Information Architecture 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 information architecture (IA) research and design processes to enhance the website customer experience.
Information Architecture 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 information architecture (IA) research and design processes to enhance the website customer experience.
Information Architecture 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 information architecture (IA) research and design processes to enhance the website customer experience.
Background
Nomat was engaged by the retailer to complete 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 focused on improvements to a specific section of the website and product catalogue.
Background
Nomat was engaged by the retailer to complete 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 focused on improvements to a specific section of the website and product catalogue.
Background
Nomat was engaged by the retailer to complete 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 focused 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, alongside members of the digital customer experience team. This allowed Nomat to explore 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 or opportunities within the IA. Additionally, we considered business insights such as top performing areas and high priority products. These findings helped to inform the research questions for the project. Research activities were undertaken to inform adjustments to the IA. This research helped consider how products were organised into groups (categories and subcategories), and what language was used to name each group (taxonomy).
What we did
The project commenced by engaging with internal stakeholders and teams responsible for the product area, alongside members of the digital customer experience team. This allowed Nomat to explore 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 or opportunities within the IA. Additionally, we considered business insights such as top performing areas and high priority products. These findings helped to inform the research questions for the project. Research activities were undertaken to inform adjustments to the IA. This research helped consider how products were organised into groups (categories and subcategories), and what language was used to name each group (taxonomy).
What we did
The project commenced by engaging with internal stakeholders and teams responsible for the product area, alongside members of the digital customer experience team. This allowed Nomat to explore 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 or opportunities within the IA. Additionally, we considered business insights such as top performing areas and high priority products. These findings helped to inform the research questions for the project. Research activities were undertaken to inform adjustments to the IA. This research helped consider how products were organised into groups (categories and subcategories), and what language was used to name each group (taxonomy).
In-store customer interviews and online usability testing
Qualitative insights were gathered through in-store interviews and usability testing. For the usability testing, sessions were conducted with customers intercepted at a local 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.
Initial testing results were benchmarked against industry metrics such as the System Usability Scale (SUS), Net Promoter Score (NPS) and Single Ease Question (SEQ) to enable future improvements to be consistently measured. Task completion rates and observed behaviour were used to evaluate usability.
IA research and design activities: Card sorting and tree testing
Card sorting was conducted online, with participants arranging products into groups that made sense to them, before naming those groups. The groups they created and language they used revealed insights into customer mental models about which 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 to locate specific products. Tree testing provided a way to quantitatively understand how effective the draft IA structure was at supporting priority user tasks compared with the existing IA structure. It also allowed for IA changes to be tested in a risk-free environment.
In-store customer interviews and online usability testing
Qualitative insights were gathered through in-store interviews and usability testing. For the usability testing, sessions were conducted with customers intercepted at a local 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.
Initial testing results were benchmarked against industry metrics such as the System Usability Scale (SUS), Net Promoter Score (NPS) and Single Ease Question (SEQ) to enable future improvements to be consistently measured. Task completion rates and observed behaviour were used to evaluate usability.
IA research and design activities: Card sorting and tree testing
Card sorting was conducted online, with participants arranging products into groups that made sense to them, before naming those groups. The groups they created and language they used revealed insights into customer mental models about which 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 to locate specific products. Tree testing provided a way to quantitatively understand how effective the draft IA structure was at supporting priority user tasks compared with the existing IA structure. It also allowed for IA changes to be tested in a risk-free environment.
In-store customer interviews and online usability testing
Qualitative insights were gathered through in-store interviews and usability testing. For the usability testing, sessions were conducted with customers intercepted at a local 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.
Initial testing results were benchmarked against industry metrics such as the System Usability Scale (SUS), Net Promoter Score (NPS) and Single Ease Question (SEQ) to enable future improvements to be consistently measured. Task completion rates and observed behaviour were used to evaluate usability.
IA research and design activities: Card sorting and tree testing
Card sorting was conducted online, with participants arranging products into groups that made sense to them, before naming those groups. The groups they created and language they used revealed insights into customer mental models about which 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 to locate specific products. Tree testing provided a way to quantitatively understand how effective the draft IA structure was at supporting priority user tasks compared with the existing IA structure. It also allowed for IA changes to be tested in a risk-free environment.
Services provided by Nomat
Stakeholder workshops
In-store interviews
Online unmoderated usability testing (via Usertesting.com)
Online card sorting (via OptimalSort)
Online tree testing (via Treejack)
IA and navigation design
Services provided by Nomat
Stakeholder workshops
In-store interviews
Online unmoderated usability testing (via Usertesting.com)
Online card sorting (via OptimalSort)
Online tree testing (via Treejack)
IA and navigation design
Services provided by Nomat
Stakeholder workshops
In-store interviews
Online unmoderated usability testing (via Usertesting.com)
Online card sorting (via OptimalSort)
Online tree testing (via Treejack)
IA and navigation design
Outcome
The electronics category saw a 22% increase in sales after the changes identified within this research were implemented.
Final recommendations to update the product catalogue were made based on findings of the research activities, in conjunction with business needs and priorities. These recommendations were documented in a series of deliverables including an executive research readout, a summary report and several posters. Validated changes to the IA were implemented by the internal team and led to a substantial uplift in revenue.
Outcome
The electronics category saw a 22% increase in sales after the changes identified within this research were implemented.
Final recommendations to update the product catalogue were made based on findings of the research activities, in conjunction with business needs and priorities. These recommendations were documented in a series of deliverables including an executive research readout, a summary report and several posters. Validated changes to the IA were implemented by the internal team and led to a substantial uplift in revenue.
Outcome
The electronics category saw a 22% increase in sales after the changes identified within this research were implemented.
Final recommendations to update the product catalogue were made based on findings of the research activities, in conjunction with business needs and priorities. These recommendations were documented in a series of deliverables including an executive research readout, a summary report and several posters. Validated changes to the IA were implemented by the internal team and led to a substantial uplift in revenue.
Interested to know more? Let’s Talk.
Interested to know more?
Let’s Talk.
Interested to know more? Let’s Talk.