Heuristic Evaluation
Before conducting user interviews, I performed a heuristic evaluation of the Mit YouSee app to identify the main usability issues and gain a better understanding of what to focus on during user research.
The website's design and content were outdated, and I noticed several usability problems that affected clarity and transparency.
| # |
Step |
Focus |
Heuristic & finding |
What can I do? |
| 1 |
Identify the flow |
The "Regning 38" label with no context |
Match between system and real world — uses internal numbering like "Regning 38" instead of showing the billing period or total amount upfront. |
Replace bill numbers with clear billing periods and show the total amount directly on the card. |
| 2 |
Evaluate feedback |
The "Ikke betalt" badge |
Visibility of system status — says "not paid" but gives no amount, no due date context, and no breakdown of what the bill contains. |
Show the amount owed and a brief summary of charges next to the status badge. |
| 3 |
Check error prevention |
"Betal nu" and "Vis" buttons |
Error prevention — "Betal nu" (pay now) is prominent, but users can pay without understanding the charges. No "Explain this charge" option exists. |
Add an "Explain this charge" button that triggers an AI assistant to break down the bill before payment. |
| 4 |
Assess help |
No in-context help or AI explanation |
Help and documentation — there is no contextual help on the billing page. The only option is to leave and contact support. |
Integrate an AI assistant directly on the billing page that reads the user's data and explains charges in plain language. |
Survey
22 respondents · Mobile operator users in Denmark · Pre-interview survey
I created this survey to validate my assumptions about user frustrations and behaviors before starting interviews.
My goal was to understand how people actually interact with their mobile operator's app and what triggers their need for support.
Question 1
What do you most often use your mobile operator's app for?
Multiple answers allowed · 22 respondents
How it helped me
When I saw that billing and data usage were the top actions, it confirmed that billing clarity should be a core focus in my redesign.
Question 2
Have you ever contacted your mobile operator's customer support?
22 respondents
How it helped me
The fact that 82% had contacted support showed me that support frustrations are widespread — which later informed the "Generic Chatbot" and "Long Wait" visuals.
Question 3
What was the main reason you contacted support?
Of the 18 who contacted support
How it helped me
When 50% said billing issues, it validated my decision to create the "Confusing Bill" visual and focus on simplifying invoices.
Question 4
How was your experience with the support?
Of the 18 who contacted support
How it helped me
Seeing that 77% had a negative experience helped me shape the emotional tone of the project — frustration, waiting, and lack of clarity.
Question 5
Would you trust an AI assistant in the app to help with your issue?
22 respondents
How it helped me
The 73% openness to AI gave me confidence to design an AI billing assistant as a realistic and desirable solution.
Interview
I wanted to understand different types of user behaviour, so I interviewed:
Mobile-service users
3 people
People who prefer calling
2 people
Rather than sticking to strict, predefined questions, I let the discussions flow so participants could share their real experiences and frustrations.
From this research, I gained a clearer understanding of what users value in a mobile operator — the elements that make the service feel trustworthy, transparent, and effortless to use.
So I identified four elements that users value the most — and I reflected these directly in the Bill page and the AI support flow, where the pain actually happens.
Clear billing
Helps users understand charges and avoid confusion
Self-service tools
Allow users to solve issues independently without calling support
Fast support response
Reduces waiting frustration and builds trust
Transparency
Communicates honesty and reliability in every interaction
Competitive Analysis
I analyzed 4 Danish mobile operators' apps, examining their billing features, support channels, and AI capabilities. This helped me identify a clear gap in the market and confirm which features would meaningfully differentiate the Mit YouSee experience.
| Feature |
Telenor |
Telia |
3 (Tre) |
CBB |
| Bill breakdown |
Itemised by service, moderate clarity |
Grouped by category, moderate detail |
Basic summary, limited detail |
Simple and clear — few services, few charges |
| AI chatbot |
Rule-based bot, no account data access |
FAQ-based bot, no personalization |
Basic chatbot, redirects to phone support |
No chatbot — email/web form only |
| Billing explanation |
None |
None |
None |
None |
| Self-service billing |
View & pay only |
View & pay only |
View & pay only |
View & pay only |
| Proactive notifications |
Usage alerts + payment reminders |
Usage alerts + payment |
Payment reminders only |
Minimal |
Key Insight
"No Danish mobile operator offers an AI assistant that can read the user's personal billing data, explain specific charges, and escalate to human support — all within one seamless in-app flow."
Market gap identified through competitor analysis
No operator explains billing
Every app lets you view and pay — but none help you understand what you're paying for. This is the gap my design fills.
AI chatbots are generic everywhere
No Danish operator uses AI that can read your personal account data and give specific answers. My design introduces a context-aware AI assistant.
Support still means waiting
Even operators with decent apps force users to call or wait for chat. My design keeps users in the app with AI + human escalation built in.
