Test-Driven Development (TDD) and Behavior-Driven Development (BDD) are popular agile development techniques. However, they don’t measure application usage or provide guidance on gaining feedback from customers. Experiment-Driven Development (EDD) is a scientific, fact-based approach to software development using agile principles.
Aspect
Explanation
Definition
Experiment-Driven Development (EDD) is a software development approach that emphasizes using experiments and data-driven insights to inform the development process. It involves iterative development cycles where hypotheses are formulated, experiments are conducted, and the results are used to guide further development decisions. EDD is rooted in the principles of agility and continuous improvement and is commonly associated with lean and agile software development methodologies.
Key Concepts
– Hypothesis-Driven Development: EDD is based on the concept of forming hypotheses about user behavior, needs, or system performance and using these hypotheses to drive development decisions. – Data-Backed Decisions: The approach relies on collecting and analyzing data from experiments to make informed decisions about what features or changes to pursue. – Iterative Development: EDD embraces iterative cycles where small, controlled experiments are conducted, and the results are used to adapt and refine the product. – User-Centric: EDD prioritizes understanding user needs and preferences through experimentation to deliver solutions that resonate with users. – Feedback Loop: It establishes a feedback loop that continuously informs development, reducing the risk of building features with uncertain value.
Characteristics
– Continuous Experimentation: EDD involves a continuous process of designing, running, and analyzing experiments throughout the development lifecycle. – Empirical Decision-Making: Decisions are grounded in empirical evidence gathered from user feedback and data analysis. – Adaptability: The development process is highly adaptable, allowing teams to pivot quickly based on experiment outcomes. – User-Centered: EDD places a strong focus on aligning development efforts with user needs and expectations. – Rapid Learning: Teams engage in rapid learning by conducting frequent experiments, leading to faster product improvements.
Advantages
– User Satisfaction: EDD leads to products that are better aligned with user expectations, resulting in higher user satisfaction. – Reduced Risk: The approach reduces the risk of building features or changes that may not resonate with users or meet business objectives. – Innovation: EDD fosters innovation by encouraging experimentation and exploration of new ideas. – Efficiency: Teams can avoid investing significant resources in features that do not provide the desired outcomes. – Data-Driven Culture: It promotes a data-driven culture within development teams, fostering a deeper understanding of user behavior.
Drawbacks
– Resource-Intensive: Implementing EDD can require additional resources for designing, conducting, and analyzing experiments. – Complexity: Managing multiple experiments and data sources can introduce complexity into the development process. – Misinterpretation: Incorrect interpretation of experiment results can lead to misguided development decisions. – Time-Consuming: Conducting experiments and analyzing data can extend development timelines. – Skill Requirements: Teams may need training in data analysis and experiment design.
Applications
– Digital Products: EDD is commonly used in the development of digital products, including websites, mobile apps, and software platforms. – E-commerce: E-commerce platforms use EDD to optimize user experiences, product recommendations, and purchase processes. – Online Services: Online services, such as streaming platforms and social networks, employ EDD to enhance user engagement and retention. – Product Features: EDD informs the development of new features or changes to existing features in a wide range of digital products. – Startup Growth: Startups often use EDD to rapidly iterate on their products and identify growth opportunities.
Use Cases
– A/B Testing: A popular use case involves conducting A/B tests to compare two or more versions of a feature or webpage to determine which one performs better with users. – Feature Prioritization: EDD helps prioritize features based on their potential impact, allowing teams to focus on high-value changes. – User Onboarding: Experimentation can optimize user onboarding processes to increase user retention and satisfaction. – Pricing Strategy: EDD can inform pricing decisions by testing different pricing models and strategies with users. – Content Personalization: Media and content platforms use experiments to personalize content recommendations and improve user engagement.
While TDD and BDD help developers enhance code quality and ensure that it behaves according to spec, EDD helps identify the features that should be developed. In other words, what will become the spec.
EDD is driven by split A/B testing, where a baseline (control) sample is compared to several single-variable samples to determine which of the two choices improves response rates.
This form of feedback collection avoids the need to conduct user surveys, which are often time-consuming for both parties and can be prone to bias.
