Re-imagine the filter experience - Quantive

Overview

Quantive specializes in performance management and strategy execution software, offering a product called Quantive Result. This platform leverages the Objectives and Key Results (OKRs) methodology to help organizations set, track, and manage goals, ensuring alignment from individual to organizational levels. Quantive Result features real-time performance tracking, detailed analytics, and customizable reports to provide insights into goal attainment. It also includes tools for team collaboration and integrates seamlessly with other enterprise software. Designed for organizations of all sizes, Quantive Result enhances strategic alignment, transparency, and accountability, enabling data-driven decisions and boosting productivity.

Company

Quantive

Objective

The primary objective of redesigning the filtering experience in Quantive Result is to enhance usability and flexibility, enabling users to efficiently access relevant data. By streamlining the filtering process, the aim is to improve overall user satisfaction and productivity. This redesign seeks to provide a more intuitive and powerful filtering system that meets diverse user needs, facilitating better decision-making and strategic execution. The ultimate goal is to create a seamless and efficient user experience that aligns with Quantive’s commitment to delivering top-tier performance management solutions.

The problem

The existing filtering experience in Quantive Result is a significant pain point for customers due to its complexity and lack of flexibility. Users find it challenging to navigate the current filtering system, which often results in frustration and inefficiency. Despite the critical importance of filtering data to access relevant information, the current design fails to accommodate the varying needs of users, making it difficult to extract actionable insights. This complexity not only hampers the user experience but also impedes the overall effectiveness of the platform, as users struggle to fully leverage its capabilities.

The goal

The goal of the redesign is to create a filtering experience that is both user-friendly and highly flexible, addressing the diverse needs of Quantive Result’s users. The new design aims to simplify the filtering process, making it intuitive and accessible for all users, regardless of their technical proficiency. Additionally, the redesign seeks to enhance the flexibility of the filtering options, allowing users to tailor their data views to meet specific requirements. By achieving these goals, the redesigned filtering experience will significantly improve user satisfaction, drive higher engagement with the platform, and ultimately contribute to better strategic decision-making.

Screenshots of the old filtering experience

Approach

Research and analysis

To inform the redesign of the filtering experience in Quantive Result, extensive user research was conducted to thoroughly understand user needs and pain points. We began by conducting in-depth interviews with a diverse range of stakeholders, including end-users, product managers, customer success managers, and technical support representatives. These interviews revealed significant user frustration due to the current filtering’s complexity and inflexibility. Technical support representatives provided valuable insights from both a UX and technical perspective, highlighting frequent user complaints and common technical issues that users encountered. As part of this initial phase, I reviewed past and present bug and suggestion tickets logged by our customers.

In addition to stakeholder interviews, we deployed surveys to a broader user base to gather quantitative data on user experiences and preferences. The survey results underscored the qualitative findings, with many users citing difficulty in navigating and customizing filters to suit their specific needs. To complement our user research, we performed a competitive analysis by examining the filtering systems of leading performance management and data analytics platforms. This analysis helped us identify best practices and innovative solutions that could be adapted to enhance our filtering experience. The products we examined included Tableau, Power BI, Asana, Smartsheet, Monday.com.

Through this competitive analysis, we identified that the most successful filtering experiences were those that balanced power and flexibility with simplicity and ease of use. Platforms like Tableau and Power BI offered robust filtering capabilities but often at the cost of user-friendliness, particularly for new or non-technical users. On the other hand, tools like Asana and Monday.com excelled in simplicity and user experience but lacked advanced filtering options required for complex data analysis.

These insights guided our redesign efforts, emphasizing the need to create a filtering experience in Quantive Result that combined the best aspects of our competitors: robust, customizable filters that are easy to use and quick to apply, ensuring that users can efficiently access relevant data without being overwhelmed by complexity.

We also conducted usability tests on the current filtering experience to observe users in real-time as they attempted to complete typical filtering tasks. These tests revealed specific points of confusion and areas where users struggled the most.

Ideation and conceptualization

This comprehensive research approach ensured that our redesign efforts were deeply informed by actual user needs and industry standards, paving the way for a more intuitive and flexible filtering experience. It allowed me to create the user personas we were working with.

