Overview
It is not the most glamours task but in todays experience-driven economy, understanding user needs is paramount. The product team at ZOLEO recognized this, and embarked on a mission to refine their UX Tracker. I was tasked to refine ZOLEO's massive dataset — over 12,000 user feedback points culled from Zendesk.
By harnessing this wealth of user-provided data, I aimed to create a powerful, filterable UX Tracker that empowers stakeholders to ensure data-driven, user-centric decisions for ZOLEO's future.
The problem
The initial iteration of the UX Tracker, intended to assess the business value of a redesign, had been neglected and was nearing its capacity. Due to the lack of dedicated resources and the growing volume of data, the tracker was unable to provide the insights needed to inform decision-making. Additionally, its manual processes hindered its ability to deliver timely and accurate information to management.
Challenges & solutions
Challenge: The UX Tracker was overwhelmed by the constant influx of data from the Customer Service Zendesk, making it difficult to extract meaningful insights. This was due to its original design, which captured a broad range of data without considering the specific needs of the design team and project stakeholders.
Solution: To address this issue, I implemented a data reduction strategy.
Removed Technical issues
Removed Hardware issues
Removed incomplete tickets
By refining the data collection process and eliminating irrelevant categories, I significantly reduced the volume of data stored in the UX Tracker. This not only improved the tracker's performance but also made it easier to analyze and extract valuable information.
Challenge: The UX Tracker required significant manual effort to generate reports on loss-gains, deactivations, and user experiences. This was due to the team's lack of expertise in using Airtable for automation.
Solution: To automate the reporting process, I introduced a granular tag list to index tickets effectively. This allowed me to create a manual report dashboard as a first step. To enhance the accuracy and relevance of the metrics, I further refined the tag categories, focusing on those that were considered critical business indicators. This structured approach laid the foundation for future automation efforts.
Building continuous monitoring
The newly implemented tagging system is the stepping stone for continuous optimization by regularly reviewing and making data-driven adjustments. This will be achieved with periodic scheduled reviews, followed by stakeholder feedback to refine the system, ensuring alignment with evolving needs. This refined system unlocks automation potential:
Automated Reporting: Track credits, refunds, and cancellations efficiently.
Detailed Insights: Generate reports on cancellation reasons and trends for improved retention strategies.
Geographical Analysis: Capture location data to identify and address regional user experiences.
Improved User Feedback Analysis
To obtain business relevant metrics from user feedback, we implemented a targeted tagging system that identified Cancellation Differentiation and Refund & Credit Tracking.
This resulted in actionable insights for stakeholders with comprehensive data on churn (cancellations, returns, refunds/credits) provided a clearer picture of customer behavior. An initial report showcased the impact of cancellations and refunds/credits, aiding communication and progress tracking.
Ticket summary issues
As the improvements continued I discovered inconsistent language and ticket formats by the Customer Service, this ultimately hindered automation. In order to ensure future tickets would abide by a set of rules to ensure continuous automation, I developed standardizing terminology by collaborating with the Customer Service stakeholder. Examples were to define consistent terms for credits, refunds, and "App to App. Another discussion surrounded Improving Ticket Summaries. Together with our Customer Service liaison I developed a training manual for Customer Support outlining clear guidelines, standardized terminology, and baseline requirements for summaries.
User stories become user-centered solutions
The UX Tracker allowed tickets to become converted into User Stories. The user stories served as a vital bridge between user feedback and actionable solutions. As the data analyst, I played a crucial role in indexing and extracting insights from these user stories to address specific problems identified by the team.
By delving into the user stories, I was able to:
Identify User Pain Points: User stories provided a window into the challenges and pain points faced by our users. This understanding of user needs formed the foundation for developing user-centered solutions.
Prioritize User Stories: Not all user stories held equal weight. By analyzing the frequency and impact of reported issues, we prioritized user stories to ensure development efforts were directed towards the most critical user needs.
Fuel User-Centered Design Decisions: The insights gleaned from user stories empowered the team to make informed design decisions that addressed specific user pain points. This resulted in a more user-centric product experience.
User story: "I made multiple payments by accident and would like to get a refund / credit"
Elderly user accidentally made double payment ($5,250) instead of monthly fee ($26.25).
Solution: Introduced a modal during payment:
Triggers: Appears for high amounts exceeding standard plan fee.
Dual Warnings:
Overpayment: Warns user if exceeding intended amount.
Double Payment: Highlights existing payment and potential overpayment for duplicate entries.
User Story: "I don't understand my invoice / how I'm being charged"
Root Cause: Invoice lacked transparency: no breakdown of plan, top-up costs, or taxes.
Solution: New invoice overview with "See More Details" option:
Expands to show:
Plan details (fees & features)
Feature usage costs
Top-up bundle details
Tax breakdown
Takeaways
Data Blindness Hinders User-Centered Design
Without readily available user data, businesses face significant challenges in understanding user needs and making informed design decisions. This lack of data transparency can have detrimental consequences, including:
Missed Opportunities: Valuable insights about user behavior and preferences may be overlooked or forgotten over time, preventing businesses from identifying opportunities for innovation and improvement.
Outdated Information: As user behavior and business priorities evolve, relying on outdated data can lead to design decisions that are no longer relevant or effective. This can result in solutions that fail to meet user expectations and deliver a positive experience.
Increased Costs: Ineffective design decisions can lead to higher development costs, increased customer support efforts, and lower user satisfaction, ultimately impacting the bottom line.
Data Blindness Hinders User-Centered Design
To bridge the gap between data availability and user-centered design, I implemented a two-pronged approach:
Data Preparation for Automation: Recognizing the long-term benefits of automation, I focused on preparing the data feed to be compatible with automated systems. This involved ensuring that the data was structured in a way that would facilitate the efficient generation of actionable insights at scale.
Short-Term Reporting with Tags and Keywords: While automation was not immediately feasible, I implemented a system of tags and keywords to enable the creation of short-term reports. This provided the business with critical, up-to-date insights into user issues and financial metrics, informing design decisions in the interim.
Impact on User Experience
This data-driven approach directly influenced the design process. By analyzing the restructured data feed, I gained a deeper understanding of critical user issues, which I was able to address proactively during the ideation phase. This in-depth knowledge of user pain points ensured that they remained a central focus throughout the design process, ultimately leading to the development of more user-centered solutions.