Duplicate Management
Senior Product Designer
2025
Background
The cost of duplicates
Investors at venture capital firms are always searching for their next breakout company, one successful investment can define their entire career, so it's no surprise they gravitate toward tools that help give them a competitive edge.
A tool like Affinity can help investors do their jobs better by surfacing insights in their network that uncover opportunities they might have otherwise not seen. Since these insights are at the foundation of the Affinity platform, accurate data is absolutely essential.
However, since data is primarily imported from multiple sources, such as emails, calendars, and CSVs, duplicates can often surface, which creates confusion and prevents users from seeing accurate insights.
PROBLEM
The current experience was seen as inaccurate and untrustworthy
At the start of the project, Affinity already offered a duplicate manager that allowed users to merge duplicate records. However, the experience was falling short of customer expectations and was perceived as untrustworthy. In a survey, participants rated the current duplicate manager 1.6 out of 5 for usefulness.
Improving the duplicate manager had become one of the most requested features with more than 100+ customers submitting feedback citing frustrations.
The core issue was that the matching algorithm often displayed false positives, which eroded users' trust in the system. Even as engineering improved match accuracy, those improvements remained behind the scenes and weren't reflected in the experience, leaving users still feeling skeptical.
Explorations
Designing with the long-term in mind
With the new shift in approach, I explored a range of design concepts focused on giving users control over the underlying data. These included two primary patterns: an accordion layout optimized for speed and efficiency, and a full-page layout designed to support deeper evaluation.
I reconnected with customers and conducted prototype sessions, walking them through each concept to gather feedback. Across the board, the ability to see and control data at the field level was strongly validated and received positively.
With the new shift in approach, I explored a range of design concepts focused on giving users control over the underlying data. These included two primary patterns: an accordion layout optimized for speed and efficiency, and a full-page layout designed to support deeper evaluation.
I reconnected with customers and conducted prototype sessions, walking them through each concept to gather feedback. Across the board, the ability to see and control data at the field level was strongly validated and received positively.
When comparing the two patterns, the accordion layout was perceived as more efficient and allowed users to quickly scan details without needing to navigate elsewhere, while the full-page layout was more thorough and provided a more focused environment.
Although the accordion pattern offered greater efficiency, we chose to move forward with the full-page layout since our goal was to enable users to confidently adopt bulk merge as their primary workflow. The full-page layout better provided the depth and clarity needed to build that trust.
Usability Testing
Addressing friction points identified in testing
After multiple rounds of feedback, we conducted usability testing with six participants. While users were able to complete most core tasks with ease, the sessions surfaced a few areas where they struggled with, which we addressed later.
After sharing the usability testing results with the broader product organization, we aligned with engineering to assess feasibility and prioritized updates that were low effort and high impact.
Final Designs
All roads lead back to building trust
While users expressed a strong need to control the underlying data, they also found significant value in seeing signals like match reasons and confidence scores, which provided far more context than the previous experience. In the main list view, our goal was to surface as many relevant signals as possible so users could quickly assess matches and take action.
For deeper evaluation, the detail view presented all fields side by side, making it easy to compare records and choose which values to carry into the merged result. A real-time profile preview showed the final outcome as users made selections, helping them understand exactly what would happen before committing a merge.
To further reduce risk, we introduced the ability to unmerge, which was consistently highlighted in interviews as a key driver of confidence. This gave users reassurance they could safely take action, knowing mistakes could easily be reversed.
As trust increased, users could shift to bulk merging for greater efficiency. While most preferred to carefully review each merge, a segment of users had no time to inspect and wanted to act immediately. Supporting both behaviors was essential for us to meet different user needs.
Impact
Measuring impact through quantitative and qualitative feedback
Within three months, merge completions increased 3.4x, driven by a 530% increase in company merges and a 200% increase in people merges, exceeding our adoption goals.
Within six months, duplicates also decreased by 55%, which demonstrated significant improvements in data quality.
After launch, we received a lot of positive feedback from users, including ones from our Big 10 Customers, whom we consistently prioritized due to their significant impact on our revenue.
