CVENT

Event Agenda Builder using AI

AI-powered solution recommending sessions to event attendees, personalized based on their interests.

Agenda builder project Cover Image

Role

Senior Product Designer & owner

Duration

8 months

Team

1 Senior Product Designer ·
1 UX Researcher ·
1 Content Designer ·
1 Localization Specialist ·
2 Product Managers ·
FE and BE Engineers

Tools

Figma ·
FigJam (workshops) ·
MixPanel and Sigma (data analysis)

Background

Cvent's Attendee Hub is an all-in-one digital event platform that powers virtual, hybrid, and in-person experiences by centralizing content delivery, networking, and engagement tools into a single unified interface.

From our early discovery and analysis, we confirmed that attendees at large, complex multi-day events like conferences, were very overwhelmed when it came to building their agenda. The problem was two-fold:

  1. Sheer Volume: For large, multi-day events, attendees faced a flat list of 50+ sessions. They had to manually scan this entire list for each day resulting in choice overload.
  2. Lack of Relevance: They were recommended a few sessions but it gave no clear indication of why the session might be right for them. This forced attendees to click into every single session description, a time-consuming and frustrating process.
The business impact was clear. This friction led to attendees missing valuable sessions or choosing sessions that weren't a good fit, resulting in low engagement and dissatisfaction with the event.

My Role

I led the 0-to-1 design of an AI-powered Agenda Builder for Cvent's Attendee Hub product, that helped attendees at large-scale events, like conferences, build an agenda that was personalized to their interests and goals, enabling them to get the most out of the event. This project directly addressed session discovery friction, and helped support high-scale attendee engagement.

Problem Statement

How might we help attendees build their event agenda so they get the most value out of the sessions, and prevent cognitive overload and decision paralysis?

The Process

Defining the Problem & Scope

I synthesized existing research, analyzed product data (MixPanel, Sigma), and conducted extensive internal as well as external audits to build a foundational understanding of the problem space. My initial research validated the problem. My next step was to build cross-functional alignment. I led two key workshops:

  1. Problem Discovery Workshop: I planned and facilitated a problem-discovery workshop with product, research, and design partners to collaboratively define the problem statement, success metrics, and constraints. I presented my research, which guided the cross-functional team to a single, unified problem statement.
    Screenshot of the Problem Discovery workshop in FigJam
    Img: Screenshot of the Problem Discovery workshop in FigJam
  2. Opportunity Solution Tree Workshop: Since there were various ways of meeting the OKR for this project, I planned and facilitated a follow-up workshop with my PM and Design Manager, where we used the Opportunity Solution Tree methodology to map out all possible opportunities and solutions, and strategically align on a phased scope. This workshop was pivotal for our product strategy. It allowed us to deliberately scope down to a more focused yet high-impact solution for our first release.
    Screenshot of the Opportunity Solution Tree workshop in FigJam
    Img: Screenshot of the Opportunity Solution Tree workshop in FigJam

Design Ideation

I created in-person and hybrid Attendee Journey Maps (based on existing JTBD research) and facilitated a design ideation workshop for the design and research teams, resulting in a rich collection of ideas. The journey maps helped the participants have a wholistic view of an attendee's agenda building journey rather than focussing on their experience within the Attendee Hub platform alone.

Screenshot of the Design Ideation workshop in FigJam
Img: Screenshot of the Design Ideation workshop in FigJam

Using the ideas gathered through the workshop, I created wireframes for multiple concepts and presented them to design leadership and stakeholders for feedback.

Wireframes of some of the top ideas gathered
Img: Wireframes of some of the top ideas gathered

To get a clear understanding of what the various touch points were for an attendee to build their agenda, I mapped out the agenda building journey for an attendee that captured what the state of the agenda would be at every stage. This was then reviewed with the PMs where I cross-checked my analysis and assumptions with them, made corrections and finalized the direction I needed to take with the designs.

Attendee's agenda building journey that clarified current state and provided design direction
Img: Attendee's agenda building journey that clarified current state and provided design direction

Design & Iteration

I designed the initial flow for the Agenda Builder, built on the hypothesis that users would easily find the CTA and understand session conflicts.

  • Usability Testing: Testing revealed key gaps between our hypothesis and user behavior.
    point number one

    Confusing Entry Point

    The AI sparkle icon on the FAB was too small and not understood, causing users to miss the feature entirely.

    Image showing entry point Image showing entry point
    point number two

    Unclear Content

    The copy for the session goals section was unclear, reducing user confidence.

    Image showing session goals content Image showing session goals content
    point number three

    Missed Conflicts

    Users did not identify session conflicts and expected a clear warning.

    Image session recommendation without conflict indicator Image session recommendation without conflict indicator

  • Enhancing with User Insights: Testing also uncovered a new opportunity. When their connections were also attending the same session, users wanted to see who they were. I incorporated this insight by hyperlinking the "number of connections" text, adding a powerful layer of social proof.

The Solution

The final design is a multi-step, intelligent workflow that guides users to the right content.

The 'Get Recommendations' CTA: Altered the pattern so that the FAB text is expanded when the user lands on the page and collapses after a short delay.

The 'Goals & Interests' Selection: Empowers users by asking them to self-identify their goals. This data, combined with inferred behavioral data (used only if we had their consent), fuels the recommendation engine.

The 'Recommended List' with 'Why' & 'Conflict': The new recommendation card clearly states why a session is recommended (e.g., 'Based on your goals') and includes a high-visibility 'Conflict' indicator when there was one.

Outcome & Reflections

The project was validated through a rigorous design and testing process.

  • Solving User Problem: This project solved the core user problem. In our final round of testing, 100% of users successfully identified session conflicts (up from 40% in Iteration 1), and all users described the new flow as clear, intuitive, and easy-to-use.
  • What I Learned: This project solidified my belief that a designer's most important job is often framing the problem. By leading workshops using methodologies like the Opportunity Solution Tree, I was able to build team alignment and ensure that the solution we designed was solving the right problem.
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