In today’s market, product-based companies are in great need of strong and experienced product managers. Unfortunately, there is a need for more quality education in this area, causing many professionals to attempt the role and make mistakes. As a result, specialists often rely on their intuition and assumptions rather than real data due to the lack of reliable tools for building and developing products. That is why we want to tell you about the hypotheses underlying product development.
What is a product hypothesis?
Can you explain a product hypothesis and how it differs from an idea? A product hypothesis is a statement that proposes a connection between two or more variables and is crucially testable. When creating a product, we generate hypotheses to validate assumptions about customer behavior, market needs, or the potential impact of product changes. These hypotheses aid us in identifying product-market fit and enhancing the user experience, and also:
- Decrease potential risks and uncertainties
- Streamline decision-making by reducing biases and guesswork
- Emphasize the principle of continuous learning and development, which is highly valued
Learning to construct and test hypotheses is crucial if you value data-driven development. Hypothesis testing is a primary method for collecting data and enables unbiased decision-making about product development by the product team.
Examples of Hypotheses
Crafting hypotheses becomes intuitive once you discern them from mere opinions or ideas. Their distinction lies in their testability and clarity about expected outcomes.
For instance, consider the statement, “We should optimize our Jira app’s dashboard loading time.” This is merely an idea because it has one variable (optimizing loading time) and lacks clarity on the expected outcome. However, with a slight tweak, it becomes a testable hypothesis.
“By reducing the dashboard loading time of our Jira app by 5% (variable 1), user engagement will increase by 15% (variable 2).” Now, if, upon implementation, user engagement rises by 15%, the hypothesis is validated. If not, it’s disproven.
Here are some more hypotheses tailored to a Jira app development scenario:
- “Introducing a ‘project status’ widget to our Jira cloud app will lead to a 10% increase in monthly active users.”
- “By providing in-app video tutorials, we’ll see a 20% uptick in premium feature subscriptions.”
- “Releasing a developer interview about our latest Jira integration on our blog will drive an additional 1000 visits, of which 50 will result in app installations.”
It’s essential to remember that every hypothesis doesn’t necessarily warrant testing. Sometimes, the mere act of formulating hypotheses can sharpen your analytical skills. It’s vital to weigh the benefits of testing a hypothesis against the resources it would consume.
For instance, the developer interview hypothesis might not be worth pursuing if the anticipated 50 app installations don’t cover the time and resources spent on the interview.
Utilize hypotheses to prioritize development tasks based on the following:
- Quality of impact.
- Magnitude of impact.
- Likelihood of achieving the impact.
By doing so, your team can focus on actions that promise the highest return rather than getting swayed by the novelty or popularity of an idea.
When to Create Hypotheses
Hypotheses form the bedrock of data-driven decision-making, especially in software development and improvement. For product developers, understanding when to create hypotheses can be pivotal in ensuring product success and user satisfaction. Here are the ideal moments:
- Product Ideation & Features Addition: When brainstorming new features or contemplating adding functionalities, hypotheses can help prioritize which features will likely have the most significant positive impact on user experience or drive desired KPIs.
- User Feedback Analysis: If users consistently raise certain issues or request specific enhancements, it’s time to form hypotheses about potential solutions and their impacts. For instance, “If we introduce a drag-and-drop task manager in our Jira app, will user task completion rates improve?”
- Performance Optimization: Whenever there’s a perceived lag or glitch in your product, hypothesize the potential fixes and their effects on user engagement or retention.
- Expansion into New Markets: If you’re considering offering your product in a new geographical region or to a different user segment, hypotheses can help predict user behavior and adoption rates in those markets.
- Marketing and Outreach: Formulate hypotheses about their expected outcomes before launching marketing campaigns or partnership initiatives. For instance, “Partnering with Atlassian Marketplace influencers will lead to a 20% increase in our Jira app downloads.”
- Post-Release Analysis: Monitor user behavior and feedback after launching a new version or feature. If things aren’t proceeding as expected, it’s time to hypothesize why and what can be done to course-correct.
- Resource Allocation: When you have limited resources—be it time, human resources, or budget—and need to decide where to invest, hypotheses can guide decisions based on anticipated ROI.
- UX/UI Redesign: If considering a major design overhaul, hypotheses about user navigation patterns, engagement hotspots, and potential friction points can be invaluable.
Remember, hypotheses aren’t just about anticipating the results of changes. They are also a tool for proactive problem-solving, guiding research, and ensuring that the development team remains aligned with user needs and company goals. So, every time you’re at a decision crossroads or when intuition alone doesn’t seem sufficient, lean on the power of hypotheses.
