The design of products is a systematic project, and as a link in product design, we are engineers at first, and it is our mission to launch products on time and accurately. In the process, the quality and efficiency of design are our eternal proposition.
What is our identity, where are the qualities of designers, and how should design objectives be set? Let's go together to discuss and polish more exquisite design skills.
Actual combat, actual combat, actual combat, we use cases to tell you the actual combat abilities of interactive designers.
Main contents:
1. Division of labor based on five elements of user experience
1.1 What should PM do and what should designers do? You can be more accurate only when clearly knowing your position.
1.2 Designers' outputs in five elements.
1.3 Case study analyzes our reasonable division of labor in a project to help designers produce better results.
2. Designers effort allocation rules’
2.1 How to comprehend the ROI of demands.
2.2 How can we distinguish the equivalent requirements of ROI?
2.3 Set the principles of efficiency and don't distract your attention endlessly.
2.4 Case study analyzes the energy allocation details of our designers in a project.
3. Find the accurate design principles
3.1 Classification of design principles.
3.2 Case study analyzes several important design principles
4. Define flexible and accurate scenes
4.1 How to define a scene.
4.2 How to use a scene
4.3 Case study analyzes the projects used in scenarios
5. Perfect completion of design tracking
5.1 Reducibility indexes.
5.2 How to verify the design quality.
5.3 AB test also needs to be designed.
5.4 Case study analyzes the ABtest of design
1. Ice-breaking-tell your story
2. Theory and case interpretation - hard capabilities of interactive designers
3. Actual combat - tell me your judgment
4. Review and summary
1. Interactive designers
2. Visual designers
3. Design team management
1. Obtain efficient and accurate working methods
2. Improve the value of individuals in the team
3. Reap the results after big data validation