Integrating the Scientific Method into Product Development: An Overview of the Wonder-Learn Loop
Explore the Wonder-Learn Loop, blending the scientific method with product development to enhance design, implementation, and marketing through data-driven insights.
Ciao,
Last week, I could not find time to write: my work always involves an incessant sequence of meetings that leaves me exhausted at the end of the day. Apparently, despite copious literature on how to avoid meetings, they remain something of a necessary evil. If you have any suggestions, best practices or spells you want to share with me, please leave a comment 😄.
Table of Contents
The Wonder-Learn Loop
Y Combinator’s Request for Startups
Product Manager Vs. Technical Manager - Know the Difference! - Nancy Li
The Most Insane Week of AI News So Far This Year! - Matt Wolfe
The Wonder-Learn Loop
The design and development of a new product involves a process analogous to the scientific method and requires a cognitive cycle, i.e., a recursive path to achieve or consolidate knowledge on a given topic.
The lean startup philosophy fundamentally incorporates the scientific method's principles, even though Eric Ries inaccurately credits lean manufacturing methodologies as its theoretical base in his widely-read book.
Similarly, the influence of the scientific method extends subtly into user-centered design practices, notably within the design thinking approach, and is also integral to growth marketing strategies. Despite the varied contexts in which these methodologies are applied, they share a core methodology: a rigorous process of observation and analysis.
This process involves an iterative loop where hypotheses are formulated based on observed data; experiments are designed and conducted to test these hypotheses; the outcomes are analyzed to glean insights, and new hypotheses are developed based on these insights. This cycle of hypothesis, experimentation, analysis, and refinement is at the heart of driving innovation and improving product design and marketing strategies, ensuring that decisions are data-driven and aligned with user needs and market demands.
A few years ago, while preparing a seminar for PiSchool, I created a unified view by introducing the concept of the Wonder-Learn Loop.
This concept was designed to map out the stages of design thinking, lean startup, and growth marketing. The objective was to demonstrate how the principles of the scientific method have increasingly infiltrated every aspect of the creation, design, implementation, and marketing of new products. By aligning these methodologies under the Wonder-Learn Loop, I sought to highlight the iterative process of curiosity-driven exploration (wonder) followed by systematic learning.
The Wonder-Learn Loop encapsulates a four-step process:
Wonder: This is the initial stage, where everything begins with observation and/or the formulation of a hypothesis. This phase is crucial as it sets the direction for the development of experiments, aiming to either validate or refute the initial hypothesis.
Experiment: At this stage, the focus shifts to designing the methodology for the survey and developing the necessary experiments to collect data. The design of these experiments is critical to ensure that the data gathered will be relevant and valuable for analysis.
Customer Feedback: For the experiments to be meaningful, they must facilitate the collection of data on customer behaviors. This step is pivotal because it provides direct insight into how customers interact with the product, which is instrumental for the subsequent analysis.
Learn: The final step involves reflecting on the data collected to draw conclusions. This stage is where the analysis happens, leading to actionable insights that can inform future iterations of the product.
Mapping the Wonder-Learn Loop to the methodologies of design thinking, lean startup, and growth hacking, we can see how each step correlates with critical phases of these approaches.
Design thinking begins with empathy (wonder), moves through ideation and prototyping (experiment), and ends with tests (customer feedback and learning). I wrote an article to explain why design thinking is important for product managers.
Lean Startup starts with a hypothesis that drives the build phase (wonder and experiment) and proceeds to collect data (customer feedback) and learn.
Growth Hacking involves forming a growth hypothesis (wonder), conducting experiments to test growth strategies (experiment), measuring user engagement or conversion (customer feedback), and analyzing the results to inform the next set of actions (learn).
This mapping helps to show how the scientific method’s principles of observation, hypothesis testing, experimentation, and learning are deeply ingrained in the ideation, design, implementation, and marketing of new products.
Y Combinator’s Request for Startups
Y Combinator has rolled out a set of themes they’re interested in. Most of them are related to AI, machine learning, and large language models:
Applying machine learning to robotics
Using machine learning to simulate the physical world
Explainable AI
LLMs for manual back office processes in legacy enterprises
AI to build enterprise software
Small fine-tuned models as an alternative to giant generic ones
The second area of interest is hard tech:
New defense technology
Bring manufacturing back to America
New space companies
Climate tech
Healthcare is a huge problem in the United States and probably one of the sectors that needs to be reinvented from the ground up:
A way to end cancer
Foundation models for biological systems
The managed service organization model for healthcare
Eliminating middleman in healthcare
Finally, software and tools (for enterprises and beyond):
Commercial open-source companies
Spatial computing
New enterprise resource planning software
Developer tools inspired by existing internal tools
Better enterprise glue
Only one theme for Web3:
Stablecoin finance
Product Manager Vs. Technical Manager - Know the Difference! - Dr. Nancy Li
In the YouTube video, Nancy Li discusses the distinctions between technical product managers, technical program managers, and product managers, highlighting four key differences in responsibilities, product types, technical knowledge, and career progression.
Product managers and technical product managers are also referred to as the CEOs of products, overseeing the entire product management lifecycle from concept to market strategy. They collaborate with cross-functional teams to create customer-centric products. In contrast, technical program managers act as the COOs, focusing on managing the development process and ensuring timely and budget-friendly delivery without setting the product vision.
The roles and responsibilities of technical product managers and product managers are similar, with the key difference lying in the types of products they handle. Technical product managers work on highly technical products like Google Cloud AI, requiring a deep understanding of technical aspects and close collaboration with engineering teams. On the other hand, general product managers handle customer-facing products such as Uber or Airbnb, managing end-to-end product development and market strategies.
Nancy stresses the importance of mastering the product management lifecycle skills for all types of product managers. Additionally, technical product managers must supplement these skills with technical knowledge, including system design, software architecture, cloud architecture, large language models, and machine learning basics. While coding skills are not mandatory, the ability to communicate effectively with engineering teams and make strategic technical decisions is crucial.
Nancy also shares insights into salary ranges for different types of product managers, emphasizing the potential for high earnings in tech companies, especially for technical product managers working on cutting-edge technologies like AI.
The Most Insane Week of AI News So Far This Year! - Matt Wolfe
The artificial intelligence (AI) industry continues introducing new amazing models, and it is becoming a challenge to stay informed about and try the latest tools. I try not to miss a video from Matt Wolfe: this week, OpenAI released a preview of Sora and introduced Memory in ChatGPT, and Google updated Gemini 1.5 (to name a few).
0:00 Intro
0:23 Google Gemini 1.5’s Massive Upgrade
4:20 Sora Updates - Amazing AI Video
10:31 AI Text-To-Full Video With Your Voice
13:24 ChatGPT Getting Memory
15:06 Karpathy Leaves OpenAI
16:55 Sam Altman Wants $7 TRILLION?
18:08 Stable Cascade Text-To-Image
20:39 Nvidia Chat With RTX
23:02 V-JEPA From Meta
24:45 Eleven Labs Allows You To Sell Your Voice
25:27 AI Can't File For Patents
25:35 Zuckerburg's Thoughts on Apple Vision Pro
26:36 Announcements - Win An RTX 4080 Super
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Thanks for taking the time to read this episode of my newsletter. I hope I’ve been helpful. If you think my sketchbook might interest someone else, I’d be glad if you shared it on social media and forwarded it to your friends and colleagues.
Nicola