Creating Hypotheses for the Products
Hypotheses have a standard structure, requiring at least two variables and a connecting factor.
Step 1: Define Variables
Identify independent (cause) and dependent (effect) variables. For instance, introducing a “feature of adding email templates” is an independent variable, while expecting an “increase in the API use for sending emails” is a dependent one.
Independent variables might be product updates, such as revising landing page text or adding filters on a search panel. Dependent variables are typically measurable metrics: trials, subscriptions, monthly active users, etc.
Avoid ambiguous terms in your hypotheses. Instead of saying, “Users churn because setting up is hard,” be specific: “Providing clear setup steps will reduce user churn.”
Remember, when product managers prioritize user needs and value, it simplifies sales and monetization. Formulating hypotheses focusing on enhancing a product’s feature usage is beneficial. When users value a product, improving user experience becomes the primary focus over boosting profits.
Step 2: Linking Variables
The relationship between variables should be clear and logical. If it’s not, regardless of how well-articulated your variables sound, your test results will not be reliable.
Here are some common pitfalls to avoid when defining relationships between two or more variables:
Weak Relationship: It may seem logical that increased website traffic will result in more registrations, but this is not necessarily true. Website visitors may need to be sufficiently motivated to use your product, and registrations typically require greater commitment. A more effective hypothesis would be to focus on modifying your pricing page’s call-to-action (CTA), which will likely have a more direct and impactful relationship with increasing registrations.
Made-up Relationship: It is common to encounter issues when one of the variables depends on a metric that is not indicative. For example, the assumption that “Increasing social media views will enhance our Jira app users” may be erroneous. There is no clear reason a social media user would be inclined towards your product. They could be more attracted to your content than your actual product.
Interdependent Variables: It’s important to keep variables separate from one another. For example, if you remove the “Sign up with Google” option, you’ll likely see fewer users with Google Workspace accounts because the two are directly connected. This is especially important in product development, where accurately defining these relationships is crucial. By ensuring your hypotheses are based on strong connections between variables, you’ll be able to make informed decisions and achieve desired outcomes.
Step 3. Determining Verification Criteria for Hypotheses
Determining the verification criteria is pivotal in validating hypotheses, especially in Jira app development. This step establishes the specific metrics or outcomes you’ll use to evaluate if a hypothesis holds true.
Steps to Determine Verification Criteria
- Define Measurable Outcomes: Ensure your outcomes are tangible and can be tracked. Avoid ambiguous criteria like “improve user satisfaction.” Instead, opt for “Increase the average session duration by 2 minutes.”
- Set Benchmarks: Before testing your hypothesis, understand your current metrics. For instance, if you currently have a 5% click-through rate (CTR) on a particular product feature, and your hypothesis expects to increase it, you need this baseline for comparison.
- Specify Time Frame: All hypotheses should be tested within a specific period. “We expect a 10% increase in new Jira app installations in the next 30 days” is a clear timeframe.
Examples in Jira App Development:
📌 Hypothesis: “By simplifying the onboarding process in our app, we will increase user activation by 15%.”
- Verification Criteria: Track the number of users who complete the onboarding process and compare the rate before and after the changes over a 60-day period.
📌 Hypothesis: “Introducing a dark mode in our Jira app will reduce the app uninstall rate by 5%.”
- Verification Criteria: Monitor and compare uninstall rates for a month before and after introducing the dark mode feature.
📌 Hypothesis: “Highlighting our app’s integration features on the Jira marketplace will boost our demo requests by 20%.”
- Verification Criteria: Measure the number of demo requests received in the 30 days following the changes against the prior 30 days.
Remember, the clearer your verification criteria, the more actionable insights you’ll gather.
Prioritizing Hypotheses in Product Development
Prioritizing hypotheses is crucial in ensuring your development efforts yield the most significant returns. The sheer volume of ideas and potential improvements can be overwhelming when developing apps for Jira. By ranking these hypotheses effectively, you can allocate resources more efficiently and achieve your goals faster. Here’s how you can prioritize your hypotheses:
1. Score Each Hypothesis:
Once you have verification criteria, assign a score (e.g., on a scale of 1 to 10) for each hypothesis based on these criteria. The higher the score, the more priority the hypothesis should get.
2. Rank and Prioritize:
With scores in hand, rank the hypotheses. Those with the highest aggregate scores across all criteria should be at the top of your list.
3. Review Regularly:
The product development landscape is dynamic. As user feedback comes in or business goals shift, revisit and re-prioritize your hypotheses accordingly.
For example:
📌 Hypothesis: “Adding a dark mode to our Jira app will increase nighttime usage by 20%.”