These personas highlight the need for a filtering experience in Quantive Result that caters to both basic and pro users. Jane needs a simple, quick, and intuitive filtering experience, while Michael requires a powerful, flexible, and efficient system that supports complex data analysis and reporting. Balancing these needs is crucial for delivering a user-friendly yet robust filtering experience.

During the initial brainstorming session, it became evident that the current filtering experience in Quantive Result was primarily designed for advanced users, leaving basic users overwhelmed and frustrated. The research underscored the necessity of creating a dual-mode filtering experience to cater to both user groups effectively. Basic users, like Jane, needed a streamlined, quick-access filtering option, while pro users, like Michael, required a more flexible and powerful tool. To address these divergent needs, I decided to split the filtering experience into two distinct modes: a quick filter toolbar for basic users and an advanced filter sidebar for pro users. By comparing various design patterns, I identified that this approach would provide an intuitive and efficient solution for both user types, ensuring that each could effortlessly access the level of functionality they required.

Design

The filtering experience in Quantive Result is integral to numerous modules, each with unique use cases and user requirements. This widespread application necessitated a design approach that could be seamlessly adopted across these diverse modules while addressing their specific needs. In the first iteration, we concentrated on implementing the new filtering experience in the most complex and critical module: the OKR and alignment views. This module's intricate data relationships and high usage frequency made it an ideal starting point to test and refine the new design. By focusing on this challenging area first, we aimed to ensure that the filtering experience would be robust and adaptable, setting a strong foundation for subsequent adoption in other modules. This strategic approach allowed us to address the most pressing user needs and gather valuable feedback to optimize the filtering experience across the entire platform.

Next, I reviewed the structure of all filter parameters keeping in mind the complaints about a lack of flexibility and difficulty understanding the query building. I did it both for the quick filter and the advanced filter.

To visualize and refine the redesigned filtering experience for Quantive Result, I created detailed wireframes for both the quick filter toolbar and the advanced filter sidebar. These wireframes served as a blueprint, allowing us to iterate on the design and gather feedback from stakeholders and users, ensuring the final implementation would be intuitive and effective across all modules.

In my design approach for the filtering experience, I adhered to several key UX principles to ensure the solution was both effective and user-friendly. I prioritized simplicity by designing intuitive interfaces that minimize cognitive load and streamline user interactions, making it easy for both basic and pro users to navigate. Consistency was maintained across the filtering components to ensure a coherent experience, which helps users quickly become familiar with the system. We also emphasized flexibility to cater to diverse user needs, incorporating customizable options that allow users to tailor their filtering experience. Feedback mechanisms were integrated to provide real-time responses and ensure users are always aware of their filter settings and results. Lastly, accessibility was considered to ensure the design is usable for all users, including those with disabilities, thus broadening the overall usability and inclusiveness of the platform. These principles guided our design to create a filtering system that is efficient, adaptable, and aligned with best practices for user experience.

User Testing and Validation

As part of the feature validation process, we conducted moderated user interviews to gather detailed feedback on the redesigned filtering experience. Key findings revealed that regular users struggled with the complexity of the filtering on OKR views, noting that the feature’s extensive capabilities often led to confusion rather than efficiency. Several users suggested enhancements, such as incorporating a reset option to easily revert to default views and making the default view display active sessions and objectives relevant to the user or their team. Feedback also highlighted issues with the current session selector, which underwent multiple iterations. Initially, various versions introduced complexities that were deemed necessary only for the advanced filter. I ultimately decided to streamline the quick filter by removing these complexities, as they were better suited to the advanced filter, simplifying the user experience. Other suggestions included offering suggestions in the user and team selectors, improving the clarity of the ellipsis button for adding more filter parameters, and repositioning the advanced filter button closer to the quick filter for better visibility. Color-coding of progress was well-received, and the ability to exclude past sessions was deemed important by users. This feedback was invaluable in refining the filtering experience to better meet the needs of both basic and pro users.

