🧠 Background
Platform: Pigeonhole Live is an audience engagement tool used in events, meetings, and conferences.
Feature: This project focused on creating a dashboard for workspace admins to track how their team uses events and sessions.
The company has the data for every Pigeonhole events in a workspace, but there is no way for organizers to view the data throughout all of the events that organizer has created inside the workspace
Especially companies that uses Pigeonhole will usually have several workspaces for each organization team to cater different events happening simultaneously.
🔍 Problem / Opportunity
Lack of Data Analytics Dashboard. Users have increasingly grown to expect this as a hygiene factor in SaaS products.
Workspace admins cannot monitor their data effectively & accurately track their workspace usage for measurement. e.g. some users need to justify the value of their usage for budget purposes, or to more accurately distribute seats to power users.
🛠 What I Did
What I initially received: No existing feature other than documentation of recorded client requests & Data Retention Audit.
So I have to collaborate with the PMs and devs to align on the priorities, the expectations and introduced contextual hints.
I use datapoints from the company’s Data Retention Audit as a base. Then determine what are the Workspace-specific data which may include items like No. of Pigeonholes (events) created, No. of sessions, No. of Polls used and taking a closer look at Workspace utilization or “user activity”.
From the data points, we want to extract more utility by creating Insights from them. For example, Percentage of Pigeonholes (events) created on a month-on-month basis.
Add Export function to allow organizer to export the Insights data.
To understand what our user wants, we listed the most common support tickets from our clients that have been using pigeonhole for at least 2 years.
90% of the answers exists in our data. We just need a way to display them.
Working with Developers on the Data
I teamed up with a few developers to figure out what data we actually had and how we could show it in the dashboard. Some data was clear and easy to use, but other parts were missing or not detailed enough. We talked through what was possible, what users needed most, and made sure everything we showed was accurate and useful.
We had to make sure the date filters were easy to use and matched how people think about time (like "last 30 days" or "this quarter"). These filter options were based on what users wanted and what we found in research, not just what was technically possible.
🧪 Results
The new dashboard was a hit 🎉 especially to admins who often send us a feedback ticket of ways to keep track of how their workspace was being used. The date filters and the charts made it simple to see trends in things like event participation and session use. Organizers also loved being able to export data for deeper analysis. In the end, it was a great decision for our business to create a curated Analytics board in each workspace for our paid users.
Participants chart
Organizer can use this chart to compare signed-in and non-signed-in participants. The bar chart shows totals, and the line graph breaks it down by month and details would be displayed when hovering over each point. I designed chart specifically to help identify peak participation periods across events.
Sessions chart
This chart shows total sessions by type within your selected time range. I designed this to help organizers quickly spot which session types shine and which ones need love. Each type is color-coded, making it easy to compare usage over your chosen time period.
Interactions chart
This chart shows how participants interacted during each event, with each interaction type color-coded for easier comparison. I designed it to help organizers see what engages their audience most, so they can plan their event better, like Q&As if they're a hit for their events.
👁️ Inclusivity design
Since the chart uses a wide range of colors, I applied an inclusive color palette to ensure it stays clear and readable for all vision types.
What i learned
What worked well: Scoping the feature to session-groups helped keep the architecture clean, and aligning with existing insight structures reduced the risk of technical debt.
What was tricky: Handling changes in session hierarchy (e.g., moving sessions between parents) was more complex than expected. We had to account for edge cases where UI and data visibility didn’t align.