Implementing Experiment-Driven Development
To implement EDD, it is a matter of following these four steps:
Start with a hypothesis
Instead of beginning with a user story, the project team starts by defining a hypothesis related to customers, problems, solutions, value, or growth.
For example, a growth hypothesis may be “A virtual shoe fitting station in every store will increase shoe sales by 30%.”
Identify the experiment
In the second step, take the highest-priority hypothesis and define the smallest experiment that will prove or disprove it.
The shoe store may decide to install a virtual fitting station in five stores to begin with and measure the impact on sales.
Run the experiment
This may include creating a minimum viable product (MVP) and then measuring progress based on validated learning from the end-user.
As pointed out by Eric Ries, a minimum viable product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort through a cycle of build, measure, learn; that is the foundation of the lean startup methodology.
Here, many businesses choose to run experiments based on the Build/Measure/Learn (MVPe) loop.
Marc Andreessen defined Product/market fit as “being in a good market with a product that can satisfy that market.” According to Andreessen, that is a moment when a product or service has its place in the market, thus enabling traction for the company offering that product or service.
Debrief
For example, what are the observations?
How were the validated learnings used? Would more time spent on planning have helped?
Based on the results, the team may choose to pivot to a new hypothesis.
Alternatively, they may choose to persevere with the current hypothesis or discard it entirely and move to the next one.
Experiment-Driven Development Benefits
When a business incorporates EDD to complement an existing approach such as TDD or BDD, it can realize several benefits.
These include:
Structure
EDD allows project teams to ask and answer questions in a structured, measurable process.
Since ideas are validated by hypotheses, teams also avoid the testing of ideas simply to validate individual egos or hunches.
Versatility
Although its scientific foundations may suggest otherwise, Experiment-Driven Development can be used across any business in any industry.
It is not specifically designed for use by R&D teams.
Objectivity and efficiency
All agile methodologies dictate that value to the end-user is the primary goal.
However, the hypothesis-driven approach of EDD forces teams to define value through validated learning and not assumption alone.
Efficiency is also increased by building an MVP instead of focusing on superfluous features that provide little benefit to the end-user.
Challenge: An e-commerce platform wants to improve its product recommendation engine to boost sales and enhance user engagement.
Application of EDD:
Hypothesis: “Personalized product recommendations based on user browsing history will increase the average order value by 20%.”
Identify the Experiment: The platform introduces personalized product recommendations for a subset of users while the rest continue to see the old recommendations. Data on order values is collected for both groups.
Run the Experiment: An MVP of the new recommendation system is implemented for the selected users. The system tracks user interactions and purchase behavior, measuring the impact on the average order value.
Debrief: After a defined period, the data is analyzed. If the experiment group shows a significant increase in the average order value, the hypothesis is validated, and the new recommendation system is rolled out to all users. If not, the platform may pivot to a different hypothesis, such as refining the recommendation algorithm.
Outcome: EDD helps the e-commerce platform make data-driven decisions about feature development. If the hypothesis is validated, it can lead to increased sales and customer satisfaction.
Mobile App Development: User Onboarding Flow
Challenge: A mobile app developer wants to improve the user onboarding experience to reduce drop-off rates during registration.
Application of EDD:
Hypothesis: “Simplifying the user registration process to two steps will reduce the drop-off rate by 30%.”
Identify the Experiment: The developer creates an MVP that streamlines the registration process to two steps. A control group experiences the original registration flow, while another group uses the simplified flow. User drop-off data is collected for both groups.
Run the Experiment: Users in both groups are tracked during the registration process. The developer monitors how many users complete the registration and how many drop off at each step.
Debrief: After the experiment, the developer reviews the data. If the simplified flow shows a 30% or greater reduction in drop-off rates, the hypothesis is validated, and the new onboarding process is implemented. If not, the developer may iterate on the hypothesis or try a different approach.
Outcome: EDD enables the mobile app developer to make informed decisions about user onboarding. If successful, the simplified onboarding flow can lead to increased user retention.
SaaS Platform: Feature Adoption
Challenge: A SaaS platform wants to improve the adoption of a new feature among its existing customers.
Application of EDD:
Hypothesis: “Introducing a step-by-step tutorial for the new feature will increase its adoption rate by 25% among existing customers.”
