Reducing churn by improving data visualization for affiliate program managers.

LeadDyno is an industry leader in affiliate tracking technology and affiliate program management. Since 2014 it has tracked over 309M+ referrals and recorded over $161M in client sales in 2022 alone. In 2021, it was acquired by Sureswift Capital, where I served as UX Designer & Researcher. After the acquisition, I joined the LeadDyno team to help inform product strategy and understand LeadDyno's +2,000 customer base working closely with product and engineering on UX/UI design initiatives.

Timeline
2022-2023
My Role
UX Designer
Methods
Wireframes
User Interviews
Survey
Concept Testing
Team
Product Manager
Project Manager
UI Designer
4 Developers
Customer Support
Growth Marketing
Impact
Lowered Churn, Saved Time, Maximized Happiness
-11%
Expected Churn
Decrease
82%
Customer Satisfaction
(CSAT)
+25%
Monthly Recurring
Revenue

"The feedback on this initiative has been amazing – customers say it’s everything they need."
- Brooke Hahn, CEO

Problem
Affiliate Program Managers Struggle with Understanding and Accessing Reporting Data
Churn was slowly increasing month over month, with users citing frustration over their inability to drill into granular program details like affiliate performance and purchase data. Even more, generating a data export required users to submit help desk tickets which impacted the support team with busy work.
You know it's serious when I break out custom memes in my presentation. Seriously though, churn is an issue. Let's fix it.
Legacy view of the visitors dashboard.
Assumptions
Areas of Improvement

Export FunctionalityIntroduce basic export options for key data like purchases and affiliate details, so users can generate custom reports.

Visual AlignmentAddress visual inconsistencies in the UI to create a more cohesion.

Historical ComparisonEnable static metrics to display comparisons over time (e.g., "This month's purchases vs. last month").

Advanced AnalyticsImplement trend analysis tools to provide users with actionable insights beyond static data reporting.

User Interviews & Survey
Finding the Root of User Frustration Through Feedback & Research.

To kick off my research, I began by combing through help desk tickets to identify users who had a ticket history related to data or reporting. This method helped streamline my recruitment process for the interviews and essentially screen for potential participants to reach out to about research initiatives.

Matt was a user that I had reached out to because of a previous reporting concern. I started my career in customer support and it's one of my favorite ways to jump in and start understanding issues is by talking directly with users.

I worked with the product manager and our development team to generate a list of users who fit within our criteria as well as the help desk users I identified.  We targeted users who met key criteria, for example: program generating a minimum of 'X' ARR and having an active account for at least one year. With a list of potential interviewees, I created an email campaign in collaboration with Marketing to execute the outreach.

I remotely conducted 8 out of 10 moderated user interviews. Although scheduling conflicts prevented us from completing all 10 sessions, we were fortunate that users were willing to provide specific feedback without meeting.

After the interviews, I synthesized the interview data and presented the insights to the team and led a survey initiative targeting 100 power users. The goal was to determine if the broader user base shared the same sentiments uncovered during our interviews.

"I have to contact support to get the data I want, and when I do get it, I always have to spend time in Excel cutting up the report. I can do it but It’s so tedious.”

- User Interview

“The data I have now doesn’t tell me much, I have no idea who or how my affiliates are doing. It would be great to be able to see exactly how much of my sales is coming from them.”

- User Interview

+300
Tickets Reviewed
8
User Interviews
1
Survey
89%
Response Rate
ideation
Crafting the Solutions: Concept to Delivery
In shaping the solutions, I began by closely examining the research insights to identify the core frustrations users faced with the current reporting system. Developing key questions for each reporting section, focusing on what users most needed to know to achieve their goals.

By centering the design process around these essential questions, we ensured that the solutions were not only functional but also highly relevant to the users' needs. After we established feasibility and viability, I worked closely with the UI designer, creating and refining components and translating the wireframes into high-fidelity mocks.
The reporting overhaul consisted of multiple screens, ranging from the main dashboard, leads, customers, purchases, affiliates, and commission dashboards.
Early wireframe iteration for the purchases screen. This concept utilized interactive KPI indicators that the user could turn on/off on the supporting chart. We moved away from this due to the complexity and opted for a simplified card approach that more visually digestible.
Solution
Overhaul data visualization for reporting dashboards
Provided a comprehensive dashboard that displays both overall purchases and affiliate-specific transactions, enabling managers to quickly gauge performance at a glance. The solution also includes purchase attribution in a table format, allowing managers to filter or export data as needed for a detailed evaluation of individual affiliates.
Impact
Empowered managers to quickly identify trends and discrepancies in both overall and affiliate-specific performance. By enabling purchase attribution and enhanced data filtering and export capabilities, managers gained deeper insights, allowing for more informed decision-making and improved strategic planning.
The final design for the purchases dashboard.
Solution
Support custom data exports
Introduced custom data export functionality, enabling users to select specific criteria and generate personalized reports directly from the platform.
Impact
This solution eliminated the need for support agents to act as intermediaries, providing program managers with direct, on-demand access to their program data. It also freed up valuable time for both the users and the internal team, improving overall efficiency and user satisfaction.
Final UI for CSV exporting
Impact
Lowered Churn, Saved Time, Maximized Happiness
-11%
Expected Churn Decrease
82%
Customer Satisfaction (CSAT)
+25%
Monthly Recurring Revenue

"The feedback on this initiative has been amazing – customers say it’s everything they need. This is very helpful for the team, as they can finally look at the tool’s many features and focus on those they know customers are really looking for."

- Brooke Hahn, CEO

next steps
Challenges, Considerations, & Constraints

Usability Testing

Due to time constraints, I wasn't able to test prototypes with users. If I had to go back, I would prioritize another round of testing prior to developer handoff. I was able to gain feedback about our early high-fidelity designs from users we had previously interviewed which garnered positive feedback and continued to work with the PM to conduct flash interviews with users for our design concepts.

Rollout Strategy

Reporting consisted of various screens; visitors, leads, customers, purchases, commissions, and affiliates. Rather than bundling them as one large update, we opted for a phased approach over the next several months. In order to introduce the updates to users, I worked closely with the PM and growth team to design comprehensive communication releases to users as well as design a tool-tip walkthrough for users that see the updated designs for the first time.

Feedback

We knew the risk of forgoing usability tests, and focused on functionality expecting corner cases to surface. We wanted to set the expectation for our users that it would not be a perfect release, so we branded the redesigned screens as beta to help ease the transition. I set up an in-app survey using a tool called Refiner to solicit feedback through in-app surveys and encouraged users to contact the team about any bugs or issues. All of this feedback was stored in a research file for iterating.