limited, the domain was new, and the people involved were already exhausted.
The real challenge of this project wasn’t designing a new feature—it was earning clarity and trust in a high-pressure environment where time was limited, the domain was new, and the people involved were already exhausted.
My Process
My Process
Research Findings - user interviews & observations
Research Findings - user interviews & observations
Notes and Key Takeaways from PM Discussions
Notes and Key Takeaways from PM Discussions
As this was a new domain for me, I collaborated closely with the PM to understand the product’s business context and market positioning.
This helped me clarify the product scope, align with business goals, identify high-level user pain points, and avoid premature assumptions before moving into design.
As this was a new domain for me, I collaborated closely with the PM to understand the product’s business context and market positioning.
This helped me clarify the product scope, align with business goals, identify high-level user pain points, and avoid premature assumptions before moving into design.
After aligning on product context and constraints with the PM, I translated these insights into a structured main product flow.
After aligning on product context and constraints with the PM, I translated these insights into a structured main product flow.
User Mental Model
User Mental Model
Insights from my research
Most of the product knowledge is only with the Senior Campaign Manager. Other teams barely know the details, which makes everyone dependent on them.
The user is often frustrated because they have to explain the same things again and again. It’s exhausting for them.
The main goal of the product is to reduce this dependency on the senior campaign creator.
The product should be smart enough that anyone, even without domain knowledge—like HRs—can use it confidently.
Insights from my research
Most of the product knowledge is only with the Senior Campaign Manager. Other teams barely know the details, which makes everyone dependent on them.
The user is often frustrated because they have to explain the same things again and again. It’s exhausting for them.
The main goal of the product is to reduce this dependency on the senior campaign creator.
The product should be smart enough that anyone, even without domain knowledge—like HRs—can use it confidently.
Screens - Idea suggested
Screens - Idea suggested
Idea 2 - where I gave the smart recommendation
Idea 2 - where I gave the smart recommendation
The goal is to make campaign creation simple for juniors or new users. Smart publisher recommendations guide them toward the right choices, reduce cognitive load, and build confidence without confusion.
The goal is to make campaign creation simple for juniors or new users. Smart publisher recommendations guide them toward the right choices, reduce cognitive load, and build confidence without confusion.
Idea 3 - Asking for the feedback from the user
Idea 3 - Asking for the feedback from the user
The intent is to make the system adaptive by learning from user actions. When users skip or override recommended suggestions, the system treats this as feedback to better understand their expertise and refine recommendations going forward.
The intent is to make the system adaptive by learning from user actions. When users skip or override recommended suggestions, the system treats this as feedback to better understand their expertise and refine recommendations going forward.
