Optimising Digital Self-Service: Reducing Costly Call Centre Volume Through Information Architecture Transformation
The Commercial Context (The Problem)
The hypothesis was that customers were confused about their claim statuses throughout their claims process after applying for compensation. I conducted various research to validate this hypothesis. Using data and insights from sources such as Hotjar surveys, Google Analytics and Call centre call logs, it became clear that there was in fact 3 separate issues. Whilst I conducted interviews with call centre colleagues, it became even clearer that this was an old and ongoing problem that had been overlooked for a very long time. I decided to address this issue by analysing the current content and communication regarding the subject and made a proposal.
By allowing users to quickly access the right information when they want, customers no longer had to call in, leave messages on forms and chats which costs hours for colleagues to respond to.
The Challenge
A digital platform suffered from severe information overload, fragmented navigation, and a broken information hierarchy. This left customers confused, lost and having to call in to the call centre to find very simple information about their claims status.
The Business Impact
Unable to locate critical information or complete tasks self-sufficiently, users defaulted to high-cost support channels. This triggered an expensive surge in call centre volume, strained customer support operations, and lowered overall customer satisfaction (CSAT) scores.
Data Diagnostics & Research (The Processs)
Quantitative Baseline: Analysed top user exit pages and search queries within Google Analytics to identify where users were abandoning the site out of frustration. Looked at search queries and their respective results.
Measure call center queries and quantified the data
Qualitative Insights: Conducted targeted usability testing and session recording reviews (via Hotjar / user interviews). This revealed that the existing navigation structure mismatched the users’ mental models, forcing them to give up and pick up the phone.
The new hypothesis
By restructuring the platform’s information architecture (IA) around core user intent, we could increase self-service completion rates and successfully deflect unnecessary support tickets and phone calls.
The Optimisation Strategy (The Solution)
Information Architecture Overhaul: Designed and validated a streamlined navigation system using card sorting and tree testing to ensure logical content categorization.
Streamlined Information Flow: Stripped away redundant content and established a strict visual hierarchy, prioritizing the top 20% of content that addresses 80% of user queries.
Frictionless Navigation: Implemented clear contextual help, prominent search functionality, and intuitive pathways directly guiding users to self-service resolutions.
The Measurable ROI (The Results)
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Operational Efficiency: Drastically reduced the volume of “frequently asked question” calls entering the customer support queue.
Channel Deflection: Successfully shifted user behaviour toward digital self-service, freeing up call centre agents to handle complex, high-value inquiries.
Product Success Metrics: Achieved a significant drop in support-page bounce rates, a lift in task completion scores, and a measurable reduction in customer service operational costs.
The user’s 123 steps