Identify the Experiment: The platform introduces an interactive tutorial for the new feature. Half of the existing customers are exposed to the tutorial when they log in, while the other half does not see it. User interaction and feature adoption data are collected.
Run the Experiment: Users’ interactions with the tutorial and their subsequent adoption of the feature are tracked. The platform measures how many users from each group actively use the new feature.
Debrief: After the experiment, the platform analyzes the data. If the group exposed to the tutorial shows a 25% or higher increase in feature adoption, the hypothesis is validated, and the tutorial is implemented for all existing customers. If not, the platform may refine the tutorial or explore alternative strategies.
Outcome: EDD helps the SaaS platform make evidence-based decisions to drive feature adoption among its customer base.
Key takeaways
Experiment-Driven Development is a hypothesis-driven approach to software development that is based on fact.
Experiment-Driven Development incorporates A/B testing, where a baseline sample is compared to a single-variable sample to determine which sample delivers a better outcome. This allows the business to formulate, test, and evaluate hypotheses.
Experiment-Driven Development complements approaches such as TDD and BDD, but it does not replace them. EDD can be used in any industry or department as an efficient and (most importantly) objective means of agile software development.
Key Highlights
Understanding Experiment-Driven Development (EDD): EDD is an agile development approach rooted in scientific methods. While TDD and BDD focus on code quality and behavior, EDD helps identify features by testing hypotheses with A/B split testing.
EDD Process in Four Steps:
Hypothesis: Start with a hypothesis related to customers, problems, solutions, value, or growth.
Identify Experiment: Define a small experiment to prove or disprove the hypothesis. For instance, testing a virtual shoe fitting station’s impact on sales.
Run Experiment: Create an MVP, use validated learning from end-users, and apply the Build/Measure/Learn loop.
Debrief: Analyze observations, learnings, and results. Decide to pivot, persevere, or move to a new hypothesis.
Benefits of EDD:
Structure: EDD provides a structured process for asking and answering questions based on validated hypotheses.
Versatility: EDD is adaptable across various industries and departments, not just R&D.
Objectivity and Efficiency: EDD ensures value through validated learning, avoids assumptions, and prioritizes efficient MVPs over unnecessary features.
Key Takeaways:
EDD is a scientific approach to software development.
It uses A/B testing for hypothesis validation.
EDD complements TDD and BDD, enhancing agility and objectivity.
EDD is versatile and applicable to various industries and departments.
Related Frameworks, Models, or Concepts
Description
When to Apply
Experiment-Driven Development (EDD)
– Experiment-Driven Development (EDD) is an approach to software development that emphasizes conducting controlled experiments to validate hypotheses, inform decision-making, and drive product improvements. – It involves defining clear hypotheses, designing experiments to test them, collecting and analyzing data, and iterating based on findings. – EDD aims to reduce uncertainty, mitigate risks, and optimize outcomes by making data-driven decisions throughout the development lifecycle.
– When seeking to validate assumptions, prioritize features, and optimize product outcomes. – To reduce uncertainty and mitigate risks associated with product development. – To foster a culture of experimentation, learning, and continuous improvement within development teams.
Lean Startup
– The Lean Startup methodology applies principles of Lean manufacturing to the process of starting and scaling a business. – It emphasizes building a minimum viable product (MVP), testing assumptions with real users through validated learning, and iterating based on feedback. – Lean Startup aims to minimize waste, accelerate learning, and maximize the chances of building a successful and sustainable business.
– When developing new products or launching new ventures with uncertain market demand. – To validate business ideas, test hypotheses, and iterate based on customer feedback. – To minimize investment and time-to-market by focusing on what matters most to customers.
A/B Testing
– A/B Testing, also known as split testing, is a controlled experiment where two or more variants (A and B) of a webpage, feature, or campaign are compared to determine which performs better. – It involves randomly assigning users to different variants and measuring key metrics such as conversion rate, engagement, or revenue. – A/B Testing helps optimize user experience, increase conversions, and inform product decisions based on empirical evidence.
– When seeking to optimize user experience, conversion rates, or other key metrics. – To compare different design variations, messaging, or features objectively. – To make data-driven decisions and iterate based on user behavior and preferences.
