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Bayesian Updating
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Today, we’re starting with Bayesian Updating. It’s an approach where we adjust our confidence in a hypothesis based on new evidence—like initial user test results. Can anyone explain what a prior weight might mean?
Is it like our initial guess about something?
Exactly! Let’s say we think a feature is 70% effective based on our assumptions. If we test it and get new feedback, we revise our belief. What do you think that process would look like?
We probably take the new feedback into account and adjust that 70% accordingly?
Right! This means we can better focus on what works. Remember, knowledge progresses as we gather more data.
So, are we weighing evidence differently as we test more?
Absolutely! More tests lead to more informed decisions. Let’s summarize, Bayesian Updating helps us adjust our beliefs with new data—key to effective decision-making.
Kano Model Mapping
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Now, let’s dive into the Kano Model. It’s great for feature evaluation. The features are categorized into three types: ‘Must-have,’ ‘Performance,’ and ‘Delighters.’ Can anyone give an example of a must-have feature?
I think a login function for an app would be a must-have?
Spot on! What about a feature that makes the app stand out completely?
Maybe a customizable interface could be a Delighter?
Exactly! This approach lets us align our designs with user expectations. By identifying which features are critical, we can ensure we satisfy our users more effectively.
So how do we decide if something is a performance feature?
Good question! Performance features increase satisfaction when executed well. We gauge their importance based on user feedback and market trends. Summarizing, the Kano Model helps us strategically prioritize features based on user desires.
Introduction & Overview
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Quick Overview
Standard
The section delves into key decision-making frameworks such as Bayesian Updating and the Kano Model. It emphasizes the importance of adapting decision-making processes based on initial user data and categorizing features to meet user expectations and delight.
Detailed
Decision-Making Methodologies
This section introduces critical decision-making methodologies essential in the design process. The methodologies covered include Bayesian Updating and Kano Model Mapping. Bayesian Updating focuses on incorporating user test results to refine prior assumptions, enhancing accuracy in decision-making. The Kano Model categorizes features into three groups: ‘Must-have’, ‘Performance’, and ‘Delighter’, providing a structured approach to prioritize features based on user satisfaction. Understanding these methodologies enables designers to make informed choices, balancing user needs and technical feasibility, ultimately contributing to a validated concept.
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Bayesian Updating
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○ Bayesian Updating: Incorporate initial user test results to adjust weight priors.
Detailed Explanation
Bayesian Updating is a statistical method that allows us to make decisions based on new evidence. Imagine you start with an initial belief about a product's usability, known as a prior. After conducting user tests, you receive feedback that can confirm or challenge your initial belief. The process of Bayesian Updating involves taking this new feedback and adjusting your initial belief accordingly. Over time, as you gather more data, your beliefs will continue to evolve and become more refined, leading to better decision-making.
Examples & Analogies
Think of it as a weather forecast. Initially, the weather service might predict a 70% chance of rain based on previous data. However, if new conditions arise—like sudden humidity changes—they update their forecast, incorporating this new information. After each update, the forecast becomes more accurate, much like how Bayesian Updating improves decision quality with new user feedback.
Kano Model Mapping
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○ Kano Model Mapping: Classify features into ‘Must‑have’, ‘Performance’, and ‘Delighter’ categories.
Detailed Explanation
The Kano Model is a useful framework for prioritizing features in a product development process. It basically divides features into three categories: 'Must-have', which are essential for basic user satisfaction; 'Performance', which improve user satisfaction as their quality increases; and 'Delighters', which are unexpected features that can significantly enhance user satisfaction if included. Understanding these categories helps teams focus on delivering what users truly need and want, ultimately improving the user experience.
Examples & Analogies
Consider a smartphone. The 'Must-have' features are basic functionalities like making calls and texting, which users expect. 'Performance' features could include battery life and camera quality—better performance here leads to increased user satisfaction. On the other hand, a 'Delighter' could be a unique feature like a new augmented reality application that users didn't anticipate but find exciting and valuable. By mapping features into these categories, developers can better prioritize their work based on what will have the most significant impact on user satisfaction.
Definitions & Key Concepts
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Key Concepts
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Bayesian Updating: A method to enhance decision-making by adjusting prior beliefs with new information.
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Kano Model: A framework for categorizing features that impacts user satisfaction to prioritize feature development.
Examples & Real-Life Applications
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Examples
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A new software tool that initially scores a 70% effectiveness based on assumptions but is revised to 85% after user testing.
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A mobile app that includes a must-have login, performance search function, and a delightfully customizable interface.
Memory Aids
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🎵 Rhymes Time
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In Bayesian’s flight, updates keep it right, changing beliefs with new insights.
📖 Fascinating Stories
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Imagine a ship captain adjusting their course based on changing weather patterns; this is like a designer updating their feature based on user feedback.
🧠 Other Memory Gems
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For Kano, think M-P-D: Must-have, Performance, Delighter.
🎯 Super Acronyms
Kano
- Keep Analyzing Needs and Opportunities.
Flash Cards
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Glossary of Terms
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Term: Bayesian Updating
Definition:
A statistical method that updates the probability estimate as more evidence or information becomes available.
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Term: Kano Model
Definition:
A model used to categorize features based on how they impact customer satisfaction. It includes 'Must-have,' 'Performance,' and 'Delighter' categories.