Software teams have to move quickly because iterative releases are critical to business growth. But while teams are shipping products faster than ever, their confidence in the success of new features can lag behind.
Too often, features go live without a structured way for teams to measure how well they’re working. The result is a familiar loop: product teams ship, observe a handful of lagging metrics, and hope that upward trends mean success. However, when signals are mixed (or absent), these teams are forced to rely on instinct—which can be problematic.
The problem with guessing
A redesigned homepage might increase conversion, but it also might push some prospective users away. A revised onboarding flow might streamline activation, but it could also introduce a new source of confusion.
These aren’t theoretical risks; they’re common outcomes that can happen silently when there’s no way to detect what’s changed. Without experimentation, teams are left to guess which changes are beneficial, which are detrimental, and why.
In addition to affecting product performance, this uncertainty shapes team dynamics. Disagreements are more difficult to resolve without data, and roadmaps are harder to prioritize effectively. Feedback loops slow down. Confidence can gradually erode because no one can clearly identify what’s working.
The limits of intuition
In the absence of structured feedback, teams fall back on what they know: intuition, experience, and anecdotal evidence. This approach isn’t without value; in fact, successful teams can develop strong instincts over time. But instincts are often shaped by personal context, and often don’t scale well across different users, markets, and product surfaces.
Research shows that even experienced product professionals are wrong more often than they expect. One of the most well-known references to this finding is from a large-scale A/B testing program conducted at Microsoft Bing, where researchers discovered that only about one-third of ideas that experts believed would improve metrics actually did so when tested experimentally.
Despite this limitation, many teams still treat product development as a matter of opinion. A new feature may be prioritized because it “feels right,” or a design may be shipped because it has tested well in a limited user interview. These judgments, while well-intentioned, are not definitive.
The challenge of measuring what matters
Even teams that want to experiment often struggle to do so. Experimentation requires clarity around what’s being tested, how success is defined, and what metrics to observe. It also requires a solid technical foundation, including instrumentation, data infrastructure, and analytical support.
In many organizations, these elements exist—but are fragmented. For example, metrics may live in one system, while releases live in another. Experimentation tools are often disconnected from day-to-day development workflows (if they exist at all). As a result, experimentation becomes difficult to adopt, harder to trust, and easy to deprioritize.
When experiment results do arise, they’re sometimes too late to act on, too technical to interpret, or too shallow to be useful. Teams don’t simply need data; they need the right data, at the right time, in the right format. And, most importantly, they need that data to be trusted across disciplines, including engineering, product, design, and beyond.
The case for experimentation
Experimentation is a decision-making framework. It allows teams to ask clear questions, define measurable outcomes, and evaluate impact with rigor. Done well, it can turn uncertainty into insight. Instead of debating what might work, teams can validate what does. Experimentation helps teams:
- Understand how real users behave in real environments
- Detect unintended consequences early
- Compare multiple ideas without committing prematurely
- Iterate quickly based on observable impact
Perhaps most critically, it helps build trust between individuals, across functions, and among users. Decisions grounded in data are easier to defend and explain, and more likely to lead to meaningful outcomes.
From observation to action
Integrating experimentation into the development lifecycle requires aligning it with the tools and data that teams already use. It means designing experiments that reflect the actual goals of a product rather than generic KPIs.
The value of experimentation lies not only in what it reveals, but also in how it accelerates iteration. Faster feedback leads to faster learning; faster learning leads to better products.
A path to informed development
Integrating experimentation into the way you build products, using the data you already trust, is the best way to stop guessing.
LaunchDarkly can help you make this change by embedding experimentation into feature flags and engineering workflows, and by supporting warehouse-native experimentation powered by the metrics your team already uses. This results in faster iteration and a more resilient product development process.
You don’t need to build a lab to test your ideas; you just need the infrastructure to measure what matters and the tools to act on what you find. Want to see what that looks like in practice?
Request a demo to learn about how LaunchDarkly can help.
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