Design Thinking
– Design Thinking is a human-centered approach to innovation that emphasizes empathy, ideation, prototyping, and testing to solve complex problems and generate innovative solutions. – It involves understanding user needs, exploring possibilities through brainstorming and prototyping, and iterating based on feedback to arrive at viable solutions. – Design Thinking encourages collaboration, creativity, and iteration to address user challenges effectively.
– When developing new products, features, or services with a focus on user needs and preferences. – To uncover insights, generate ideas, and prototype solutions rapidly. – To iterate and refine designs based on user feedback and validation.
Lean UX
– Lean UX applies Lean principles to the practice of user experience design, emphasizing rapid experimentation, collaboration, and feedback to deliver value to users efficiently. – It focuses on minimizing waste, validating assumptions, and iterating on designs through continuous user research and testing. – Lean UX encourages cross-functional teams to work collaboratively, iterate quickly, and incorporate user feedback into the design process.
– When designing user experiences for digital products or services in a fast-paced, iterative environment. – To validate assumptions, test designs, and gather feedback from users early and often. – To streamline the UX design process and deliver value to users more efficiently.
Hypothesis-Driven Development
– Hypothesis-Driven Development is an approach to product development that starts with formulating hypotheses about user needs, behaviors, or problems and then designing experiments to test them. – It involves identifying key assumptions, defining success criteria, and validating hypotheses through data-driven experiments. – Hypothesis-Driven Development helps focus efforts on what matters most to users, minimize waste, and accelerate learning and iteration.
– When developing new features, products, or services with uncertain outcomes. – To validate assumptions, mitigate risks, and optimize product-market fit through experimentation. – To foster a culture of curiosity, learning, and evidence-based decision-making within development teams.
Data-Driven Product Development
– Data-Driven Product Development is an approach that uses quantitative and qualitative data to inform product decisions, prioritize features, and optimize user experience. – It involves collecting and analyzing data from various sources, such as user feedback, usage metrics, and market trends, to identify opportunities and challenges. – Data-Driven Product Development enables teams to make informed decisions, measure the impact of changes, and iterate based on empirical evidence.
– When seeking to understand user behavior, preferences, and pain points. – To prioritize features, optimize user experience, and drive product improvements based on data insights. – To measure and track key metrics to assess the effectiveness of product changes and initiatives.
Rapid Prototyping
– Rapid Prototyping is a technique for quickly creating low-fidelity or high-fidelity prototypes of digital products or services to gather feedback and validate design ideas. – It involves using tools such as wireframes, mockups, or interactive prototypes to simulate user interactions and test usability. – Rapid Prototyping helps iterate on designs, gather stakeholder feedback, and validate assumptions before investing in full-scale development.
– When designing user interfaces, interactions, or workflows for digital products or services. – To gather early feedback from users and stakeholders, validate design assumptions, and iterate rapidly. – To minimize rework and ensure alignment between design concepts and user needs before development begins.
Customer Development
– Customer Development is a methodology for validating product-market fit and building successful businesses by deeply understanding customer needs and pain points. – It involves engaging with potential customers through interviews, surveys, and observations to gather insights, test hypotheses, and refine value propositions. – Customer Development complements product development by informing feature prioritization, market positioning, and go-to-market strategy.
– When launching new products or ventures with uncertain market demand. – To validate business ideas, understand customer needs, and refine value propositions through direct engagement with target customers. – To mitigate risks, iterate on product concepts, and optimize product-market fit before scaling operations.
Agile Experimentation
– Agile Experimentation integrates principles of Agile software development with experimentation techniques to validate assumptions, iterate on designs, and deliver value to users incrementally. – It involves defining hypotheses, designing experiments, implementing changes, and measuring results within short iterations or sprints. – Agile Experimentation enables teams to learn quickly, adapt to changing requirements, and deliver customer value iteratively.
– When developing digital products or features in an Agile software development environment. – To validate assumptions, prioritize features, and optimize user experience through iterative experimentation. – To align development efforts with user needs, business goals, and market dynamics in a rapidly evolving environment.
What are the steps to implement experiment-driven development?
The steps to implement experiment-driven development are:
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Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
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