IA Research and Design
A major Australian retailer sought assistance to improve the findability of products on their website and ultimately increase sales. Nomat undertook a range of IA research and design to enhance the website customer experience.
Nomat was engaged by the retailer to undertake a series of research projects to inform improvements to the existing IA of their website. The objective was to make adjustments to the IA so that it better supported customers to intuitively locate relevant products. Each project focussed on improvements to a specific section of the website and product catalogue.
What we did…
The project commenced by engaging with internal stakeholders and teams responsible for the product area and members of the digital customer experience team. This explored business priorities and needs from the IA, as well as any current strategies and priorities relevant to the IA. We also gathered stakeholder hypotheses regarding problem areas of the IA, opportunities and identified relevant business insights such as top performing areas and priority products. These informed the research questions for the project.
Research activities were undertaken to inform adjustments to the IA that would in terms of how products were organised into groups (categories and subcategories), and what language was used to name each group (taxonomy).
In-store customer interviews & online usability testing
Qualitative insights were gathered through in-store interviews and usability testing conducted with customers intercepted at a Kmart store. These exploratory discussions uncovered customer expectations and needs from the website IA in terms of browsing for products. The effectiveness of the existing IA was then further investigated through online unmoderated usability testing.
IA research & design activities: card sorting and tree testing
Online card sorting was conducted which participants arranging products into groups that makes sense to them and naming those groups. The groups they create and language they use revealed insights into customer mental models about what products belonged together and why. This was used to inform refinements to the organisation of products on the website and the language used in the navigation menus.
Based on the analysis and insights from the research activities, a draft IA structure was created and then benchmarked against the existing IA structure. This was done using a quantitative activity called tree testing where a sitemap is tested with customers who are given a series of tasks asking them to locate products. Tree testing provided a way to quantitatively understand the how effective the draft IA structure was at supporting priority user tasks compared with the existing. It also allowed for IA changes to be tested in a risk-free environment.
A 22% increase in sales of Electronics products resulted from implementing the changes identified from the research.