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.
|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.|
Understanding Experiment-Driven Development
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.
Here, many businesses choose to run experiments based on the Build/Measure/Learn (MVPe) loop.
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.
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.
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.
E-Commerce Platform: Optimizing Product Recommendations
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.
- 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.
- 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.
What are the steps to implement experiment-driven development?
What are the benefits of experiment-driven development?
Connected Agile & Lean Frameworks
- Business Models
- Business Strategy
- Business Development
- Distribution Channels
- Marketing Strategy
- Platform Business Models
- Network Effects
Main Case Studies: