Everything’s a Gamble: Validating Your Backlog with Experiments


Validating product ideas through experimentation is a crucial practice in product management. Rather than making assumptions about what users want, product managers must treat everything in the backlog as a hypothesis that needs to be tested.

The lean startup methodology emphasizes the importance of getting out of the building and testing your ideas with real users. As Steve Blank says, „No facts exist inside the building, only opinions.” Rather than developing products based on hunches and internal discussions, we need to verify our assumptions by running experiments that involve target users.

This validation mindset is key because we often think we understand our users, but we can be wrong in our assumptions about their problems, needs, and behaviors. Running quick experiments allows us to collect real data on how users respond to potential solutions. This reduces risk and ensures we build products that effectively serve user needs.

Validating ideas through experimentation is not just about avoiding failure – it helps companies pivot faster and identify winning products sooner. By testing product concepts early and often, we can focus energy on the ideas that have the most potential to delight users and achieve business goals.

Everything in the Backlog is a Hypothesis

Product managers often make the mistake of treating everything in their backlog as facts and certainties, rather than assumptions and hypotheses that need validation. The truth is, every new product idea, every feature on your roadmap represents a hypothesis about what will bring value to users. You believe that building that feature will drive business outcomes like increased engagement, retention or revenue. But until you test that assumption with real users, it remains just that – an unproven hypothesis.

Approaching your backlog with the mindset that „everything is a bet” is immensely powerful. It forces you to question your assumptions and prevents you from wasting time building features users don’t want. The core principle is that you should test your hypotheses early and often through experiments, not build out your roadmap based on hunches. Prototyping and releasing minimum viable versions allows you to validate what resonates with users, so you can double down on what delivers results. With an experimentation mindset, you turn ideas into facts.

Validating Through Experimentation

Every product hypothesis needs validation through experimentation. Rather than guessing what users want, product managers should design and run experiments to test assumptions.

There are several types of experiments that can validate hypotheses:

  • Prototype testing: Create a prototype of a feature or product and get feedback from target users. This can range from low-fidelity sketches to clickable prototypes. Observe how users interact with it and incorporate feedback into the next iteration.
  • Landing page tests: Build a landing page describing the product and drive traffic to it from target customer segments. Measure conversion rates, clickthroughs, signups, etc. to gauge interest.
  • A/B testing: Release variant versions of a product or feature to subsets of users. Analyze the usage data to identify which variant better achieves the desired metric.
  • Email/ad campaigns: Run focused email campaigns or online ads for the product concept and track engagement. Are people clicking through or signing up?
  • Exploratory user research: Interview or survey potential users about the product concept. Gauge their enthusiasm, understand pain points, and clarify the target market.
  • Beta tests: Release an early product version to a limited set of users. Collect feedback, monitor usage metrics, and gain insights to improve the product before a full launch.

The key is to identify your biggest assumptions and focus experimentation efforts on validating those product hypotheses first. Using data to make decisions builds confidence in product direction and improves the chances of success.

Determining Key Metrics

Choosing the right metrics to measure experiments is critical for understanding if a feature or change had the intended impact. Rather than relying on vanity metrics like clicks or downloads, focus on metrics tied to core business or user goals.

For example, if the experiment involves a new sign up flow, measure metrics like sign up conversion rate, drop off at each step, and quality of new users. If testing a new recommendation algorithm, measure metrics like engagement, clicks/orders per user, and revenue per user.

Ideally, have a small set of quantitative metrics that map to overall objectives. Be specific in defining each metric and how it will be calculated prior to running tests. Avoid vanity metrics that seem positive but don’t actually indicate performance. Track metrics over both the short and long-term to account for changes over time.

Set clear hypotheses and target metric thresholds for each experiment. For example, aim to increase conversion rate from landing page by 10% or get 5% more users to regularly engage with new feature. This helps interpret results and identify meaningful vs. statistical changes.

Prioritizing Experiments

When it comes to experimentation, you can’t test everything at once. You’ll need to prioritize which hypotheses to validate first. Focus your experiments on the biggest risks and assumptions in your product roadmap.

For example, if you’re planning a major new feature but aren’t sure how customers will respond, test demand for that feature before fully building it out. Or if you’re redesigning your signup flow, test the new flow against the old one before rolling it out completely.

Prioritize experiments that have the potential to make the biggest impact. Look for assumptions that, if proven wrong, would significantly influence your roadmap and strategy. Test those risky hypotheses first to avoid wasted effort and build confidence as you move forward.

Some key areas to focus experiment prioritization:

  • New features with high dev investment
  • Significant changes to core flows
  • Redesigns of critical pages
  • Major marketing and go-to-market initiatives
  • Pricing changes or new business models

By validating the biggest assumptions early, you can refine your roadmap, focus engineering capacity on proven solutions, and avoid costly false directions. Move fast by testing your biggest risks before you build.

