Defining a Project Direction
Where we’d like to see this project go and what we’ll need to achieve to get there
Our task
The core value proposition that Zensors makes to users is the ability to use the power of crowd computing and machine learning to use video and associated metadata to answer questions about a given physical environment or state. Zensors’ immediate goal is to productionalize this capability and bring it to market, but until now, has had limited insight and attention paid to customer discovery and development.
Over the course of the semester, the MHCI team will determine the most valuable market to target and features to develop to ensure product market fit.
In order to do this, there some key questions that we’ll seek to answer by the end of this semester:
Who are our target users?
What do our target users need from us and what do we need from them?
How do we address the needs of our target users?
How do we plan to scale or sustain service for our users?
Product components
While much needs to be determined about the user experience and is already planned for development within the product itself, a successful Zensors experience will require a cohesive system in order to fit any potential market. As we begin our work understanding how Zensors might best fit user needs, we’ve outlined the core objectives that the system will need to achieve:
Query generation and response
The creation of a question that will render actionable data is critical to the effective and successful use of this system. A range of users will need to understand and be able to take action from the data gathered. Specifically, this component of the product experience will need:
- Clear instruction and onboarding for users to understand what value they can derive
- Error or poor query recovery
- Actionable findings
Crowd analysis
The system depends on the crowd to respond to queries and provide accurate answers. This experience will need to be streamlined and well planned in order to ensure scalability and reliability. Specifically, it will need:
- Clear tasks and next steps
- Little interference between users and submitting responses
- Integrated machine learning and training based on human responses
Research Overview
To start, we’ll conduct a contextual inquiry into this problem space, focusing on a few promising market verticals to understand current processes, constraints, and technology usage. This will entail the application of the following methods:
Interviews and shadowing
Talking to and witnessing firsthand the experiences of potential users will help us to begin to understand the problem space in which Zensors could make the biggest impact.
Research analysis
Using such methods as modeling and affinity diagramming, we’ll derive insights from the user sessions.
Customer discovery
Using an established customer discovery and development framework, we’ll build on the research analysis to devise a series of design, research, and development sprints to validate findings and assumptions.
Coming up
With this direction in place, we’ll get starting on our research and discovery. Look for another post detailing our findings on the product landscape, academic research paper review, and plans for primary research.
