Background and Problem
Snapp operated a 900-agent call center, costing approximately (~$315K monthly, ≈ $3.8M annually).
As the user base grew, call volumes increased rapidly, yet users still waited an average of 5 minutes to reach an agent, causing frustration.
Digital support solutions (in-app ticketing and FAQs) existed but were underutilized (20% adoption). Leadership needed to understand:
Why users avoided digital support channels.
Whether a shift to self-service was realistic.
How to reduce call center costs without harming the user experience.
Goals:
Identify experience-level problems preventing users from choosing digital solutions.
Understand the gap between call center and in-app support usage.
Assess whether it was realistic to expect users to convert to self-service channels.
Metrics
Increase conversion rate to in-app support channels.
Decrease incoming call volume to the call center.
Improve user satisfaction scores in monthly surveys.
Constraints and Challenges:
No tracking tools: Limited ability to observe real user behavior in the app (no event tags, no session replays). Relied on call/ticket tag data and qualitative methods.
Tight timeline: Project delivered in 1 month while managing two other product verticals.
High stakes: Stakeholders required evidence-backed recommendations before committing resources to large-scale changes, creating a need for quantitative validation in Phase 2.
Phase 1
Exploring Barriers to Digital Support Adoption
Content Analysis
What we did:
Analyzed ~87,000 support interactions (14K tickets, 73K calls) from one month.
Reviewed tags provided by the call center (main + subcategories).
Compared frequency of issues across tickets and calls to visualize where users chose calls instead of tickets.
Frequency of user issues by channel, comparing ticket submissions versus call center requests
What we Found:
~40% of call requests overlapped with issues already handled well through in-app tickets (e.g., FAQs, lost & found).
These were mostly non-critical topics, meaning they could have been resolved digitally if users trusted or found the channel easily.
Usability Test & Interview:
What we did:
Recruited 10 active users who had contacted support in the past week.
Conducted in-depth interviews to explore their expectations, frustrations, and decision-making process between calls and tickets.
Ran usability tasks on the in-app support section to observe real navigation behavior, confusion points, and drop-offs.
A UX designer joined sessions as a note taker and observer while I led facilitation and follow-up probing.
Usability test Table included tasks, desired answer, facilitator observation and overall result
What we Found:
Incomplete user flow: Users didn’t know what happens after ticket submission, fearing their issue would remain unresolved.
Poor information architecture: Categories were unclear, overlapping, or in unexpected locations, forcing guesswork.
Weak UX writing: Labels lacked guidance, making it unclear where to go or what would happen next.
Urgent or sensitive cases: Users avoided tickets altogether, preferring calls for speed and reassurance.
Complex process: Uploading documents or extra steps made ticketing slower than calling in some cases.
Insights & Recommendations:
Combining findings from data analysis, interview and usability test revealed that low adoption of digital support is not an awareness issue, but a findability and trust issue:
Information Architecture and UX writing are key barriers to using tickets.
Users need clearer guidance, simpler steps, and visible feedback after submission to trust the process.
Fixing these issues could shift ~40% of calls to self-service channels, reducing operational load and cost while improving user experience.
I recommended:
Testing improvements to IA and wording to align with user mental models.
Validating these changes quantitatively before committing to a full redesign.
Phase 2
Discovering IA Problems.
Why we Ran Phase 2 ?
From Phase 1, we uncovered clear usability issues in the current Information Architecture (IA) that caused low adoption of in-app support.
Stakeholders agreed these issues existed, but a full IA redesign would require significant resources (design time, engineering changes, testing).
Before committing to a costly rollout, leadership needed data proving that a revised IA would deliver measurable improvements in findability, task success, and overall user confidence.
To reduce risk and build confidence, I designed a quantitative validation study, combining Card Sorting and Tree Testing, to test IA improvements before implementation.
Hybrid Card Sorting (Qualitative Exploration)
Goal
Understand how users mentally group support topics.
Process
20 participants, randomly recruited from active users.
In-office moderated sessions where participants grouped and renamed issues into categories that made sense to them.
Used a hybrid approach (open + closed sorting) to allow flexibility while testing pre-existing categories.
Analyzed results with a similarity matrix, clustering cards with ≥60% agreement to propose a new IA structure.
User-driven categorization informed proposed IA.
Outcome
A proposed new IA structure based on user mental models.
Tree Testing (Measuring IA Performance)
Goal
Compare the old IA vs. the new IA for accuracy and efficiency.
Process
97 participants per tree (old IA vs. new IA).
Remote unmoderated test via (Lysnna Usertesting platform.)
Measured task success rate, time to find, and navigation directness.
Confidence level 90%, margin of error 10%.
new IA based on card sorting
Outcome
New IA improved success rate, reduced errors and time-to-find significantly compared to the old IA.
Provided quantitative proof that redesigning IA would create measurable value.
New IA improved success rate and reduced errors.
Impact
Within the first month post-launch:
📈 Digital adoption increased: 20% → 29% usage of in-app support.
📉 Call volume reduced: ~20% fewer calls, saving an estimated $60K/month in staffing costs.
😊 User satisfaction improved: +3% increase in monthly survey scores.
New Version
Old Version
Thanks for reading
Pooria Hassanzadeh
2023