The Power of User Research in Lean Startup and ML Model Explainability


Hatched by Glasp

Aug 16, 2023

4 min read


The Power of User Research in Lean Startup and ML Model Explainability


In today's fast-paced business environment, innovation plays a crucial role in the success of startups and established companies alike. To achieve sustainable innovation, organizations have started adopting methodologies like Lean Startup and incorporating Machine Learning (ML) techniques. However, two critical areas often pose challenges in these approaches - user research and model explainability. In this article, we will explore effective methods for user research in Lean Startup and the combination of Semantic Web Technologies and ML for model explainability.

User Research in Lean Startup:

Lean Startup is a methodology that focuses on customer-centric decision making and rapid innovation cycles. To implement Lean Startup effectively, UX and product designers rely on a user research technique called Lean UX. According to Jenice, a renowned expert, avoiding quantitative survey-type user research and instead conducting qualitative research, such as interviews, is crucial for successful customer development. Rather than merely describing the demographics and challenges of users, it is essential to define how specific hypotheses can be proven correct through research.

Best Practices for User Interviews:

Conducting user interviews is a vital part of effective user research. However, it is crucial to avoid certain pitfalls and follow best practices. Here are five things you should avoid during user interviews:

  • 1. Talking about the product: User interviews should focus on understanding the user's needs and challenges, not selling the product.
  • 2. Asking about future actions: Instead of asking users about their future behavior, focus on their current experiences and pain points.
  • 3. Pitching or leading questions: Avoid asking questions that steer the user towards a specific answer.
  • 4. Talking too much: Let the user do most of the talking, as their insights are invaluable.
  • 5. Failing to take notes: Take detailed notes during the interview to capture important information accurately.

On the other hand, here are five things you should do during user interviews:

  • 1. Take notes: Document key points and insights during the interview to reference later.
  • 2. Maintain a positive demeanor: Keep a friendly and approachable attitude to ensure the user feels comfortable sharing their thoughts.
  • 3. Ask open-ended questions: Encourage users to provide free-form responses by asking questions that don't have a simple yes or no answer.
  • 4. Elicit stories: Stories provide rich insights into the user's experiences and emotions.
  • 5. Listen attentively: Give the user your undivided attention and actively listen to their responses.

Debriefing for Effective Analysis:

After conducting user interviews, the data collected needs to be analyzed and organized. Debriefing is a crucial step in this process. It involves summarizing and categorizing the information gathered during interviews, allowing for more structured analysis. Debriefing consists of two steps: "DUMP" and "SORT." In the "DUMP" step, all the information collected is written down, while in the "SORT" step, the information is organized and transformed into usable insights.

ML Model Explainability with Semantic Web Technologies:

While ML techniques like Neural Networks have proven to be powerful in predictive tasks, they often lack explainability. In domains where explainability is crucial, such as healthcare or transportation, Semantic Web Technologies offer semantically interpretable tools. These tools allow for reasoning on knowledge bases and provide explanations for ML model outcomes.

Combining Semantic Web Technologies and ML:

To enhance model explainability, researchers have proposed combining Semantic Web Technologies with ML. This combination focuses on two types of ML models: supervised classification tasks using Neural Networks and unsupervised embedding tasks. Neural Networks are the dominant prediction model, but taxonomical information from knowledge bases is also utilized. Importantly, the use of Semantic Web Technologies does not sacrifice performance; these systems often achieve state-of-the-art results.

Applying ML and Semantic Web in Healthcare and Recommendation Systems:

The healthcare industry has seen numerous proposals for interpretable ML models that use taxonomical knowledge to aid performance and interpretability. High stakes in healthcare and the availability of medical ontologies drive the development of such systems. Recommendation systems, on the other hand, commonly combine embedding models with knowledge graphs. However, most systems offer static explanations without much user interaction, highlighting the need for adaptive and interactive explanations.

Challenges and Future Research:

The field of ML model explainability with Semantic Web Technologies faces challenges such as knowledge matching, data interconnectedness, and increased system complexity. Future research should focus on developing automated and reliable methods for knowledge matching and addressing the potential lack of data interconnectedness. Additionally, efforts should be made to create truly explainable systems that incorporate reasoning and external knowledge in a human-understandable manner.


In conclusion, user research and model explainability are essential components of successful Lean Startup and ML initiatives. By following best practices for user interviews and leveraging Semantic Web Technologies for ML model explanations, organizations can drive innovation while maintaining a customer-centric approach. Three actionable pieces of advice to keep in mind are: prioritize qualitative user research over quantitative surveys, actively listen and take notes during user interviews, and strive for truly explainable ML models by incorporating reasoning and external knowledge. By adopting these practices, organizations can unlock the full potential of Lean Startup and ML for sustainable innovation.

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