The Strategic Approach to Building Machine Learning Models (Part 1/9): Understanding the Business/Product Requirements

Introduction

The building of a successful Machine Learning (ML) product begins with a fundamental yet frequently underestimated step: comprehensively understanding the business and product requirements. This pivotal phase lays the foundation for the project's trajectory, feasibility, and ultimate value to stakeholders. In this article, we will explore the significance of this, emphasizing the importance of focusing solely on the problem and desired outcomes, without prematurely discussing solutions.

Understanding Business/Product Requirements

1. Grasping the Stakeholders' Vision: Effective communication with stakeholders is vital. It’s about capturing their vision and expectations and understanding the impact they foresee from the ML product. Crucially, this is not the stage to propose solutions but to align the project with the overarching business objectives.
2. Defining the Problem, Not the Solution: Clear problem definition is key. This step requires asking the right questions to ensure a thorough understanding of the issue at hand. It's essential to resist the urge to jump into solution mode; the focus here is to identify what needs to be solved, not how to solve it.
3. Identifying the Target Users: Understanding the end-users of the model is crucial. This knowledge influences the design and functionality of the ML application, ensuring it addresses the real needs of users. Again, the focus is on understanding these needs, not on how to meet them just yet.
4. Aligning with Business Strategy: The project’s objectives must synchronize with the broader business strategy. This alignment ensures the project contributes meaningfully to the company’s overall goals, emphasizing understanding the strategic fit rather than devising the technical approach.

Tools for Gathering Product Requirements

• Stakeholder Interviews: Conduct thorough interviews with all key stakeholders, focusing on uncovering explicit and implicit requirements, expectations, and visions for the project. Avoid the inclination to discuss potential solutions during these conversations.
• Requirements Meetings: Organize meetings with diverse stakeholders to brainstorm and build a shared understanding of the product's goals. Use these meetings to gather various perspectives on the problem, not to explore potential solutions.
• Competitive Analysis: Analyze the market and competitors’ use of ML. This step is to understand the landscape and identify gaps or opportunities, without yet considering how your project will address these gaps.

Hypothetical Machine Learning Problem

FashionFiesta seeks to develop a computer vision system that allows customers to virtually try on clothes. The goal is to reduce return rates and improve customer satisfaction by providing a more interactive and personalized shopping experience.

Understanding Product Requirements for FashionFiesta’s Product

1. Grasping the Stakeholders' Vision: The Product and ML teams conduct interviews with FashionFiesta’s management, marketing team, and customer service representatives. They gather insights on expected outcomes like decreased return rates and increased customer engagement. The discussions revolve around understanding the need for such a system and its expected impact, rather than how the system will be developed.
2. Defining the Problem, Not the Solution: The problem is identified as "How can we enhance the online shopping experience to reduce return rates and increase customer satisfaction?" At this stage, the team deliberately avoids discussing potential computer vision technologies or algorithms. The focus is on understanding the issue in-depth.
3. Identifying the Target Users: The team conducts surveys and focus groups with a sample of FashionFiesta’s customers to understand their online shopping behaviors and challenges. They also gather information on customer expectations and experiences with online shopping, not on how a virtual try-on feature should be designed.
4. Aligning with Business Strategy: The team ensures that this project aligns with FashionFiesta’s overall strategy of leveraging technology to enhance customer experience. They discuss how the virtual try-on feature could integrate into the current platform and contribute to the company's long-term growth, without delving into technical specifics.

Practical Steps for Understanding FashionFiesta’s Requirements

1. Stakeholder Interviews: The Product and ML teams focus on understanding the different perspectives on the need for a virtual try-on feature, avoiding premature solution discussions.
2. Requirements Meetings: Meetings are organized with stakeholders to brainstorm the desired outcomes of the project. The team focuses on what success looks like for this initiative.
3. Competitive Analysis: The team analyzes competitors who have implemented similar features, focusing on understanding their impact on customer experience and business metrics. There should also be a focus on understanding the gaps in the competitors existing solutions so FashionFiesta's product can better address that gap.

By thoroughly understanding the business and product requirements for FashionFiesta’s machine learning problem, the ML team sets a solid foundation for the product. This approach ensures that the subsequent phases of the product, which will involve choosing the right technologies and designing the solution, are aligned with the company’s objectives and customer needs. It exemplifies how, in ML products, a deep understanding of the problem and desired outcomes is crucial before jumping into solution development.

Conclusion

Understanding the business and product requirements is the cornerstone of any ML project. It’s about laying a solid foundation based on clear communication, comprehensive problem understanding, user-centric focus, and strategic alignment - all without the distraction of solution discussions. By dedicating time and resources to this phase, businesses can significantly enhance their ML product’s chance for success, ensuring it delivers tangible value and aligns seamlessly with their broader objectives.