Clearing mechanism was tested as a pattern during the user validation and highly praised by users. As a result, an option to clear all selections was added to all dropdowns used in the quick filters, and a separate option to revert back to the initial state of the view was added to the advanced filter. It was also an interesting fact that from the user’s perspective, a ‘Clear All’ button is also a reminder that filters are applied.

Final design

In the final design, I decided to reposition buttons to ease the transition between the modes. For the addition of quick filter parameters, I decided to go with a simple “More” button rather than an icon. Indicators were added to show how many options are selected both in the quick and advanced filters. The selected sections in dropdowns were very well received. For the session selector, I decided to go with a very simple tree structure as hierarchy was very important for users but I decided to include only current sessions for the quick filters and leave an opportunity to customize settings for the selector only in the advanced filter.

Implementation

The implementation of the redesigned filtering feature involved close collaboration with developers, ensuring that the design vision was faithfully realized while addressing technical challenges. We held daily sync meetings to discuss progress, tackle any issues promptly, and provide ad-hoc support as needed, which facilitated smooth communication and problem-solving throughout the development process. The handoff process was conducted using Figma, where detailed design specifications and interactive prototypes were provided to guide the developers. Ensuring design fidelity was a priority we worked diligently to balance our design vision with technical feasibility, particularly given the introduction of a new components library and the complexity of handling extensive data. The new components library introduced initial challenges, as its integration required adjustments to maintain consistency with our design goals. Despite these hurdles, close collaboration and iterative feedback helped us navigate these complexities and deliver a filtering experience that met both user needs and technical constraints effectively.

Results and impact

Deployment

For the deployment of the redesigned filtering feature, we adopted an iterative approach and implemented continuous delivery during a beta phase. This allowed select users to test the feature with their real data, providing valuable insights into its performance and usability in actual scenarios. We facilitated the training of OKR champions through our customer success managers to ensure they were well-equipped to support their teams in using the new feature. Additionally, we incorporated a feature onboarding experience into the application, utilizing popovers and interactive walkthroughs to guide users through the new filtering functionality. Throughout the deployment process, we focused on maintaining maximum compatibility with previously saved filters, implementing a recovery mechanism to address any issues that might arise, and ensure a smooth transition for users. I facilitated multiple design sprints with stakeholders across product, engineering, and customer support teams, ensuring alignment on the project goals and timelines.

Monitoring and feedback

Post-launch, we utilized Amplitude to track analytics and explore user paths, enabling us to gain insights into how the filtering feature was being used and identify areas for further improvement. We continued to gather feedback through our customer-facing colleagues and customer ticket systems, which revealed that users found the new filters significantly easier to use. This positive reception led to several accounts expanding their use of Quantive Result, particularly adopting modules that had previously been hindered by difficult filtering systems. Quantitative metrics associated with filters saw significant improvements, reflecting the feature's enhanced effectiveness and user satisfaction - filter usage increased by 40% during the closed beta launch, task completion time reduced by 25%, making the process faster and more efficient, users interacting with the filter feature increased by 30%, indicating higher engagement, 50% more weekly active users for the whole product, support tickets related to filtering dropped by 30%, signalling a smoother experience. This design change was crucial in supporting the company's broader goal of increasing user retention by optimizing search workflows.

Lessons learned

The redesigned filtering feature saw significant success, particularly with the implementation of the two distinct modes—the quick filter and the advanced filter. Both modes were very well received by users, as they effectively addressed the diverse needs of basic and pro users. The quick filter provided a streamlined, intuitive experience for those needing rapid access to relevant data, while the advanced filter offered the flexibility and depth required by more experienced users. This dual-mode approach was successful because it allowed us to cater to varying levels of complexity and user expertise. However, a notable challenge was navigating the vastly different user scenarios based on the user's position within the company. Balancing these needs required careful consideration to ensure that both basic and advanced filtering experiences were optimized without compromising on usability or functionality.

Future enhancements and areas for further research - during the research I noticed some users think of sessions in a timeline (past, present, future) and others think of them in terms of status (open, archived, in progress). Additional settings to control the list in the dropdown in the advanced filter could be explored as an enhancement. Another enhancement that could be explored is the ability to turn on and off deactivated users and teams and to add another level of customization regarding teams and what selecting a team means to the user.