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CRD

For a client in the debt collection industry, we developed a unique concept of a debt exchange, and then we were responsible for comprehensive implementation - from UX design, through UI, to deployment on the Magento platform.

Lead Time:
Year of implementation:
2014
CRD
The specificity of the industry in which the client operates required us to carry out an in-depth analysis of the processes and requirements of end users, which we realized during interviews and research
The specificity of the industry in which the client operates required us to carry out an in-depth analysis of the processes and requirements of end users, which we realized during interviews and research
Special attention at the design stage required designing a layer of presentation of key information on the platform
CRD
The specificity of the industry in which the client operates required us to carry out an in-depth analysis of the processes and requirements of end users, which we realized during interviews and research
CRD
Special attention at the design stage required designing a layer of presentation of key information on the platform
CRD
The large amount of key data contained in one view meant that the visual layer had to be characterized by minimalism and clarity

01.

1. PREDESIGN analysis‍

At this stage, we systematize business requirements, analyze the market environment, and define the needs and expectations of end users

02.

2. Strategic concept

Based on the gathered information and data from the analytical stage during workshops with the client, we develop a strategic concept for the proposed solution

03.

​​3. Information Architecture and wireframing

Before proceeding to the final project design, we develop the structure of the solution and define the key relationships between the elements of the proposed solution

04.

4. Graphic Design

Based on the guidelines and semi-finished products developed in earlier stages, we create the final graphic design of the solution

05.

5. Testing and optimization

The generated projects undergo testing with users, and after implementation, we carry out optimization based on available quantitative and qualitative data sources

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