This feature enables customers to filter their analytics by key audience attributes and content types—such as Channels, Posts, and Pages—allowing them to understand how different parts of their company engage with Happeo.
Interactive prototype — click to explore
Analytics lacked granular filtering to understand engagement across departments, content types, and channels.
Research with 10+ companies, competitive analysis, iterative design, Maze usability testing.
Customer interviews, usability tests with Maze, post-launch analytics.
Cross-team collaboration · Led research · Redesign UI · Test & validation · Impact analysis · Continuous Improvement
Team
Junaed Numan (me), James Dashwood (PM), Engineering team and other stakeholders.
Overview
To empower customers with deeper insights into employee engagement, we enhanced Happeo's filtering features based on user research and customer feedback.
Filter by attributes like Date, People (Segment), and Granularity for organization-wide insights.
Analyze individual channel performance to understand team and department engagement trends.
Advanced analytics at post and page levels for targeted performance tracking.
Research
Through interviews with 10 companies, including Randstad (46,000 employees) and Visma (13,500 employees), we uncovered user needs:
Filters for department, location, and post types.
Clean exports aligned with on-screen data.
Ability to save filtered dashboards for frequent use.
Demand for sharing analytics with non-admin users.
Customer Feedback
"We need customizable filters to track the type of content driving engagement, like announcements versus meeting minutes."
— Bram Koster, Randstad
"I'd like to be able to compare data, like two departments or different audiences."
— Amanda Macleod, Venturewell
"The CSV export gives us too much data, and it's hard to manage. We need more built-in filters to easily track the key metrics we care about."
— Amanda Macleod, Venturewell
"We need more insights into user engagement—like how people are interacting with different channels and departments."
— Mandy Burger, Visma
"Most useful thing about audience filters is using groups to filter by location."
— Lucia Nieto, Making Science
"We want to know which posts resonate with different departments or regions, but there's no easy way to filter or compare across teams."
— Harriet Dempsey, GWI
Design Moodboard
Competitors offer filters by content type, time, roles, and engagement metrics.
Detailed segmentation by role, location, and engagement levels.
Filters for post types, team dynamics, sentiment analysis, and real-time data updates.
Information Architecture
Differences of global filters & section filters with saving filters flow diagram.
Usability Testing
Refined the design by simplifying the filter interface and improving usability, making it more intuitive and user-friendly. Simplified filter functionalities, ensuring ease of use and better organization for improved navigation. Ensured scalability, allowing for future filter types without complicating the interface.
Maze Findings
Through interviews with 10 companies, including Randstad (46,000 employees) and Visma (13,500 employees), we uncovered user needs:
Users found creating and applying filters quick, but the condition selection page caused frequent misclicks and delays.
Fix: Simplify the condition selection interface.
Users spent too long reading options, leading to frequent errors. Fix: Streamline the selection process.
Fix: Streamline the selection process.
High misclick rates due to unclear "close" icons/text and a cumbersome reset process.
Fix: Add a dedicated "Remove Filter" button.
Positive feedback on advanced filtering but low participation in testing highlights the need for better user engagement.
Results
Prototype
I streamlined condition selection by auto-displaying options on page load, reducing misclicks and extra steps. Filter application is now faster, with operators and conditions shown instantly after selection. Onboarding guidance for the new filter flow ensures a smoother user experience.
Impact
The highlights demonstrate the growing impact of advanced filtering in enhancing user engagement and driving meaningful insights.
Large organizations like Randstad and Givaudan use filters 10x more than smaller clients, driving higher engagement (9% vs. 5.5%).
The department filter accounts for 25% of activity, proving its relevance.
Advanced filters are widely embraced by users familiar with basic options.
17% of admins actively use analytics, with 7% applying audience filters.
Analytics retains 30-40% of users, showing steady value.
A comprehensive analytics overview, including features like top posts, clickable links, and AI-driven insights, could serve all customers, not just large enterprises.
Conclusion
While the advanced filter functionality has proven valuable, it may not be the most effective solution for driving universal engagement and insights. Moving forward, we should focus on combining accessible insights with filtering capabilities to deliver greater value for a broader range of users.
This project's success is a result of the collaborative efforts of our Product Manager, Mahdi (initial design), the entire squad, and all internal stakeholders whose contributions were instrumental in achieving these outcomes.
Improvement ideas for future