[ ArchiPro ]
[Info]
B2B Business Analytics Product
Sector: Architecture/Design and Build
Client: Archipro
Project Timeline: 12 Weeks
Platforms: Slack, Figma, Figjam, Maze, Otter
2024
​
The ArchiPro Business Analytics project was a 2.0 version of the current Business Analytics tool. With a B2B user focus it’s intention is to provide subscription based suppliers and professionals with real time data and analytics to enable them with tools to enhance their targeting/re-targeting processes, reporting, ROI, ad and campaign strategies, leads funnels and conversions with the overall intention being to communicate profile value.
​
This was an extensive project which had been in the road-map for an extended period of time. I came on as a UX designer to implement a research strategy to gain insights into the users, to prototype and user test the product to then have it be approved and viable for final development.
This was a great example of seeing how the user research process and data can really help inform clear design changes to steer a project to final sign-off.
​
*Imagery has been blurred to respect company IP.
​
​​
My Role:
​
Research Lead. UI Designer.​​
​
-
Understand and design with key advertising, marketing, ad tracking and engagement data and analytics (conversions, prospects and leads funnels, demographics, ROI/ROAS, benchmarking, e-commerce, product tagging, campaings/ads)
-
Stake holder engagement
-
Research
-
Lead interviews and meetings
-
Create and action timelines
-
Conduct user research from beginning to end
-
One to one Interviews
-
Focus Groups
-
Competitor analysis
-
Affinity mapping
-
Synthesising and analysing data
-
User Personas
-
Ideation
-
Information Architecture
-
UI
-
Collaboration and handover with UI designers
-
Integrate key data into the UI
-
Hi-fi prototype​
Key Goals:
-
Implement a research strategy
-
Understand the user, their key needs and pain points
-
Collate a user database/contact list
-
Set interviews with users
-
Conduct one to one interviews and focus groups
-
Conduct user testing with Hi-fi prototypes
-
Affinity map, sort and analyse data to pin point pan points, needs, and potential opportunities
-
Track and heat map users interaction with pages and data
-
Split data into actionable lists and handover/implement with UI designers
-
Extract user behaviors from affinity map data
-
Create user personas for: Marketing, sales/business development, the architect, the designer, office manager/multiple hats, new user, business owner, manufacturer and the reseller
The Solution:
​​
User research: Through implementing an essential user research process (which hadn’t previously been done for this company or project) we were able to extract fundamental user data and insights, integrate that information into the current state of the Business Analytics product which consequently ​improved output and moved the prototype into final development.
​
User personas: The creation of user personas to assit internal teams in understanding who they’re talking to, who they’re targeting in ad campaigns and who they’re designing for.
​​
Hi-Fi Prototype
*To respect IP the pages above are blurred and are not the final prototype, they are part of the development stage while data was being integrated into the designs.
Research
Research was conducted through 19 one to one interviews and focus groups, hi-fi prototype user testing and Maze prototype testing with internal stakeholders. This data was then placed and sorted into an affinity map and user pain-points and actionable tasks were then extracted and implemented into the UI. User behaviors were then also pulled from the data to be cross-pollinated with the User Personas and during the research phase the user product navigation was also mapped while they interacted with the prototype so that we could pinpoint which pages and data were of highest importance to particular groups so that could also be inserted into the User Personas.
User Personas
11 User Personas were generated from the user research. This was a rigorous task in itself. The research resulted in a realisation that there was a more robust list of users than we originally perceived so extracting accurate information from each interview conducted and seeding it into the correct user persona was a meticulous job but one which hadn't been done before and the company will see benefit from.