David Randall PhD 👋
A Passionate User Research Specialist with 15 years of Experience conducting research in academia and business.
Barclays App IA
Overview
The Barclays App includes a comprehensive Products and Rewards section that provides customers with a range of benefits and services. Ahead of launching several new products, we needed to re-evaluate the existing information architecture to ensure the new offerings were logically grouped and easily understood by customers.
Client:
Barclays
Time Frame:
August 2020
Tools:
UserZoom, Adobe Analytics
Card Sorting
The Barclays App features a Products and Rewards section showcasing the range of products available to customers. With several new products due for release, research was commissioned to explore how the page’s information architecture could be improved. This included examining how products could be grouped into new or existing categories, and identifying which offerings were most relevant to customers’ everyday app use.
Objective:
- 🎯 Understand how customers perceive and group existing products.
- 🎯 Identify which products users consider most important to their needs.
- 🎯 Determine the most intuitive way to categorise upcoming products.
Process:
- ⚙️ Open Card Sort & Top Task: 25 Barclays App users grouped products into their own categories and ranked them by likelihood of use.
- ⚙️ Clarifying Questionnaire: 100 participants answered targeted questions about the new ‘pack’ product offerings.
- ⚙️ Closed Card Sort & Top Task: 25 users sorted products into predefined categories and ranked them again by relevance.
Methodology:
- 🧪 Pilot Study: Ran a small pilot (6 participants) which revealed confusion around new products. This led to a clarifying questionnaire to provide context and suggest category labels.
- 🧪 Open Card Sort: Participants grouped existing products into custom categories and labelled them.
- 🧪Clarifying Questionnaire: Collected naming preferences for new product groupings between card sort phases.
- 🧪Closed Card Sort: Validated groupings from the open sort using all products, including upcoming ones.
- 🧪Top Task: Participants selected their top 5 most relevant products for the next 12 months, then repeated the task for their least relevant ones, explaining their choices.
- 🧪Analytics: Supplemented findings with behavioural data from 3.5 million iOS app interactions to understand actual product engagement.
Results/Conclusion:
The research led to a recommended structure for the new Products page, featuring high-level categories with naming conventions informed by the open card sort and questionnaire. Products were grouped based on card sort results and prioritised using Top Task data. While proprietary constraints limit further detail, the recommendations were implemented, and follow-up research confirmed improved user satisfaction with the new structure.