- Potential Impact: 8 (Many users have requested this feature.)
- Feasibility: 6 (Requires some redesign but manageable.)
- Resource Requirement: 5 (Needs designer and developer time.)
- Risk: 2 (Few users might not like the new design.)
- Total Score: 21
📌 Hypothesis: “Integrating a voice-command feature will boost productivity by 30%.”
- Potential Impact: 9 (Could be a game-changer for hands-free task management.)
- Feasibility: 3 (Voice technology is still nascent and might have bugs.)
- Resource Requirement: 7 (Needs significant investment in new tech and training.)
- Risk: 5 (Could frustrate users if not implemented perfectly.)
- Total Score: 24
📌 Hypothesis: “Improving the onboarding tutorial will reduce drop-offs by 15%.“
- Potential Impact: 7 (Better onboarding can retain more users.)
- Feasibility: 8 (We have clear feedback on improvements.)
- Resource Requirement: 4 (Requires updating existing content.)
- Risk: 1 (Low risk; it’s just improving existing content.)
- Total Score: 20
In this example, despite its challenges, the voice-command feature is the top priority due to its game-changing potential. However, teams might tackle the onboarding tutorial first, as it’s more feasible and has fewer risks. The key is to balance impact and feasibility while always keeping the user’s best interests at heart.
Hypothesis Testing Process
Hypothesis testing can be an intriguing process, especially when determining the best methodology for each test. For example, the Jira software environment, known for tracking and managing software development projects, presents unique opportunities for hypothesis testing, particularly in app development. Let’s delve into some methods and creative examples:
1. A/B Testing A/B testing involves creating two or more versions of a webpage, feature, or functionality and collecting data on user reactions.
Example: Suppose you’re developing a Jira app with a built-in search feature on the dashboard. You hypothesize that positioning the search bar at the top right will increase user interaction compared to having it at the bottom left. You’d launch an A/B test to verify, exposing users to both designs. By tracking which group engages more with the search function, you can determine the optimal placement.
2. Prototyping Creating a prototype is a cost-effective way to gather feedback. It’s flexible enough to prototype the entire product or a specific feature.
Example: Consider introducing a new visualization tool in your Jira app. Instead of directly coding it, you can draft its design in tools like Figma.
3. User Interviews (CustDev) Engaging in one-on-one interviews reveals underlying motivations, pain points, and desires. While more effort-intensive, the insights gained can be profound.
Example: Picture a scenario where you’ve launched a Jira app extension aimed at project managers. By organizing 30-minute to 1-hour face-to-face or online interviews with real project managers, you can uncover nuances of their challenges, preferences, and desires. Feedback from these interactions could reshape the app’s roadmap, ensuring it becomes indispensable in a project manager’s toolkit.
To conclude, product hypothesis testing can benefit immensely from a blend of quantitative (like A/B testing) and qualitative (like interviews) methods. It’s about making changes and ensuring they resonate with the users. And in the agile world of Jira, such adaptability is crucial.
Documenting Hypotheses & Results
Every developer needs a systematic approach to hypothesis testing. In product development, consistency is key. Here’s how to streamline the process:
- Centralized Recording: Utilize tools like Coda or Google Sheets to document your hypotheses, plans, and results. This creates a single reference point for your development team and stakeholders, ensuring everyone is aligned.
- Reviewing & Decision-making: After testing, delve into the data. Examine key metrics to decide the next steps. Depending on the clarity, you may need further tests or can proceed with the proposed changes.
- Best Practices for Testing in Jira Apps Development:
- Precision: Ensure data accuracy. Whether you’re analyzing user behavior or gathering feedback, precision matters.
- Experiment Volume: Don’t be afraid to run multiple tests, but be wary of confirmation bias. Accept the data and adapt accordingly.
- Target Audience: Define who you’re testing for. Specificity ensures more reliable results.
- Avoid Bias: Stay neutral. If you’re assessing user interactions, for instance, include comprehensive data sets.
- Learning from Failures: Not all hypotheses will prove correct. However, every test can bring insights, like refining user experience aspects.
- Prioritize Tests: Not everything needs a hypothesis test. If something is evidently off in your Jira app, address it immediately.
Integrating these practices into the product development process ensures a more streamlined and data-driven approach, leading to better products and user experiences.
Conclusion
Crafting and testing hypotheses might seem daunting, but with the right tools, it’s straightforward. The essence lies in posing the right questions, framing actionable statements, and employing efficient testing methodologies.
While intuition-driven development offers speed, hypothesis-based development paves the way for tailored products resonating deeply with customers’ desires.
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