Running Effective Experiments

When running experiments, it’s important to follow best practices to get valid, reliable results. Here are some tips:

  • Have a clear hypothesis. What do you think will happen and why? Spell out your assumptions. This focuses the experiment and helps interpret results.
  • Isolate variables. Change only one factor at a time so you know what caused the effect. If you change multiple things, you won’t know which impacted the outcome.
  • Use A/B testing. Split your audience into two groups – the control gets the current version, the experiment gets the change. This isolates the variable.
  • Choose relevant metrics. Pick metrics that will validate or invalidate your hypothesis. Focus on the key outcomes that matter.
  • Run enough iterations. Test until statistical significance is reached. For web experiments, often hundreds or thousands of users are needed.
  • Randomize users. Assign users randomly to groups to avoid sampling bias. Randomization ensures fairness.
  • Analyze results correctly. Use statistics, not gut feelings. Beware of things like novelty effects wearing off.
  • Learn and improve. No experiment is a complete failure if you learn something. Iteratively improve based on insights gained.

Following structured best practices for setting up and analyzing experiments makes it more likely you’ll get valid results and actionable insights. With the right approach, experiments can inform smart product development.

Analyzing and Learning

Once an experiment is complete, it’s critical to thoroughly analyze the results and extract key learnings. This is the most important part of the process.

Evaluate whether your hypothesis was proven or disproven based on the metrics you defined upfront. Dig into the data and try to understand why users responded the way they did. Look for any surprising or unexpected results.

Some key questions to ask:

  • Did we observe the desired behavior change in our target segment? Why or why not?
  • How did the key metrics we defined compare to our hypothesis?
  • Are there differences we should analyze by segment, cohort, or attribute?
  • What user feedback or qualitative data did we gather from the experiment?
  • What worked well that we should amplify going forward?
  • What didn’t work that we should revise or remove?

The learnings from each experiment build on top of each other, so make sure to document the results thoroughly. Look for patterns and insights that apply more broadly beyond the specific experiment. Track key learnings over time to continuously improve.

Be sure to share results across your team and organization. Experiments are wasted if the lessons don’t lead to changes in strategy, priorities, and execution.

Iterating Quickly

A crucial advantage of validating hypotheses through experiments is the ability to learn and iterate quickly. Each experiment provides an opportunity to gain insights into what resonates with users and what doesn’t. As you run experiments, pay close attention to the results and feedback. Look for patterns and key learnings that can inform future iterations.

Resist the urge to theorize and make assumptions. Instead, let the data guide you. If a hypothesis is invalidated, use that learning to update your thinking. If an experiment shows positive results, double down and expand on what’s working. Small tweaks and adjustments add up over time.

Move fast, leverage learnings, and continually refine based on real user data. The faster you iterate, the quicker you hone in on product solutions users want. Be nimble and flexible, evolving the product as you go. Don’t get stuck on a predetermined path, be open to pivot based on new insights. Iterating quickly allows you to stay aligned with user needs even as they change over time.

The key is to establish a rapid cycle of ideation, experimentation, learning and iteration. By implementing this build-measure-learn loop, you can iterate your way to product-market fit faster than the competition. Speed matters when it comes to innovation, so focus on quick experiments that drive continuous improvement. The faster you iterate, the faster you win.

Avoiding Common Mistakes

Conducting experiments effectively requires avoiding some common pitfalls that can undermine results:

  • Confirmation bias – Looking only for data that confirms your hypothesis, and ignoring contradictory data. Remain objective and acknowledge all results.
  • Small sample sizes – Testing with too few users leads to variability and inaccurate conclusions. Determine minimum sample sizes upfront for statistical significance.
  • Changing multiple variables – Altering more than one thing at once makes it impossible to know which change impacted the metrics. Isolate each variable and test them independently.
  • No control group – Having a baseline to compare against is crucial. Run A/B tests or keep part of your product unchanged as a control.
  • Stopping too soon – Ending an experiment prematurely before collecting enough data can miss long term effects or trends. Run tests long enough to achieve stastical confidence.
  • No actionable metrics – Focusing on vanity metrics that don’t directly measure outcomes. Define quantifiable, meaningful metrics aligned to key goals.
  • Not testing repeatedly – One-off tests in artificial environments provide limited value. Build a culture of continuous experimentation.

Proactively avoiding these missteps will lead to higher quality results from experiments to validate product hypotheses. Failing fast to proven learning is the desired outcome.


Taking an experimental approach to product development is critical for product managers. Rather than assuming that every idea and feature will be successful, product managers should view their backlogs as a series of hypotheses that need validation.

By designing and running experiments, product managers can test key assumptions and gain valuable insights into what resonates with users. This enables more informed product decisions, reducing waste and increasing the chances of shipping something customers truly want.

A validation mindset also encourages rapid iteration. Failures become learning opportunities rather than setbacks, as experiments reveal areas for improvement. Product managers can quickly pivot based on user feedback, optimizing the product experience over time.

In today’s competitive landscape, winning products come from validating ideas early and often. Product managers who embrace experimentation are better equipped to identify and double down on what delivers real value. While experimentation takes work, the payoff is immense. Validated learning leads to customer-informed products that solve real problems and satisfy market needs.

By treating everything as a testable hypothesis, product managers can focus their efforts on creating products users love. And they can avoid wasted time and resources building features no one wants. Experimentation transforms product discovery from guesswork to a scientific, evidence-based process. For any product manager seeking innovation and growth, it is an indispensable approach.

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